<![CDATA[ (CANCELED) SCL Course: World-Class Integrated Business Planning (Virtual/Instructor-led)]]> 27233 Course Description

World-Class Integrated Business Planning provides a holistic view toward supply chain performance and supports effective supply chain management. You’ll learn how to handle the challenges of today’s operating environment with “Big Data,” best practices in building a cost-to-serve solution, and analytics that create actionable insights for developing smart strategies for financial improvements. Best practices will be applied in an interactive exercise using the results from the latest innovative software.

Who Should Attend

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1686681533 2023-06-13 18:38:53 1695393254 2023-09-22 14:34:14 0 0 event The course provides a holistic view toward supply chain performance and supports effective supply chain management. You’ll learn how to handle the challenges of today’s operating environment with “Big Data,” best practices in building a cost-to-serve solutions, and analytics that create actionable insights for developing smart strategies for financial improvements. Best practices will be applied in an interactive exercise using the results from the latest innovative software.

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2023-09-28T20:00:00-04:00 2023-09-29T17:00:00-04:00 2023-09-29T17:00:00-04:00 2023-09-29 00:00:00 2023-09-29 21:00:00 2023-09-29 21:00:00 2023-09-28T20:00:00-04:00 2023-09-29T17:00:00-04:00 America/New_York America/New_York datetime 2023-09-28 08:00:00 2023-09-29 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[LeeAnn and Walter Muller Distinguished Lecture Series | Ann Dunkin]]> 36284 Lecture Title: Modeling for Energy Resilience | How DOE uses simulation to model and manage everything from the power grid to the strategic petroleum reserve.   

ABSTRACT: 

Modeling for Energy Resilience | How DOE uses simulation to model and manage everything from the power grid to the strategic petroleum reserve.  

The US Department of Energy’s responsibilities run the gamut from managing the nuclear stockpile and the strategic petroleum reserve to running the power grid in 36 states to performing basic and applied research to protect national security, ensure stable power sector operations and accelerate the clean energy transition.  

Leveraging the power of DOE’s computing infrastructure, including the world’s fastest supercomputer, simulation models are used to accelerate advancements in nearly every field of research across DOE. Through a series of examples highlighting grid management, cybersecurity, cavern modeling and fundamental physical phenomena, this keynote will illuminate how DOE applies modeling and simulation to both research and operations.   

BIO: 

Ann Dunkin currently serves as Chief Information Officer at the U.S. Department of Energy, where she manages the Department’s information technology (IT) portfolio and modernization; oversees the Department’s cybersecurity efforts; leads technology innovation and digital transformation; and enables collaboration across the Department.  

She served in the Obama Administration as CIO of the U.S. Environmental Protection Agency. Prior roles include Chief Strategy and Innovation Officer, Dell Technologies; CIO, County of Santa Clara, CA; CTO, Palo Alto Unified School District, California; and various leadership roles at Hewlett Packard focused on engineering, research and development, IT, manufacturing engineering, software quality, and operations. 

Ann is a published author, most recently of the book Industrial Digital Transformation, and a frequent speaker on topics such as government technology modernization, digital transformation, and organizational development.  

Ann received the 2022 Capital CIO Large Enterprise ORBIE Award, and she has been given a range of previous awards, including DC’s Top 50 Women in Technology for 2015 and 2016, ComputerWorld’s Premier 100 Technology Leaders for 2016, StateScoop’s Top 50 Women in Technology list for 2017, FedScoop’s Golden Gov Executive of the Year in 2016 and 2021, and FedScoop’s Best Bosses in Federal IT 2022.  

Ms. Dunkin holds a Master of Science degree and a Bachelor of Industrial Engineering degree, both from the Georgia Institute of Technology. She is a licensed professional engineer in the states of California and Washington. In 2018, she was inducted into Georgia Tech’s Academy of Distinguished Engineering Alumni. 

]]> chenriquez8 1 1692843142 2023-08-24 02:12:22 1695310628 2023-09-21 15:37:08 0 0 event The ISyE Distinguished Lecture Series was established in 2008 to promote discussion on critical issues in the fields of industrial and systems engineering by bringing in prominent scholars and business leaders who engage and share their expertise with students, faculty, and alumni.

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2023-09-28T15:00:00-04:00 2023-09-28T17:00:00-04:00 2023-09-28T17:00:00-04:00 2023-09-28 19:00:00 2023-09-28 21:00:00 2023-09-28 21:00:00 2023-09-28T15:00:00-04:00 2023-09-28T17:00:00-04:00 America/New_York America/New_York datetime 2023-09-28 03:00:00 2023-09-28 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> 671468 671468 image <![CDATA[Ann Dunkin]]> image/jpeg 1692842658 2023-08-24 02:04:18 1692842792 2023-08-24 02:06:32 <![CDATA[RSVP to Event]]>
<![CDATA[Georgia Statistics Day 2023]]> 27764 Gathering Minds Across Georgia: Promoting Interdisciplinary Statistics Research

You are cordially invited to the 2023 Georgia Statistics Day, a one-day workshop that brings together top researchers across the state to foster collaboration and innovation in statistics, data science, and related disciplines.  

Our workshop provides a unique opportunity for faculty and graduate students from Georgia’s leading institutions to present their latest work, connect with peers, and gain exposure to cutting-edge developments shaping their fields.

With a focus on mentorship and networking, Georgia Statistics Day facilitates idea exchange and partnership building among statisticians, data scientists, and interconnected domain experts in Georgia and the Southeast. Attendees will experience invited talks, panel discussions, and poster sessions that spark new interdisciplinary perspectives and opportunities for growth. 

Don’t miss this chance to contribute to and be inspired by the thriving statistics and data science community thriving in the Peach State! We look forward to welcoming you at the 2023 Georgia Statistics Day on Oct.9 in the Exhibition Center of Georgia Tech.

Registration Ends: Thursday October 5th

 

2023 Georgia Statistics Day is a fully reimbursed event for students and postdocs with confirmed attendance.

]]> Scott Jacobson 1 1694808003 2023-09-15 20:00:03 1695243956 2023-09-20 21:05:56 0 0 event Georgia Statistics Day 2023

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2023-10-09T08:45:00-04:00 2023-10-09T17:00:00-04:00 2023-10-09T17:00:00-04:00 2023-10-09 12:45:00 2023-10-09 21:00:00 2023-10-09 21:00:00 2023-10-09T08:45:00-04:00 2023-10-09T17:00:00-04:00 America/New_York America/New_York datetime 2023-10-09 08:45:00 2023-10-09 05:00:00 America/New_York America/New_York datetime <![CDATA[Georgia Tech Exhibition Hall]]> Monike Welch

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<![CDATA[Event Website]]> <![CDATA[Event Flyer]]>
<![CDATA[Day In the Atrium - Walmart]]> 36284 We can't wait to welcome Walmart on campus next week! Be sure to stop by for Day In The Atrium with Walmart, on September 14 at 11AM. 🛒 🤝 🐝

This is a great time to network and connect with industry partners in the field to learn about upcoming opportunities and resources.

Refreshments will be served. 

]]> chenriquez8 1 1694195003 2023-09-08 17:43:23 1694195470 2023-09-08 17:51:10 0 0 event Be sure to stop by for Day In The Atrium with Walmart, on September 14 at 11AM.

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2023-09-14T11:00:00-04:00 2023-09-14T14:00:00-04:00 2023-09-14T14:00:00-04:00 2023-09-14 15:00:00 2023-09-14 18:00:00 2023-09-14 18:00:00 2023-09-14T11:00:00-04:00 2023-09-14T14:00:00-04:00 America/New_York America/New_York datetime 2023-09-14 11:00:00 2023-09-14 02:00:00 America/New_York America/New_York datetime <![CDATA[]]> Donald Phan, Development Assc-Fundraising

donald.phan@isye.gatech.edu

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671658 671658 image <![CDATA[Day In the Atrium, Walmart]]> image/png 1694195011 2023-09-08 17:43:31 1694195011 2023-09-08 17:43:31 <![CDATA[Walmart Global Tech]]>
<![CDATA[ISyE Picture Day 2023]]> 36284 Photos will be taking place over the course of 3 days in the Cecil G. Johnson ISyE Studio, located on the first floor in ISyE Main, Room 103. All faculty, and staff members are highly encouraged to take new headshots; all Ph.D. students are required. 

If you cannot make your assigned group day, please feel free to come by on any operating date below: 

Recommendations for attire: 

]]> chenriquez8 1 1693917565 2023-09-05 12:39:25 1693917845 2023-09-05 12:44:05 0 0 event If you took new headshots last year, it is highly encouraged to retake them. Please attend the day that corresponds to your profile type.

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2023-09-06T10:00:00-04:00 2023-09-08T15:00:00-04:00 2023-09-08T15:00:00-04:00 2023-09-06 14:00:00 2023-09-08 19:00:00 2023-09-08 19:00:00 2023-09-06T10:00:00-04:00 2023-09-08T15:00:00-04:00 America/New_York America/New_York datetime 2023-09-06 10:00:00 2023-09-08 03:00:00 America/New_York America/New_York datetime <![CDATA[]]> Camille C. Henriquez

chenriquez8@gatech.edu

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671582 671582 image <![CDATA[ISyE Picture Day]]> image/jpeg 1693914370 2023-09-05 11:46:10 1693914370 2023-09-05 11:46:10
<![CDATA[ISyE Seminar Speaker - Nikita Zhivotovskiy]]> 36374 Title:

Optimal PAC Bounds without Uniform Convergence

Abstract:

In statistical learning theory, the problem of determining sample complexity of realizable binary classification for VC classes was a longstanding challenge. Notable advancements by Simon and Hanneke established sharp upper bounds, but their argument’s reliance on the uniform convergence principle curtailed its broader applicability to learning settings like multiclass classification. In this presentation, we will discuss a new technique to resolve this limitation and introduce optimal high probability risk bounds within a framework that surpasses uniform convergence constraints. Beyond binary classification, we will also delve into applications in scenarios where uniform convergence is notably sub-optimal. For multiclass classification, we will prove an optimal risk bound that scales with the one-inclusion hypergraph density of the class, effectively addressing the sub-optimality in the analysis by Daniely and Shalev-Shwartz. Additionally, for realizable bounded regression with absolute loss, we will derive an optimal risk bound based on a revised version of the scale-sensitive dimension, thus refining the results of Bartlett and Long. This talk is based on the joint work with Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, and Abhishek Shetty.

Bio:

Nikita Zhivotovskiy is a tenure-track Assistant Professor at the University of California Berkeley, Department of Statistics. From January 2021 to October 2022 he was a postdoctoral researcher at the department of mathematics ETH, Zürich hosted by Afonso BandeiraBetween January 2019 and December 2020 he was a postdoctoral researcher at Google Research, Zürich hosted by Olivier BousquetBefore that he spent half a year at the department of mathematics, Technion I.I.T. hosted by Shahar MendelsonNikita defended my thesis at Moscow Institute of Physics and Technology Moscow in 2018 under the supervision of Vladimir Spokoiny and Konstantin Vorontsov. During my time in Moscow, he was affiliated (part-time) with the Institute for Information Transmission Problems, Higher School of Economics, and Skoltech. His main interests are in the intersection of mathematical statistics, probability and learning theory.

 

]]> mwelch39 1 1693571163 2023-09-01 12:26:03 1693571163 2023-09-01 12:26:03 0 0 event In statistical learning theory, the problem of determining sample complexity of realizable binary classification for VC classes was a longstanding challenge. Notable advancements by Simon and Hanneke established sharp upper bounds, but their argument’s reliance on the uniform convergence principle curtailed its broader applicability to learning settings like multiclass classification. In this presentation, we will discuss a new technique to resolve this limitation and introduce optimal high probability risk bounds within a framework that surpasses uniform convergence constraints. Beyond binary classification, we will also delve into applications in scenarios where uniform convergence is notably sub-optimal. For multiclass classification, we will prove an optimal risk bound that scales with the one-inclusion hypergraph density of the class, effectively addressing the sub-optimality in the analysis by Daniely and Shalev-Shwartz. Additionally, for realizable bounded regression with absolute loss, we will derive an optimal risk bound based on a revised version of the scale-sensitive dimension, thus refining the results of Bartlett and Long. This talk is based on the joint work with Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, and Abhishek Shetty.

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2023-09-15T11:30:00-04:00 2023-09-15T12:30:00-04:00 2023-09-15T12:30:00-04:00 2023-09-15 15:30:00 2023-09-15 16:30:00 2023-09-15 16:30:00 2023-09-15T11:30:00-04:00 2023-09-15T12:30:00-04:00 America/New_York America/New_York datetime 2023-09-15 11:30:00 2023-09-15 12:30:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ Undergraduate / Graduate First-Time TA orientation]]> 27764 All first-time Teaching Assistants (TAs) are required to participate in an orientation program.

Register On-Campus Link

Register Off-Campus Link

]]> Scott Jacobson 1 1693331983 2023-08-29 17:59:43 1693336466 2023-08-29 19:14:26 0 0 event  Undergraduate / Graduate First-Time TA orientation

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2023-09-05T11:00:00-04:00 2023-09-05T12:00:00-04:00 2023-09-05T12:00:00-04:00 2023-09-05 15:00:00 2023-09-05 16:00:00 2023-09-05 16:00:00 2023-09-05T11:00:00-04:00 2023-09-05T12:00:00-04:00 America/New_York America/New_York datetime 2023-09-05 11:00:00 2023-09-05 12:00:00 America/New_York America/New_York datetime <![CDATA[]]> case@isye.gatech.edu

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<![CDATA[Register On-Campus Link]]> <![CDATA[Register Off-Campus Link]]>
<![CDATA[Undergraduate / Graduate First-Time TA orientation]]> 27764 All first-time Teaching Assistants (TAs) are required to participate in an orientation program.

Register On-Campus Link

Register Off-Campus Link

]]> Scott Jacobson 1 1693336254 2023-08-29 19:10:54 1693336375 2023-08-29 19:12:55 0 0 event Undergraduate / Graduate First-Time TA orientation

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2023-09-07T11:00:00-04:00 2023-09-07T12:00:00-04:00 2023-09-07T12:00:00-04:00 2023-09-07 15:00:00 2023-09-07 16:00:00 2023-09-07 16:00:00 2023-09-07T11:00:00-04:00 2023-09-07T12:00:00-04:00 America/New_York America/New_York datetime 2023-09-07 11:00:00 2023-09-07 12:00:00 America/New_York America/New_York datetime <![CDATA[]]> case@isye.gatech.edu

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<![CDATA[Register On-Campus Link]]> <![CDATA[Register Off-Campus Link]]>
<![CDATA[SCL Course: Machine Learning Applications for Supply Chain Planning (Virtual/Instructor-led)]]> 27233 Course Description

This course is the third in the four-course Supply Chain Analytics Professional certificate program. It introduces the field of machine learning, an area where algorithms learn patterns from data to support proactive decision making, as it applies to supply chain management. You’ll use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You’ll use Python and PowerBI to create and analyze regression, clustering, and classification models.

The course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course. An optional pre-course webinar is typically held the Thursday before the course start date (July 6).

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1661444268 2022-08-25 16:17:48 1693327394 2023-08-29 16:43:14 0 0 event An introduction to the field of machine learning as it applies to supply chain management. You’ll then use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance.

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2023-10-16T13:00:00-04:00 2023-10-19T17:00:00-04:00 2023-10-19T17:00:00-04:00 2023-10-16 17:00:00 2023-10-19 21:00:00 2023-10-19 21:00:00 2023-10-16T13:00:00-04:00 2023-10-19T17:00:00-04:00 America/New_York America/New_York datetime 2023-10-16 01:00:00 2023-10-19 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Essentials of Negotiations and Stakeholder Influence (Virtual/Instructor-led)]]> 27233 Course Description

Essentials of Negotiations and Stakeholder Influence level-sets the participants' understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style “DNA” to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a “no judgement zone” environment.

Who Should Attend

This course is ideal for sourcing initiative leaders, project leaders, business unit leaders, operations managers, sales leaders and procurement & supply management-related professionals who are involved with supplier selection, contract development and supplier performance management.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1692281405 2023-08-17 14:10:05 1693326752 2023-08-29 16:32:32 0 0 event This course level-sets the participants' understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style “DNA” to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a “no judgement zone” environment.

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2024-03-07T14:00:00-05:00 2024-03-14T17:00:00-04:00 2024-03-14T17:00:00-04:00 2024-03-07 19:00:00 2024-03-14 21:00:00 2024-03-14 21:00:00 2024-03-07T14:00:00-05:00 2024-03-14T17:00:00-04:00 America/New_York America/New_York datetime 2024-03-07 02:00:00 2024-03-14 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Category Management and Sourcing Leadership (Virtual/Instructor-led)]]> 27233 Course Description

Category Management and Sourcing Leadership is designed to deepen participants' knowledge base of core activities in the procurement & supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation & selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This "hands on" delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.

Who Should Attend

This course is ideal for sourcing initiative leaders, procurement professionals, project managers, finance analyst, contract managers and all procurement & supply management-related professionals involved with bid package development, bid package analysis, negotiations preparation, contracting and supplier selection activity.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1691524984 2023-08-08 20:03:04 1693326745 2023-08-29 16:32:25 0 0 event This course is designed to deepen participants' knowledge base of core activities in the procurement & supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation & selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This "hands on" delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.

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2024-02-26T14:00:00-05:00 2024-02-29T15:30:00-05:00 2024-02-29T15:30:00-05:00 2024-02-26 19:00:00 2024-02-29 20:30:00 2024-02-29 20:30:00 2024-02-26T14:00:00-05:00 2024-02-29T15:30:00-05:00 America/New_York America/New_York datetime 2024-02-26 02:00:00 2024-02-29 03:30:00 America/New_York America/New_York datetime <![CDATA[]]> EMAIL: info@scl.gatech.edu or CALL: (404) 385-3501 between 9:00a.m. and 4:00p.m., Eastern time.

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Engineering the Warehouse (Virtual/Instructor-led)]]> 27233 COURSE DESCRIPTION

The requirement for high levels of customer service, increasing numbers of SKUs and high labor costs have dramatically increased the complexity of warehouse operations. It is no longer sufficient to manage a warehouse based on a simple, arbitrary “ABC” classification of SKUs, which treats all those in a category as if they were identical. Instead, each decision – such as where to store or where to pick product – must be based on careful engineering and economic analysis. Each SKU must identify its own cheapest, fastest path through the warehouse to the customer and then compete with all the other SKUs for the necessary resources. This results in warehouse operations that are finely tuned to patterns of customer orders and maximally efficient. Learn the concepts necessary to address modern warehouse trade-offs between space and time in optimizing and managing your warehouse.

Essential learning for those who are seeking cost reductions through better handling methods. Also valuable for those who must replace, upgrade, or add material handling equipment. The two-day course will include case examples and a guided exercise to ensure mastery of the techniques presented.

WHO SHOULD ATTEND

Supply chain and logistics consultants, supply chain engineers and analysts, facility engineers, and warehouse supervisors and team leaders

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

WHAT IS COVERED

COURSE MATERIALS

COURSE PREREQUISITES

None.

CERTIFICATE INFORMATION

This course is part of the Distribution Operations Analysis & Design (DOAD) Certificate.

]]> Andy Haleblian 1 1691518540 2023-08-08 18:15:40 1693326690 2023-08-29 16:31:30 0 0 event The requirement for high levels of customer service, increasing numbers of SKUs and high labor costs have dramatically increased the complexity of warehouse operations. It is no longer sufficient to manage a warehouse based on a simple, arbitrary “ABC” classification of SKUs, which treats all those in a category as if they were identical. Instead, each decision – such as where to store or where to pick product – must be based on careful engineering and economic analysis.

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2023-11-07T12:00:00-05:00 2023-11-10T18:00:00-05:00 2023-11-10T18:00:00-05:00 2023-11-07 17:00:00 2023-11-10 23:00:00 2023-11-10 23:00:00 2023-11-07T12:00:00-05:00 2023-11-10T18:00:00-05:00 America/New_York America/New_York datetime 2023-11-07 12:00:00 2023-11-10 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Supply Chain Optimization and Prescriptive Analytics (Virtual/Instructor-led)]]> 27233 Course Description

This course is the fourth in the 4-course Supply Chain Analytics Professional certificate program. It incorporates learning advanced analytics and mathematical optimization to find solutions for supply chain problems. You’ll learn how to use linear programming, mixed integer programming, and heuristics to conduct prescriptive analytics related to production processes, distribution networks, and routing. The course serves as a capstone for the program by culminating in a hackathon where you’ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.

The online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1663249004 2022-09-15 13:36:44 1693326681 2023-08-29 16:31:21 0 0 event Learn advanced analytics and mathematical optimization to find solutions for supply chain problems. The course also serves as a capstone for the Supply Chain Analytics Professional certificate program by culminating in a hackathon where you’ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.

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2023-11-06T14:00:00-05:00 2023-11-09T18:00:00-05:00 2023-11-09T18:00:00-05:00 2023-11-06 19:00:00 2023-11-09 23:00:00 2023-11-09 23:00:00 2023-11-06T14:00:00-05:00 2023-11-09T18:00:00-05:00 America/New_York America/New_York datetime 2023-11-06 02:00:00 2023-11-09 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Principles of Transportation Management (Virtual/Instructor-led)]]> 27233 Course Description

This course prepares students in the basics of transportation operations and analysis. The course includes review of the key elements of transportation such as: modes of transportation, transportation procurement, cost minimization techniques, the role of ports in global logistics, and international trade terms.  The course also will discuss emerging trends in North American transportation markets, emerging techniques, and greenhouse gas emissions reduction.

Who Should Attend

This course is designed for Supply Chain Managers, Distribution Managers, Transportation Planners, Transportation Clerks, Transportation Analysts, and Transportation Managers and learners seeking to enter these roles.  Supply chain professionals from other domains will also benefit through gaining insights into transportation operations.

How You Will Benefit

Upon completion of this course, you will be able to:

What is Covered

]]> Andy Haleblian 1 1666129915 2022-10-18 21:51:55 1693326674 2023-08-29 16:31:14 0 0 event This course prepares students in the basics of transportation operations and analysis.  The course includes review of the key elements of transportation such as: modes of transportation, transportation procurement, cost minimization techniques, the role of ports in global logistics, and international trade terms.  The course also will discuss emerging trends in North American transportation markets, emerging techniques, and greenhouse gas emissions reduction.

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2023-10-24T08:00:00-04:00 2023-10-26T17:00:00-04:00 2023-10-26T17:00:00-04:00 2023-10-24 12:00:00 2023-10-26 21:00:00 2023-10-26 21:00:00 2023-10-24T08:00:00-04:00 2023-10-26T17:00:00-04:00 America/New_York America/New_York datetime 2023-10-24 08:00:00 2023-10-26 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Supply Chain Project Management Vendor Selection & Management (Virtual/Instructor-led)]]> 27233 Course Description

To keep pace with the continuous moves toward outsourcing of operations and the advancement of technology, companies need to focus on selecting the right suppliers and partnerships to provide the most value to their customers and to remain profitable. This course provides a deeper understanding of the Project Management Body of Knowledge (PMBOK) areas of project integration and procurement, as applied to the supply-chain vendor-selection and management process. You will gain the knowledge, skills, and tools to ensure that you are selecting the right supply-chain partners based on your business goals. In addition, you will learn about alternative techniques for supplier selection, including applied quantitative decision-making techniques.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1666129252 2022-10-18 21:40:52 1693326666 2023-08-29 16:31:06 0 0 event This course provides a deeper understanding of the PMBOK knowledge areas of project integration and procurement applied in the supply chain vendor selection and management process. To keep pace with the continuous moves toward outsourcing of operations and the advancement of technology, companies need to focus on selecting the right suppliers and partnerships to provide the most value to their customers and to remain profitable. This course provides the knowledge, skills, and tools to ensure that you are selecting the right supply chain partners (including 3PL’s) based on your business goals. Emphasis is placed on understanding alternative techniques for supplier selection including applied quantitative decision making techniques.

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2023-10-17T08:00:00-04:00 2023-10-19T17:00:00-04:00 2023-10-19T17:00:00-04:00 2023-10-17 12:00:00 2023-10-19 21:00:00 2023-10-19 21:00:00 2023-10-17T08:00:00-04:00 2023-10-19T17:00:00-04:00 America/New_York America/New_York datetime 2023-10-17 08:00:00 2023-10-19 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

]]>
<![CDATA[Course webpage with the SCL website]]>
<![CDATA[SCL Course: Lean Warehousing (Virtual/Instructor-led)]]> 27233 Course Description

This course will demonstrate how warehouse operations are a key enabler to a successful supply chain implementation and the starting point for a transformation. It is critical to manage safety, quality and efficiency. Learn to leverage the lean supply chain modifications to improve customer responsiveness and reduce operating costs and in doing so contributing to a supply chain that creates a competitive advantage for a company. To accomplish this goal, we must bring lean principles into the warehouse and distribution center.

Who Should Attend

Supply chain professionals, logistics professionals, material managers, production control managers, transportation managers, warehousing managers and purchasing managers

How You Will Benefit

Upon completion of this course, you will be able to:

Benefits:

What is Covered

]]> Andy Haleblian 1 1666127245 2022-10-18 21:07:25 1693326660 2023-08-29 16:31:00 0 0 event This course will demonstrate how warehouse operations are a key enabler to a successful supply chain implementation and the starting point for a transformation. It is critical to manage safety, quality and efficiency. Learn to leverage the lean supply chain modifications to improve customer responsiveness and reduce operating costs and in doing so contributing to a supply chain that creates a competitive advantage for a company. To accomplish this goal, we must bring lean principles into the warehouse and distribution center.

]]>
2023-10-10T08:00:00-04:00 2023-10-12T17:00:00-04:00 2023-10-12T17:00:00-04:00 2023-10-10 12:00:00 2023-10-12 21:00:00 2023-10-12 21:00:00 2023-10-10T08:00:00-04:00 2023-10-12T17:00:00-04:00 America/New_York America/New_York datetime 2023-10-10 08:00:00 2023-10-12 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

]]>
<![CDATA[Course webpage within the SCL website]]>
<![CDATA[Professional Education Course: Systems Operations and Strategic Interactions in Supply Chains]]> 27233 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm EDT each day. 

Course Description

Often the lack of cooperation and coordination between organizations or stakeholders lead to inefficiencies, despite having common goals. A systems view is needed to ensure appropriate use of scarce resources to meet the multiple, and often conflicting, short- and long-term goals from multiple constituents. This course will focus on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

About the Course and the HHSCM Course Series

This course is the first in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://hhls.scl.gatech.edu/ by December 31, 2023.  

Questions? Reach out to chhs@gatech.edu!

]]> Andy Haleblian 1 1693310056 2023-08-29 11:54:16 1693310137 2023-08-29 11:55:37 0 0 event This course focuses on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.

]]>
2024-04-22T09:30:00-04:00 2024-04-25T13:00:00-04:00 2024-04-25T13:00:00-04:00 2024-04-22 13:30:00 2024-04-25 17:00:00 2024-04-25 17:00:00 2024-04-22T09:30:00-04:00 2024-04-25T13:00:00-04:00 America/New_York America/New_York datetime 2024-04-22 09:30:00 2024-04-25 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu

]]>
<![CDATA[Course Details via Center for Health and Humanitarian Systems website]]> <![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Health & Humanitarian Supply Chain Management Certificate]]> <![CDATA[Apply for a Scholarship!]]>
<![CDATA[Professional Education Course: Inventory Management and Resource Allocation in Supply Chains]]> 27233 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm EDT each day.

Course Description

Many Supply Chain decisions are concerned with the timely and efficient procurement, allocation, and distribution of resources (e.g. funds, supplies, volunteers, money, employees) through a supply chain network. This course will explore methodologies for “medium term” decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and supply chain design.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

About the Course and the HHSCM Course Series

This course is the second in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://hhls.scl.gatech.edu/ by December 31, 2023.  

Questions? Reach out to chhs@gatech.edu!

]]> Andy Haleblian 1 1693309935 2023-08-29 11:52:15 1693310016 2023-08-29 11:53:36 0 0 event This course explores methodologies for tactical decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and transportation decisions.

]]>
2024-04-15T09:30:00-04:00 2024-04-18T13:00:00-04:00 2024-04-18T13:00:00-04:00 2024-04-15 13:30:00 2024-04-18 17:00:00 2024-04-18 17:00:00 2024-04-15T09:30:00-04:00 2024-04-18T13:00:00-04:00 America/New_York America/New_York datetime 2024-04-15 09:30:00 2024-04-18 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu 

]]>
<![CDATA[Course Details via Center for Health and Humanitarian Systems website]]> <![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Health & Humanitarian Supply Chain Management Certificate]]> <![CDATA[Apply for a Scholarship!]]>
<![CDATA[Professional Education Course: Responsive Supply Chain Design and Operations]]> 27233 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm ET each day.

Course Description

Meeting demand in a timely and cost-effective manner is important both in public and private supply chains, and heavily depend on the design and operation of these supply chains. Demand is affected by ongoing factors such as local economy, infrastructure, and geographic location, as well as unexpected events such as natural or manmade disasters or other large-scale disruptions. Designing and operating responsive supply chains requires the consideration of uncertainty in timing, scope, scale, and understanding of various topics such as forecasting, distribution network design, and inventory management. This course will examine methods and models for making supply chain design and operational decisions and explore the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for meeting the need of customers and beneficiaries.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

About the Course and the HHSCM Course Series

This course is the first in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://hhls.scl.gatech.edu/ by December 31, 2023.  

Questions? Reach out to chhs@gatech.edu!

]]> Andy Haleblian 1 1693309716 2023-08-29 11:48:36 1693309930 2023-08-29 11:52:10 0 0 event This course examines methods and models for making pre-planning decisions and explores the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for sustaining wellness.

]]>
2024-04-01T09:30:00-04:00 2024-04-04T13:00:00-04:00 2024-04-04T13:00:00-04:00 2024-04-01 13:30:00 2024-04-04 17:00:00 2024-04-04 17:00:00 2024-04-01T09:30:00-04:00 2024-04-04T13:00:00-04:00 America/New_York America/New_York datetime 2024-04-01 09:30:00 2024-04-04 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu

]]>
<![CDATA[Course Details via Center for Health and Humanitarian Systems website]]> <![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Health & Humanitarian Supply Chain Management Certificate]]> <![CDATA[Apply for a Scholarship!]]>
<![CDATA[2023 Health & Humanitarian Logistics Conference (Kenya)]]> 27233 The 15th annual Health & Humanitarian Logistics (HHL) Conference will take place November 21-22 | Nairobi, Kenya to provide an open forum to discuss the challenges and new solutions in disaster preparedness and response, long-term development and humanitarian aid, and global health delivery. The event serves as an opportunity for practitioners, aid organizations, government representatives, innovators, academics, and others to learn and engage on challenges and opportunities relevant to today’s health and humanitarian supply chains. It is an ideal platform for sponsors and innovators to show-case their brand to a wide audience and link to a valuable annual cycle of follow-on activities. The event will be driven by case studies and discussions with a mix of high level and practical engagement.

Representatives from the humanitarian sector, government, NGOs, foundations and private industry, and academia present diverse perspectives in health and humanitarian challenges through keynote addresses, panel discussions, focused workshops, lunchtime group discussions, and interactive poster sessions covering a broad set of research topics and applications.

We invite you to attend and participate as a presenter in the following areas: collaborative workshopsoral presentations and poster sessions. To see our requirements and submit a proposal visit our Call For Presentations Page.

]]> Andy Haleblian 1 1693265173 2023-08-28 23:26:13 1693309325 2023-08-29 11:42:05 0 0 event The  15th annual Health & Humanitarian Logistics (HHL) Conference will take place November 21-22 | Nairobi, Kenya to provide an open forum to discuss the challenges and new solutions in disaster preparedness and response, long-term development and humanitarian aid, and global health delivery. We invite you to attend and participate as a presenter in the following areas: collaborative workshopsoral presentations and poster sessions.

]]>
2023-11-21T14:00:00-05:00 2023-11-22T18:15:00-05:00 2023-11-22T18:15:00-05:00 2023-11-21 19:00:00 2023-11-22 23:15:00 2023-11-22 23:15:00 2023-11-21T14:00:00-05:00 2023-11-22T18:15:00-05:00 America/New_York America/New_York datetime 2023-11-21 02:00:00 2023-11-22 06:15:00 America/New_York America/New_York datetime <![CDATA[Register Now for Early Bird Rates!]]> If you have a question or comments for the organizers, please submit them using our contact form.

]]>
<![CDATA[Register Now for Early Bird Rates!]]> <![CDATA[Call for Presentations]]>
<![CDATA[Climate Sustainability Challenges and Opportunities Workshop]]> 36284 The workshop is focused on advancing the frontiers of climate sustainability through the presentation of cutting-edge research, prioritizing time for discussion and fostering informal interactions among students and scientists of all career stages. This workshop includes four themes: Climate Justice, Climate Science, Climate Modeling, Climate Tech and Solutions. A daily schedule can be found here.

Co-organized by Georgia Tech and Spelman College, it will be held in the Manley Atrium at Spelman College in the heart of the Atlanta University Center, near the historic community of West End, from September 10 to September 14, 2023, and is open to Georgia Tech graduate students and to undergraduates from all Colleges and Universities in the Atlanta area. In addition to premier talks, the workshop has designated time for poster sessions from individuals of all career stages, and communal meals for creating lasting collaborations and friendships and for informal networking opportunities with leaders in the climate sustainability challenge.

Participation at the event is free of charge, but registration is required, because space is limited. The registration deadline has been updated to September 3rd (Sunday). For registration, please complete the form at https://forms.gle/7CjuApx4V5cLFTUX7 or follow the QR code on the enclosed poster.  

Financial support for the workshop has been generously provided by UCAR/NCAR, ORAU, and the Strategic Energy Institute, the Direct Air Capture Center (DirACC), the Brook Byers Institute for Sustainable Systems and the Ocean Science and Engineering Program at Georgia Tech.

]]> chenriquez8 1 1692845901 2023-08-24 02:58:21 1692846018 2023-08-24 03:00:18 0 0 event The Georgia Institute of Technology and Spelman College are pleased to present Climate Sustainability: Challenges & Opportunities. A workshop on climate science, climate solutions, and climate justice organized by graduate students for graduate and undergraduate students.

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2023-09-10T09:00:00-04:00 2023-09-14T09:00:00-04:00 2023-09-14T09:00:00-04:00 2023-09-10 13:00:00 2023-09-14 13:00:00 2023-09-14 13:00:00 2023-09-10T09:00:00-04:00 2023-09-14T09:00:00-04:00 America/New_York America/New_York datetime 2023-09-10 09:00:00 2023-09-14 09:00:00 America/New_York America/New_York datetime <![CDATA[]]> 671470 671470 image <![CDATA[Climate Sustainability Challenges and Opportunities Workshop]]> image/png 1692845651 2023-08-24 02:54:11 1692845669 2023-08-24 02:54:29
<![CDATA[ISyE Thesis Defense Announcement – Sara Kaboudvand]]> 36284 Thesis Title: Hyperconnected Parcel Logistics: Planning and Assessment  

Thesis Committee: 

Dr. Benoit Montreuil (advisor), School of Industrial and Systems Engineering, Georgia Institute of Technology 

Dr. Martin Savelsbergh (co-advisor), School of Industrial and Systems Engineering, Georgia Institute of Technology 

Dr. Leon McGinnis, School of Industrial and Systems Engineering, Georgia Institute of Technology 

Dr. Uday Venkatadri, School of Industrial Engineering, Dalhousie University 

Dr. Walid Klibi, Supply Chain Center of Excellence, Kedge Business School  

Date and Time: Friday, August 25, 2023, 10 am - 12 pm EST 

Online Meeting Link: Click Here to Join the Meeting  

Abstract: 

Today's last-mile logistics faces numerous challenges, particularly concerning cost management and meeting customers' escalating expectations. With the steady expansion of e-commerce, the volume of last-mile deliveries has surged, consequently driving up costs for logistics providers. Additionally, the competitive landscape has intensified, with businesses striving to offer same-day or even on-demand deliveries to cater to customers' demands for convenience and satisfaction. This thesis investigates pivotal facets of megacity parcel logistics, with a specific emphasis on two main objectives: (1) realizing the cost-saving potential of package consolidation and containerization, and (2) analytically assessing novel logistical network configurations that revolutionize the handling of packages within the logistics network. 

In Chapter 2, we present a formal definition of containerized consolidation in megacity parcel logistics and explore its potential benefits, including reductions in total handling and transit costs. We propose an Integer Programming (IP) formulation and conduct an extensive sensitivity analysis across diverse network configurations and demand patterns. The findings showcase the potential for remarkable savings, with up to 80% reduction in handling costs and over 20% reduction in total in-transit costs through the implementation of containerized consolidation.  

In Chapter 3, we shift our focus from tactical containerized consolidation planning to a dynamic and data-driven approach tailored to the fast-paced last-mile delivery environment. We present a Mixed Integer Programming (MIP) formulation for decentralized and dynamic consolidation and containerization of packages at distribution hubs. Given the complexity of this model, solving it in real-time dynamic scenarios is impractical. To tackle this challenge, we introduce two heuristic approaches to handle dynamic decisions and assess their performance against the optimal solution. Our findings demonstrate that the proposed heuristics can achieve nearly optimal solutions while considerably reducing computational time. 

Finally, In Chapter 4, we adopt a more holistic perspective on megacity parcel logistics, evaluating the advantages of the recently introduced and innovative Hyperconnected Logistic Web concept for enhancing urban parcel logistics efficiency and responsiveness. We emphasize the significance of a holistic approach in validating such solutions and introduce an agent-based discrete-event simulator platform, capable of modeling urban delivery networks at the parcel granularity. This simulator adeptly handles a range of strategic, tactical, and operational decisions necessary for urban logistic operations. Utilizing the proposed model and given real data from a high-profile package delivery company, we execute two sets of experiments to evaluate the effect of different package routing and consolidation strategies on logistics network performance. Preliminary results illustrate that a higher level of interconnection among nodes in the lower network tiers leads to reduced in-transit costs, enabling the provision of tighter customer delivery services. Furthermore, the initial experiments with the consolidation heuristics introduced in Chapter 3, demonstrate that the proposed heuristic yields lower operational costs compared to other traditional consolidation approaches. 

]]> chenriquez8 1 1692843910 2023-08-24 02:25:10 1692844023 2023-08-24 02:27:03 0 0 event ISyE Thesis Defense Announcement – Sara Kaboudvand, Industrial Engineering Ph.D. Candidate 

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2023-08-25T10:00:00-04:00 2023-08-30T12:00:00-04:00 2023-08-30T12:00:00-04:00 2023-08-25 14:00:00 2023-08-30 16:00:00 2023-08-30 16:00:00 2023-08-25T10:00:00-04:00 2023-08-30T12:00:00-04:00 America/New_York America/New_York datetime 2023-08-25 10:00:00 2023-08-30 12:00:00 America/New_York America/New_York datetime <![CDATA[Candidate ISyE Profile]]> 671469 671469 image <![CDATA[Sara Kaboudvand]]> image/jpeg 1692843696 2023-08-24 02:21:36 1692843721 2023-08-24 02:22:01
<![CDATA[ISyE Seminar Speaker - Jianfeng Lu]]> 36374 Title:

Actor-critic method for solving high dimensional Hamilton-Jacobi-Bellman type PDEs 

 

Abstract:

In this talk, we will discuss numerical approach to solve high dimensional Hamilton-Jacobi-Bellman (HJB) type partial differential equations (PDEs). 

The HJB PDEs, reformulated as optimal control problems, are tackled by the actor-critic framework inspired by reinforcement learning, based on neural network parametrization of the value and control functions. Within the actor-critic framework, we employ a policy gradient approach to improve the control, while for the value function, we derive a variance reduced least-squares temporal difference method using stochastic calculus. We will also discuss convergence analysis for the actor-critic method, in particular the policy gradient method for solving stochastic optimal control. Joint work with Jiequn Han (Flatiron Institute) and Mo Zhou (Duke University).

 

Bio:

Jianfeng Lu is a Professor of Mathematics, Physics, and Chemistry at Duke University. Before joining Duke University, he obtained his PhD in Applied Mathematics from Princeton University in 2009 and was a Courant Instructor at New York University from 2009 to 2012. He works on mathematical analysis and algorithm development for problems and challenges arising from computational physics, theoretical chemistry, materials science, high-dimensional PDEs, and machine learning. He is a fellow of AMS. His work has been recognized by a Sloan Fellowship, a NSF Career Award, the IMA Prize in Mathematics and its Applications, and the Feng Kang Prize.

 

]]> mwelch39 1 1692358264 2023-08-18 11:31:04 1692358264 2023-08-18 11:31:04 0 0 event  

In this talk, we will discuss numerical approach to solve high dimensional Hamilton-Jacobi-Bellman (HJB) type partial differential equations (PDEs). 

The HJB PDEs, reformulated as optimal control problems, are tackled by the actor-critic framework inspired by reinforcement learning, based on neural network parametrization of the value and control functions. Within the actor-critic framework, we employ a policy gradient approach to improve the control, while for the value function, we derive a variance reduced least-squares temporal difference method using stochastic calculus. We will also discuss convergence analysis for the actor-critic method, in particular the policy gradient method for solving stochastic optimal control. Joint work with Jiequn Han (Flatiron Institute) and Mo Zhou (Duke University).

 

 

 

 

 

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2023-09-01T11:30:00-04:00 2023-09-01T12:30:00-04:00 2023-09-01T12:30:00-04:00 2023-09-01 15:30:00 2023-09-01 16:30:00 2023-09-01 16:30:00 2023-09-01T11:30:00-04:00 2023-09-01T12:30:00-04:00 America/New_York America/New_York datetime 2023-09-01 11:30:00 2023-09-01 12:30:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[SCL Course: Contracting and Legal Oversight (Virtual/Instructor-led)]]> 27233 Course Description

Contracting and Legal Oversight provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.

The online version of the course is comprised of (3) instructor-led LIVE group webinars, homework, and pre-work (e.g. installing and testing software on your computer, testing connectivity with Canvas LMS and BlueJeans meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

This course is ideal for contract managers, procurement professionals, sourcing initiative leaders, project managers and all procurement & supply management-related professionals involved with bid contract development, contract execution or supplier performance management.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1692281033 2023-08-17 14:03:53 1692281116 2023-08-17 14:05:16 0 0 event This course provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.

]]>
2024-02-29T16:00:00-05:00 2024-03-06T18:00:00-05:00 2024-03-06T18:00:00-05:00 2024-02-29 21:00:00 2024-03-06 23:00:00 2024-03-06 23:00:00 2024-02-29T16:00:00-05:00 2024-03-06T18:00:00-05:00 America/New_York America/New_York datetime 2024-02-29 04:00:00 2024-03-06 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[ISyE Seminar Speaker - Mor Harchol]]> 36374 Title:

Scheduling Multiserver Compute Jobs

 

Abstract:

 

Almost all queueing models assume that a job runs on a single server. But this one-server-per-job model is not a good representation of today's compute jobs.   A typical data center job today occupies multiple cores concurrently.  We refer to a job that requires a fixed number of cores for some duration as a multiserver job.  Unfortunately, very little is known about the performance of multiserver job queueing models.  We present the first results on response time for multiserver job models. To do this, we introduce a new scheduling policy, called ServerFilling, which is both throughput-optimal in many settings and also lends itself to response time analysis.  

 Joint work with:  Isaac Grosof and Alan Scheller-Wolf  

 

Bio:

Mor Harchol-Balter is the Bruce J. Nelson Professor of Computer Science at Carnegie Mellon University.  She is a Fellow of both ACM and IEEE.  She currently serves as SIG Chair for ACM SIGMETRICS, and has previously served as General Chair and TPC Chair for SIGMETRICS. She is the recipient of the NSF CAREER award, dozens of Industrial Faculty Awards, and several teaching awards, including the Herbert A. Simon Teaching Award and the Spira Teaching Award. Mor is the author of a popular queueing theory textbook, “Performance Analysis and Design of Computer Systems,” published by Cambridge University Press 2013.  She also has a new textbook coming out called “Introduction to Probability for Computing,” published by Cambridge University Press 2024. Mor’s work has been honored with many paper awards: INFORMS George Nicholson Prize 22, SIGMETRICS 21, SIGMETRICS 19, PERFORMANCE 18, INFORMS APS 18, EUROSYS 16, MASCOTS 16, MICRO 10, SIGMETRICS 03, ITC 03, SIGMETRICS 96.  She has also been blessed with fantastic PhD students, almost all of whom are professors at top universities. 

 

 

]]> mwelch39 1 1692275719 2023-08-17 12:35:19 1692275719 2023-08-17 12:35:19 0 0 event Almost all queueing models assume that a job runs on a single server. But this one-server-per-job model is not a good representation of today's compute jobs.   A typical data center job today occupies multiple cores concurrently.  We refer to a job that requires a fixed number of cores for some duration as a multiserver job.  Unfortunately, very little is known about the performance of multiserver job queueing models.  We present the first results on response time for multiserver job models. To do this, we introduce a new scheduling policy, called ServerFilling, which is both throughput-optimal in many settings and also lends itself to response time analysis.  

 

 Joint work with:  Isaac Grosof and Alan Scheller-Wolf  

]]>
2023-08-25T11:30:00-04:00 2023-08-25T12:30:00-04:00 2023-08-25T12:30:00-04:00 2023-08-25 15:30:00 2023-08-25 16:30:00 2023-08-25 16:30:00 2023-08-25T11:30:00-04:00 2023-08-25T12:30:00-04:00 America/New_York America/New_York datetime 2023-08-25 11:30:00 2023-08-25 12:30:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISYE Statistic Seminar - Annie Qu]]> 36433 A Model-Agnostic Graph Neural Network for Integrating Local and Global Information

Abstract

Graph neural networks (GNNs) have achieved promising performance in a variety of graph focused

tasks. Despite their success, the two major limitations of existing GNNs are the capability

of learning various-order representations and providing interpretability of such deep learning-based

black-box models. To tackle these issues, we propose a novel Model-agnostic Graph Neural

Network (MaGNet) framework. The proposed framework is able to extract knowledge from

high-order neighbors, sequentially integrates information of various orders, and offers explanations

for the learned model by identifying influential compact graph structures. In particular, MaGNet

consists of two components: an estimation model for the latent representation of complex

relationships under graph topology, and an interpretation model that identifies influential nodes,

edges, and important node features. Theoretically, we establish the generalization error bound for

MaGNet via empirical Rademacher complexity and showcase its power to represent the layer-wise

neighborhood mixing. We conduct comprehensive numerical studies using both simulated data

and a real-world case study on investigating the neural mechanisms of the rat hippocampus,

demonstrating that the performance of MaGNet is competitive with state-of-the-art methods.

Bio: 

Annie Qu

Chancellor’s Professor, Department of Statistics, University of California Irvine

Ph.D., Statistics, the Pennsylvania State University

Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data, and developing cutting-edge statistical methods and theory in machine learning and algorithms on personalized medicine, text mining, recommender systems, medical imaging data and network data analyses for complex heterogeneous data. The newly developed methods are able to extract essential and relevant information from large volume high-dimensional data. Her research has impacts in many fields such as biomedical studies, genomic research, public health research, social and political sciences.

Before she joins the UC Irvine, Dr. Qu is Data Science Founder Professor of Statistics, and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She was awarded as Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC, a recipient of the NSF Career award in 2004-2009. She is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and a Fellow of American Association for the Advancement of Science. She is also a recipient of Medallion Award and Lecturer. She is JASA Theory and Methods co-editor in 2023-2025.

]]> mrussell89 1 1692191176 2023-08-16 13:06:16 1692191176 2023-08-16 13:06:16 0 0 event Graph neural networks (GNNs) have achieved promising performance in a variety of graph focused

tasks. Despite their success, the two major limitations of existing GNNs are the capability

of learning various-order representations and providing interpretability of such deep learning-based

black-box models. To tackle these issues, we propose a novel Model-agnostic Graph Neural

Network (MaGNet) framework. The proposed framework is able to extract knowledge from

high-order neighbors, sequentially integrates information of various orders, and offers explanations

for the learned model by identifying influential compact graph structures. In particular, MaGNet

consists of two components: an estimation model for the latent representation of complex

relationships under graph topology, and an interpretation model that identifies influential nodes,

edges, and important node features. Theoretically, we establish the generalization error bound for

MaGNet via empirical Rademacher complexity and showcase its power to represent the layer-wise

neighborhood mixing. We conduct comprehensive numerical studies using both simulated data

and a real-world case study on investigating the neural mechanisms of the rat hippocampus,

demonstrating that the performance of MaGNet is competitive with state-of-the-art methods.

]]>
2023-09-12T11:00:00-04:00 2023-09-12T12:00:00-04:00 2023-09-12T12:00:00-04:00 2023-09-12 15:00:00 2023-09-12 16:00:00 2023-09-12 16:00:00 2023-09-12T11:00:00-04:00 2023-09-12T12:00:00-04:00 America/New_York America/New_York datetime 2023-09-12 11:00:00 2023-09-12 12:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISYE Statistic Seminar - Sabyasachi Chatterjee]]> 36433

TITLE:  Theory for Cross Validation in Nonparametric Regression

 
ABSTRACT:  We formulate a general cross validation framework for signal denoising. The general framework is then applied to nonparametric regression methods such as Trend Filtering and Dyadic CART. The resulting cross validated versions are then shown to attain nearly the same rates of convergence as are known for the optimally tuned analogues. There did not exist any previous theoretical analyses of cross validated versions of Trend Filtering or Dyadic CART. Our general framework is inspired by the ideas in Chatterjee and Jafarov (2015) and is potentially applicable to a wide range of estimation methods which use tuning parameters.
 
BIO: I am an Assistant Professor (from 2017 onwards) in the Statistics Department at University of Illinois at Urbana Champaign. Most of my research has been in Nonparametric Function Estimation/ Statistical Signal Processing. I am also interested in Machine Learning and Probability. I obtained my Phd in 2014 at Yale University and then was a Kruskal Instructor at University of Chicago till 2017.

 

]]> mrussell89 1 1692189044 2023-08-16 12:30:44 1692189313 2023-08-16 12:35:13 0 0 event
We formulate a general cross validation framework for signal denoising. The general framework is then applied to nonparametric regression methods such as Trend Filtering and Dyadic CART. The resulting cross validated versions are then shown to attain nearly the same rates of convergence as are known for the optimally tuned analogues. There did not exist any previous theoretical analyses of cross validated versions of Trend Filtering or Dyadic CART. Our general framework is inspired by the ideas in Chatterjee and Jafarov (2015) and is potentially applicable to a wide range of estimation methods which use tuning parameters.
]]>
2023-08-29T13:00:00-04:00 2023-08-29T14:00:00-04:00 2023-08-29T14:00:00-04:00 2023-08-29 17:00:00 2023-08-29 18:00:00 2023-08-29 18:00:00 2023-08-29T13:00:00-04:00 2023-08-29T14:00:00-04:00 America/New_York America/New_York datetime 2023-08-29 01:00:00 2023-08-29 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar Speaker - Sheldon H. Jacobson]]> 36374 Title:

Using Computational Algorithms for Political Redistricting 

 Abstract:

Political redistricting is a multi-criteria problem with conflicting objectives (based on metrics like compactness, population balance, and efficiency gaps, among others). Many of these metrics have received significant attention, though they remain controversial as to which such metrics are best suited to define fair district maps. This research uses a multi-objective optimization approach to reveal obstacles in defining fair district maps. The results obtained challenge a number of common perceptions of redistricting, suggesting that defining fair maps may not only be extremely difficult, but also, simply unrealistic. 

 Joint research with Rahul Swamy and Douglas King 

Bio: 

Sheldon H. Jacobson is a Founder Professor of Computer Science at the University of Illinois.  He has a B.Sc. and M.Sc. (both in Mathematics) from McGill University, and a M.S. and Ph.D. (both in Operations Research) from Cornell University.  From 2012-2014, he was on leave from the University of Illinois, serving as a Program Director at the National Science Foundation.  His research interests span theory and practice, covering decision-making under uncertainty and optimization-based artificial intelligence, with applications in aviation security, public policy, public health, and sports.  He has been recognized by numerous awards, including a Guggenheim Fellowship from the John Simon Guggenheim Memorial Foundation.  He is a fellow of AAAS, IISE, and INFORMS.  He serves as the Founding Director for the Institute for Computational Redistricting (ICOR), http://redistricting.cs.illinois.edu. 

 

        

]]> mwelch39 1 1691526424 2023-08-08 20:27:04 1691526424 2023-08-08 20:27:04 0 0 event Political redistricting is a multi-criteria problem with conflicting objectives (based on metrics like compactness, population balance, and efficiency gaps, among others). Many of these metrics have received significant attention, though they remain controversial as to which such metrics are best suited to define fair district maps. This research uses a multi-objective optimization approach to reveal obstacles in defining fair district maps. The results obtained challenge a number of common perceptions of redistricting, suggesting that defining fair maps may not only be extremely difficult, but also, simply unrealistic. 

 

Joint research with Rahul Swamy and Douglas King 

]]>
2023-09-22T11:30:00-04:00 2023-09-22T12:30:00-04:00 2023-09-22T12:30:00-04:00 2023-09-22 15:30:00 2023-09-22 16:30:00 2023-09-22 16:30:00 2023-09-22T11:30:00-04:00 2023-09-22T12:30:00-04:00 America/New_York America/New_York datetime 2023-09-22 11:30:00 2023-09-22 12:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[Fall 2023 IISE Career Fair]]> 27764 Every fall and spring semester, during the IISE Career Fair, companies across the nation come to Georgia Tech to recruit some of the nation’s top talent from our Bachelor’s and Master’s programs. Our students are recruited for a variety of roles and perform well past expectations in all positions. We hope that you will join us this semester and meet some of the country’s brightest students.

This fall semester, we will be hosting an in-person career fair on Thursday, September 21st, 2023, at McCamish Pavilion. We are actively working to enhance your career fair experience through allowing students to see a summary of all companies including majors recruited, years recruited, GPA requirements, and industry. This ensures that students can pinpoint the companies actively recruiting for them helping both students and recruiters! In addition, we will be having a general diagram of company booth locations so students can easily find your booth! If you have any questions or concerns, please email us at iise@gatech.edu.

Employer Registration: If you register and PAY by August, registration is $850. After August 1st, the payment will be $1000.00.

Registration Deadline: September 14, 2023

]]> Scott Jacobson 1 1689351321 2023-07-14 16:15:21 1690567678 2023-07-28 18:07:58 0 0 event Fall 2023 IISE Career Fair

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2023-09-21T09:00:00-04:00 2023-09-21T15:30:00-04:00 2023-09-21T15:30:00-04:00 2023-09-21 13:00:00 2023-09-21 19:30:00 2023-09-21 19:30:00 2023-09-21T09:00:00-04:00 2023-09-21T15:30:00-04:00 America/New_York America/New_York datetime 2023-09-21 09:00:00 2023-09-21 03:30:00 America/New_York America/New_York datetime <![CDATA[McCamish Pavilion]]> iise@gatech.edu

]]>
671283 671283 image <![CDATA[1-iise.jpg]]> image/jpeg 1690567631 2023-07-28 18:07:11 1690567631 2023-07-28 18:07:11 <![CDATA[IISE Career Fair Information]]>
<![CDATA[Leadership Development Seminar/Webinar Series: Featuring Ron Johnson]]> 27233 Ronald L. Johnson is a Professor of the Practice in ISyE and Faculty Leadership Fellow at Georgia Tech. Prior to joining the faculty at Georgia Tech, Retired Major General Ronald L. Johnson was the National Basketball Association’s first Senior Vice President, Referee Operations, responsible for all aspects of the NBA’s officiating program, including recruiting, training and development, scheduling, data management and analysis, and work rules enforcement.

Prior to joining the NBA, General Johnson served as the deputy commanding general and deputy chief of engineers (COO), the second highest-ranking senior engineer staff officer for the U.S. Army Corps of Engineers (USACE). He retired in April 2008 after serving 32 years of military service.

From October 2005 until his retirement, Johnson was assisting the chief of engineers in maintaining Army Staff oversight for organizing, training, and equipping 70,000 engineer soldiers in the active and reserve components and USACE. He additionally served as the principal engineer advisor to the chief of staff of the Army.

Johnson’s awards and decorations include the Distinguished Service Medal (with two Oak Leaf Clusters), Bronze Star, Legion of Merit (with 4 Oak Leaf Clusters), Combat Action Badge, Parachutist Badge, Air Assault Badge, Army Staff Identification Badge, and the Recruiter Badge.

We look forward to having you attend the event in person or online!

Zoom Meeting
https://gatech.zoom.us/j/95517853132
Meeting ID: 955 1785 3132

]]> Andy Haleblian 1 1680783772 2023-04-06 12:22:52 1688664764 2023-07-06 17:32:44 0 0 event Ronald L. Johnson is a Professor of the Practice in ISyE and Faculty Leadership Fellow at Georgia Tech. Prior to joining the faculty at Georgia Tech, Retired Major General Ronald L. Johnson was the National Basketball Association’s first Senior Vice President, Referee Operations, responsible for all aspects of the NBA’s officiating program, including recruiting, training and development, scheduling, data management and analysis, and work rules enforcement.

]]>
2023-04-06T15:30:00-04:00 2023-04-06T16:00:00-04:00 2023-04-06T16:00:00-04:00 2023-04-06 19:30:00 2023-04-06 20:00:00 2023-04-06 20:00:00 2023-04-06T15:30:00-04:00 2023-04-06T16:00:00-04:00 America/New_York America/New_York datetime 2023-04-06 03:30:00 2023-04-06 04:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building Complex]]> chhs@gatech.edu

]]>
670463 670463 image <![CDATA[rjohnson.jpg]]> image/jpeg 1680784242 2023-04-06 12:30:42 1680784242 2023-04-06 12:30:42
<![CDATA[SCL September 2023 Supply Chain Day]]> 27233 Georgia Tech Supply Chain students and employers, please join us for our first fall Supply Chain Day! 

Event Details

On Campus/In-Person (Georgia Tech Exhibition Hall)

Students

We strongly encourage you to attend to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).

Organizations

If you are interested in hosting a table for the upcoming session, please let us know after reviewing the below information within our website.

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday.

]]> Andy Haleblian 1 1686253121 2023-06-08 19:38:41 1688644364 2023-07-06 11:52:44 0 0 event Georgia Tech Supply Chain students and employers, please join us for our first fall Supply Chain Day! We will be hosting an On Campus session Wednesday, September 13, 2023 from 11am-2pm ET at the Georgia Tech Exhibition Hall.

]]>
2023-09-13T11:00:00-04:00 2023-09-13T14:00:00-04:00 2023-09-13T14:00:00-04:00 2023-09-13 15:00:00 2023-09-13 18:00:00 2023-09-13 18:00:00 2023-09-13T11:00:00-04:00 2023-09-13T14:00:00-04:00 America/New_York America/New_York datetime 2023-09-13 11:00:00 2023-09-13 02:00:00 America/New_York America/New_York datetime <![CDATA[Georgia Tech Exhibition Hall]]> event@scl.gatech.edu

]]>
670952 670952 image <![CDATA[Wednesday, September 13, 2023 Supply Chain Day Career Fair]]> image/jpeg 1686253353 2023-06-08 19:42:33 1686253353 2023-06-08 19:42:33 <![CDATA[Register online to attend (for Georgia Tech students)]]> <![CDATA[Supply Chain and Logistics Institute website]]>
<![CDATA[SCL Course: Creating Business Value with Statistical Analysis (Virtual/Instructor-led)]]> 27233 Course Description

This course is the second in the four-course Supply Chain Analytics Professional certificate program. It emphasizes operational performance metrics to align supply chain management with strategic business goals. You’ll learn several statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) along with inventory management models. You’ll use diagnostic analytics with PowerBI and Python to conduct demand and service profiling, undertake root cause analysis, and use time series forecasting in inventory management.

The online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1657283144 2022-07-08 12:25:44 1684323102 2023-05-17 11:31:42 0 0 event Learn statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) and inventory management models to improve operational performance metrics and align supply chain management with strategic business goals. 

]]>
2023-07-10T13:00:00-04:00 2023-07-13T17:00:00-04:00 2023-07-13T17:00:00-04:00 2023-07-10 17:00:00 2023-07-13 21:00:00 2023-07-13 21:00:00 2023-07-10T13:00:00-04:00 2023-07-13T17:00:00-04:00 America/New_York America/New_York datetime 2023-07-10 01:00:00 2023-07-13 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

]]>
<![CDATA[Course webpage within the SCL website]]>
<![CDATA[Accelerating global health research and development: A new USAID strategic framework]]> 36008 This virtual event will feature remarks from Assistant Administrator for Global Health Dr. Atul Gawande alongside other experts from USAID and the global health research community. There will be time for dialogue with the R&D community regarding how USAID can best leverage the community’s expertise to accelerate progress.

]]> achambless6 1 1683838756 2023-05-11 20:59:16 1683840199 2023-05-11 21:23:19 0 0 event For more than 50 years, USAID has supported the development, introduction, and scale up of urgently needed health technologies and approaches as part of its broader mission to advance global health. These investments have delivered breakthrough innovations—from new treatments for malaria and tuberculosis to new HIV prevention tools—that have saved countless lives, while at the same time strengthening the capacity of partner nations to conduct research and translate evidence to action. 

]]>
2023-05-16T09:30:00-04:00 2023-05-16T11:00:00-04:00 2023-05-16T11:00:00-04:00 2023-05-16 13:30:00 2023-05-16 15:00:00 2023-05-16 15:00:00 2023-05-16T09:30:00-04:00 2023-05-16T11:00:00-04:00 America/New_York America/New_York datetime 2023-05-16 09:30:00 2023-05-16 11:00:00 America/New_York America/New_York datetime <![CDATA[]]> <![CDATA[Zoom Link to Register]]>
<![CDATA[ISyE Seminar - Dmitrii Ostrovskii]]> 34977 Title:

Self-concordance meets Laplace approximation: 

Fast and optimal algorithm for online portfolio selection

Abstract:

In 1991, Thomas M. Cover introduced a simple and elegant mathematical model for stock trading, which later on came to be known as online portfolio selection (OPS). In each round t = 1, 2, ..., T, the trader selects a portfolio—distribution pt  R+d of the current capital over the set of d assets; then, the adversary generates a vector rt  R+d of returns (i.e., relative prices of the assets), and the trader’s capital is multiplied by the “aggregated return” ptt. The model makes no further assumptions on the asset prices; in particular, they are not assumed to be sampled randomly from a distribution; at the same time, it captures the two key properties of the stock market: that it is naturally adversarial, and that money tends to accummulate multiplicatively. In the 30 years that followed, it had received a great deal of attention across several communities.

In the same paper, Cover also proposed an algorithm, termed Universal Portfolios, that admitted a strong performance guarantee: the regret of O(dlog T) against the best portfolio in hindsight, and without any restrictions of returns or portfolios. This guarantee was later on shown to be worst-case optimal; unfortunately, exact computation of a universal portfolio amounts to averaging over a log-concave distribution, which is a challenging task. To address this, Kalai and Vempala (2002) achieved the running time of O(d4T14) per round via sampling techniques. However, with such a running time essentially prohibiting problems of nontrivial size, yet remaining state-of-the-art, the problem of finding an optimal and practical OPS algorithm was left open.

In this talk, after discussing some of the arising technical challenges, I shall present a fast and optimal OPS algorithm that combines regret optimality with the runtime of O(d2T), thus dramatically improving state of the art. Its motivation and analysis turn out to be related to establishing a sharp bound on the accuracy of the Laplace approximation for a log-concave distribution with a polyhedral support; this result is of independent interest, and I shall explore the underlying connection. Finally, I shall present a broader perspective of these ideas beyond online portfolio selection.

Bio:

Dmitrii M. Ostrovskii is an Assistant Professor (RTPC) of Mathematics at the University of Southern California. Dmitrii graduated in 2018, advised by Anatoli Juditsky (University of Grenoble) and Zaid Harchaoui (University of Washington), and actively collaborated with Arkadi Nemirovski (Georgia Tech ISyE) when working on his PhD thesis. Prior to the present appointment, he was a postdoc first at Inria Research Institute in Paris, hosted by Francis Bach and funded by the ERCIM Alain Bensoussan fellowship (2018-2019), and then at USC Viterbi School of Engineering (2019-2021). Dmitrii's interests span several topics at the intersection of optimization theory, mathematical statistics, machine learning, and operations research. In particular, his recent work concerns nonconvex min-max optimization, robust estimation, statistical testing under privacy constraints, and tractable learning algorithms with near-optimal guarantees.

]]> Julie Smith 1 1682299466 2023-04-24 01:24:26 1682299524 2023-04-24 01:25:24 0 0 event Abstract:

In 1991, Thomas M. Cover introduced a simple and elegant mathematical model for stock trading, which later on came to be known as online portfolio selection (OPS). In each round t = 1, 2, ..., T, the trader selects a portfolio—distribution pt  R+d of the current capital over the set of d assets; then, the adversary generates a vector rt  R+d of returns (i.e., relative prices of the assets), and the trader’s capital is multiplied by the “aggregated return” ptt. The model makes no further assumptions on the asset prices; in particular, they are not assumed to be sampled randomly from a distribution; at the same time, it captures the two key properties of the stock market: that it is naturally adversarial, and that money tends to accummulate multiplicatively. In the 30 years that followed, it had received a great deal of attention across several communities.

In the same paper, Cover also proposed an algorithm, termed Universal Portfolios, that admitted a strong performance guarantee: the regret of O(dlog T) against the best portfolio in hindsight, and without any restrictions of returns or portfolios. This guarantee was later on shown to be worst-case optimal; unfortunately, exact computation of a universal portfolio amounts to averaging over a log-concave distribution, which is a challenging task. To address this, Kalai and Vempala (2002) achieved the running time of O(d4T14) per round via sampling techniques. However, with such a running time essentially prohibiting problems of nontrivial size, yet remaining state-of-the-art, the problem of finding an optimal and practical OPS algorithm was left open.

In this talk, after discussing some of the arising technical challenges, I shall present a fast and optimal OPS algorithm that combines regret optimality with the runtime of O(d2T), thus dramatically improving state of the art. Its motivation and analysis turn out to be related to establishing a sharp bound on the accuracy of the Laplace approximation for a log-concave distribution with a polyhedral support; this result is of independent interest, and I shall explore the underlying connection. Finally, I shall present a broader perspective of these ideas beyond online portfolio selection.

]]>
2023-04-25T11:00:00-04:00 2023-04-25T12:00:00-04:00 2023-04-25T12:00:00-04:00 2023-04-25 15:00:00 2023-04-25 16:00:00 2023-04-25 16:00:00 2023-04-25T11:00:00-04:00 2023-04-25T12:00:00-04:00 America/New_York America/New_York datetime 2023-04-25 11:00:00 2023-04-25 12:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar Speaker - Peter Glynn]]> 36374 Title:

The Fragility of Optimized Bandits

 

Abstract:

Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that algorithms that are optimal over certain exponential families can achieve expected regret that grows logarithmically in the number of trials, at a rate specified by the Lai-Robbins lower bound. In this talk, we point out that when one uses such optimized algorithms, the resulting regret distribution necessarily has a very heavy tail, specifically, that of a truncated Cauchy distribution. We show that optimized UCB algorithms are also fragile in an additional sense, namely when the problem is even slightly mis-specified, the regret can grow much faster than the conventional theory suggests. To alleviate the fragility issues exposed, we show that UCB algorithms can be modified so as to ensure a desired degree of robustness to mis-specification. This is joint work with Lin Fan.

 

Bio:

Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University, and also holds a courtesy appointment in the Department of Electrical Engineering. He received his Ph.D in Operations Research from Stanford University in 1982. He then joined the faculty of the University of Wisconsin at Madison, where he held a joint appointment between the Industrial Engineering Department and Mathematics Research Center, and courtesy appointments in Computer Science and Mathematics. In 1987, he returned to Stanford, where he joined the Department of Operations Research. From 1999 to 2005, he served as Deputy Chair of the Department of Management Science and Engineering, and was Director of Stanford's Institute for Computational and Mathematical Engineering from 2006 until 2010. He served as Chair of MS&E from 2011 through 2015. He is a Fellow of INFORMS and a Fellow of the Institute of Mathematical Statistics, and was an IMS Medallion Lecturer in 1995, a Lunteren Lecturer in 2007, the INFORMS Markov Lecturer in 2014, an Infosys-ICTS Turing Lecturer in 2019, and gave a Titan of Simulation talk at the 2019 Winter Simulation Conference. He was co-winner of the Outstanding Publication Awards from the INFORMS Simulation Society in 1993, 2008, and 2016, was a co-winner of the Best (Biannual) Publication Award from the INFORMS Applied Probability Society in 2009, was the co-winner of the John von Neumann Theory Prize from INFORMS in 2010, and gave the INFORMS Philip McCord Morse Lecture in 2020. In 2012, he was elected to the National Academy of Engineering, and in 2021 he received the Lifetime Professional Achievement Award of the INFORMS Simulation Society. He was Founding Editor-in-Chief of Stochastic Systems and served as Editor-in-Chief of Journal of Applied Probability and Advances in Applied Probability from 2016 to 2018. His research interests lie in simulation, computational probability, queueing theory, statistical inference for stochastic processes, and stochastic modeling.

 

]]> mwelch39 1 1682194930 2023-04-22 20:22:10 1682194930 2023-04-22 20:22:10 0 0 event Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that algorithms that are optimal over certain exponential families can achieve expected regret that grows logarithmically in the number of trials, at a rate specified by the Lai-Robbins lower bound. In this talk, we point out that when one uses such optimized algorithms, the resulting regret distribution necessarily has a very heavy tail, specifically, that of a truncated Cauchy distribution. We show that optimized UCB algorithms are also fragile in an additional sense, namely when the problem is even slightly mis-specified, the regret can grow much faster than the conventional theory suggests. To alleviate the fragility issues exposed, we show that UCB algorithms can be modified so as to ensure a desired degree of robustness to mis-specification. This is joint work with Lin Fan.

]]>
2023-04-28T11:30:00-04:00 2023-04-28T12:30:00-04:00 2023-04-28T12:30:00-04:00 2023-04-28 15:30:00 2023-04-28 16:30:00 2023-04-28 16:30:00 2023-04-28T11:30:00-04:00 2023-04-28T12:30:00-04:00 America/New_York America/New_York datetime 2023-04-28 11:30:00 2023-04-28 12:30:00 America/New_York America/New_York datetime <![CDATA[Exhibition Hall]]>
<![CDATA[ISyE Seminar: Modeling Polio Eradication with Dr. Kimberly M. Thompson]]> 27233 Seminar Abstract
In 1988, the World Health Assembly resolved to eradicate poliomyelitis by the year 2000. As of 2023, the job is not done. Since 2000, analytical modeling of the polio end game has provided critical insights to some national and global decision makers. However, the Global Polio Eradication Initiative (GPEI) partnership has evolved over time, with different perspectives driving the development and implementation of strategic plans. While countries and the GPEI can count many successes, polioviruses still continue to circulate, and in 2022 the US reported a case. This presentation will provide an overview of some of the decision support modeling provided over the last 2 decades, and perspective on what makes modeling impactful (or not) for highly complex global systems.

About Dr. Thompson
Dr. Kimberly M. Thompson's research interests and teaching focus on improving children’s lives and global health by integrating the best available evidence into integrated health risk, economic, and policy models that inform decisions and improve management. While on the faculty at the Harvard School of Public Health, Dr. Thompson created and directed the Harvard Kids Risk Project, which initiated collaborative work with several partners of the Global Polio Eradication Initiative (GPEI) to support polio endgame policy analyses. In late 2008, Dr. Thompson incorporated Kid Risk, Inc. as a self-standing, non-profit organization, which has continued the collaborative work with GPEI partners. In 2014, Dr. Thompson led the U.S. Centers for Disease Control and Prevention (CDC)/Kid Risk, Inc. team that won the Institute for Operations Research and the Management Sciences (INFORMS) Edelman Award.

]]> Andy Haleblian 1 1680888366 2023-04-07 17:26:06 1681991023 2023-04-20 11:43:43 0 0 event In 1988, the World Health Assembly resolved to eradicate poliomyelitis by the year 2000. As of 2023, the job is not done.

]]>
2023-04-21T11:30:00-04:00 2023-04-21T12:30:00-04:00 2023-04-21T12:30:00-04:00 2023-04-21 15:30:00 2023-04-21 16:30:00 2023-04-21 16:30:00 2023-04-21T11:30:00-04:00 2023-04-21T12:30:00-04:00 America/New_York America/New_York datetime 2023-04-21 11:30:00 2023-04-21 12:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building Complex]]> 670477 670477 image <![CDATA[Dr. Karen Thompson]]> image/jpeg 1680888395 2023-04-07 17:26:35 1680888395 2023-04-07 17:26:35 <![CDATA[Download the Event flyer]]>
<![CDATA[ISyE Seminar Speaker - Kimberly M. Thompson]]> 36374 Title:  

Modeling Polio Eradication:  Insights from 2 Decades of Waiting for the Endgame

Abstract:

In 1988, the World Health Assembly resolved to eradication poliomyelitis by the year 2000.  As of 2023, the job is not done.  Since 2000, analytical modeling of the polio end game has provided critical insights to some national and global decision makers.  However, the Global Polio Eradication Initiative (GPEI) partnership has evolved over time, with different perspectives driving the development and implementation of strategic plans.  While countries and the GPEI can count many successes, polioviruses still continue to circulate, and in 2022 the US reported a case.  This presentation will provide an overview of some of the decision support modeling provided over the last 2 decades, and perspective on what makes modeling impactful (or not) for highly complex global systems.

Bio:

 

Dr. Kimberly M. Thompson's research interests and teaching focus on improving children’s lives and global health by integrating the best available evidence into integrated health risk, economic, and policy models that inform decisions and improve management.  While on the faculty at the Harvard School of Public Health, Dr. Thompson created and directed the Harvard Kids Risk Project, which initiated collaborative work with several partners of the Global Polio Eradication Initiative (GPEI) to support polio endgame policy analyses.  In late 2008, Dr. Thompson incorporated Kid Risk, Inc. as a self-standing, non-profit organization, which has continued the collaborative work with GPEI partners.  In 2014, Dr. Thompson led the U.S. Centers for Disease Control and Prevention (CDC)/Kid Risk, Inc. team that won the Institute for Operations Research and the Management Sciences (INFORMS) Edelman Award. 

 

 

]]> mwelch39 1 1681747281 2023-04-17 16:01:21 1681747281 2023-04-17 16:01:21 0 0 event In 1988, the World Health Assembly resolved to eradication poliomyelitis by the year 2000.  As of 2023, the job is not done.  Since 2000, analytical modeling of the polio end game has provided critical insights to some national and global decision makers.  However, the Global Polio Eradication Initiative (GPEI) partnership has evolved over time, with different perspectives driving the development and implementation of strategic plans.  While countries and the GPEI can count many successes, polioviruses still continue to circulate, and in 2022 the US reported a case.  This presentation will provide an overview of some of the decision support modeling provided over the last 2 decades, and perspective on what makes modeling impactful (or not) for highly complex global systems.

]]>
2023-04-21T11:30:00-04:00 2023-04-21T12:30:00-04:00 2023-04-21T12:30:00-04:00 2023-04-21 15:30:00 2023-04-21 16:30:00 2023-04-21 16:30:00 2023-04-21T11:30:00-04:00 2023-04-21T12:30:00-04:00 America/New_York America/New_York datetime 2023-04-21 11:30:00 2023-04-21 12:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[SCL Course: Supply Chain Risk Management (Virtual/Instructor-led)]]> 27233 Course Description

In today’s global economy, operating risks are increasingly on the minds of executives. The specific context of operating risk can range from general areas of business continuity to the effects of natural disasters. In this course participants will gain a solid understanding of Supply Chain Risk Management principals including effective ways to identify, mitigate and measure the impact of potential supply chain disruptions.

Who Should Attend

How You Will Benefit

Upon completion of this course, you will be able to:

What is Covered

]]> Andy Haleblian 1 1657283383 2022-07-08 12:29:43 1681483186 2023-04-14 14:39:46 0 0 event In today’s global economy, operating risks are increasingly on the minds of executives. The specific context of operating risk can range from general areas of business continuity to the effects of natural disasters. In this course participants will gain a solid understanding of Supply Chain Risk Management principals including effective ways to identify, mitigate and measure the impact of potential supply chain disruptions.

]]>
2023-04-26T08:00:00-04:00 2023-04-27T13:00:00-04:00 2023-04-27T13:00:00-04:00 2023-04-26 12:00:00 2023-04-27 17:00:00 2023-04-27 17:00:00 2023-04-26T08:00:00-04:00 2023-04-27T13:00:00-04:00 America/New_York America/New_York datetime 2023-04-26 08:00:00 2023-04-27 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]> <![CDATA[Course registration page]]> <![CDATA[Supply & Demand Planning Certificate Course Series Flyer]]>
<![CDATA[Professional Education Course: Responsive Supply Chain Design and Operations]]> 27233 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm ET each day.

Course Description

Meeting demand in a timely and cost-effective manner is important both in public and private supply chains, and heavily depend on the design and operation of these supply chains. Demand is affected by ongoing factors such as local economy, infrastructure, and geographic location, as well as unexpected events such as natural or manmade disasters or other large-scale disruptions. Designing and operating responsive supply chains requires the consideration of uncertainty in timing, scope, scale, and understanding of various topics such as forecasting, distribution network design, and inventory management. This course will examine methods and models for making supply chain design and operational decisions and explore the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for meeting the need of customers and beneficiaries.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

About the Course and the HHSCM Course Series

This course is the first in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://hhls.scl.gatech.edu/ by February 19, 2023.  

Questions? Reach out to chhs@gatech.edu!

]]> Andy Haleblian 1 1663883682 2022-09-22 21:54:42 1681482735 2023-04-14 14:32:15 0 0 event This course examines methods and models for making pre-planning decisions and explores the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for sustaining wellness.

]]>
2023-04-03T09:30:00-04:00 2023-04-06T13:00:00-04:00 2023-04-06T13:00:00-04:00 2023-04-03 13:30:00 2023-04-06 17:00:00 2023-04-06 17:00:00 2023-04-03T09:30:00-04:00 2023-04-06T13:00:00-04:00 America/New_York America/New_York datetime 2023-04-03 09:30:00 2023-04-06 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu

]]>
<![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Course Details via Center for Health and Humanitarian Systems website]]> <![CDATA[Health & Humanitarian Supply Chain Management Certificate]]> <![CDATA[Apply for a Scholarship!]]>
<![CDATA[Professional Education Course: Inventory Management and Resource Allocation in Supply Chains]]> 27233 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm EDT each day.

Course Description

Many Supply Chain decisions are concerned with the timely and efficient procurement, allocation, and distribution of resources (e.g. funds, supplies, volunteers, money, employees) through a supply chain network. This course will explore methodologies for “medium term” decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and supply chain design.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

About the Course and the HHSCM Course Series

This course is the second in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://hhls.scl.gatech.edu/ by February 19, 2023.  

Questions? Reach out to chhs@gatech.edu!

]]> Andy Haleblian 1 1663883824 2022-09-22 21:57:04 1681482726 2023-04-14 14:32:06 0 0 event This course explores methodologies for tactical decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and transportation decisions.

]]>
2023-04-17T09:30:00-04:00 2023-04-20T13:00:00-04:00 2023-04-20T13:00:00-04:00 2023-04-17 13:30:00 2023-04-20 17:00:00 2023-04-20 17:00:00 2023-04-17T09:30:00-04:00 2023-04-20T13:00:00-04:00 America/New_York America/New_York datetime 2023-04-17 09:30:00 2023-04-20 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu 

]]>
<![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Course Details via Center for Health and Humanitarian Systems website]]> <![CDATA[Health & Humanitarian Supply Chain Management Certificate]]> <![CDATA[Apply for a Scholarship!]]>
<![CDATA[Professional Education Course: Systems Operations and Strategic Interactions in Supply Chains]]> 27233 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm EDT each day. 

Course Description

Often the lack of cooperation and coordination between organizations or stakeholders lead to inefficiencies, despite having common goals. A systems view is needed to ensure appropriate use of scarce resources to meet the multiple, and often conflicting, short- and long-term goals from multiple constituents. This course will focus on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

About the Course and the HHSCM Course Series

This course is the first in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://hhls.scl.gatech.edu/ by February 19, 2023.  

Questions? Reach out to chhs@gatech.edu!

]]> Andy Haleblian 1 1663884800 2022-09-22 22:13:20 1681482715 2023-04-14 14:31:55 0 0 event This course focuses on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.

]]>
2023-04-24T09:30:00-04:00 2023-04-27T13:00:00-04:00 2023-04-27T13:00:00-04:00 2023-04-24 13:30:00 2023-04-27 17:00:00 2023-04-27 17:00:00 2023-04-24T09:30:00-04:00 2023-04-27T13:00:00-04:00 America/New_York America/New_York datetime 2023-04-24 09:30:00 2023-04-27 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu

]]>
<![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Course Details via Center for Health and Humanitarian Systems website]]> <![CDATA[Health & Humanitarian Supply Chain Management Certificate]]> <![CDATA[Apply for a Scholarship!]]>
<![CDATA[ISyE Seminar Speaker - Prof. Fabio Sgarbossa - Advancements in Logistics: The Experience of Logistics 4.0 Lab at NTNU]]> 36458 Speaker: Prof. Fabio Sgarbossa

Short Bio: Fabio Sgarbossa is Full Professor of Industrial Logistics, leader of the Production Management and Logistics Group at Norwegian University of Science and Technology. He is also responsible of the Logistics 4.0 Lab the Norway's first logistics laboratory that merges digital technologies with traditional P&L systems, enabling researchers, practitioners, engineers, pioneers, students, and other enthusiasts to come together and collaborate on common ground. His research projects focus on traditional topics as design and management of industrial logistics systems, technological innovation, material handling and warehousing, but also new advanced one, as industry 4.0 and 5.0, human centric design, additive manufacturing, hydrogen supply chain. He has published over 150 publications in relevant international journals, and he is Associate Editor for International Journal of Production Research and member of several editorial boards.

 

]]> mellis74 1 1681481385 2023-04-14 14:09:45 1681482079 2023-04-14 14:21:19 0 0 event Abstract Seminar: Advancements in Logistics: the experience of Logistics 4.0 Lab at NTNU

Logistics 4.0 laboratory is the Norway's first logistics laboratory that merges digital technologies with traditional production and logistics systems, enabling researchers, practitioners, engineers, pioneers, students, and other enthusiasts to come together and collaborate on common ground. Since its establishment, researchers have been carrying out projects in collaboration with partners from industrial and public sectors developing new solutions for making production and logistics systems smarter, greener, more human-centric, more resilient.

In this seminar, Prof. Fabio Sgarbossa will present the research activities carried out in some projects, like SmartLIB: Smart Logistics in Library Sector, H2GLASS: Hydrogen Supply Chain, and other ones.

 

]]>
2023-04-25T11:00:00-04:00 2023-04-25T12:00:00-04:00 2023-04-25T12:00:00-04:00 2023-04-25 15:00:00 2023-04-25 16:00:00 2023-04-25 16:00:00 2023-04-25T11:00:00-04:00 2023-04-25T12:00:00-04:00 America/New_York America/New_York datetime 2023-04-25 11:00:00 2023-04-25 12:00:00 America/New_York America/New_York datetime <![CDATA[]]> 670543 670543 image <![CDATA[Fabio Sgarbossa]]> image/jpeg 1681481410 2023-04-14 14:10:10 1681481410 2023-04-14 14:10:10
<![CDATA[SCL Course: Essentials of Negotiations and Stakeholder Influence (Virtual/Instructor-led)]]> 27233 Course Description

Essentials of Negotiations and Stakeholder Influence level-sets the participants' understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style “DNA” to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a “no judgement zone” environment.

Who Should Attend

This course is ideal for sourcing initiative leaders, project leaders, business unit leaders, operations managers, sales leaders and procurement & supply management-related professionals who are involved with supplier selection, contract development and supplier performance management.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1667831363 2022-11-07 14:29:23 1681476812 2023-04-14 12:53:32 0 0 event This course level-sets the participants' understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style “DNA” to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a “no judgement zone” environment.

]]>
2023-04-06T13:00:00-04:00 2023-04-17T17:00:00-04:00 2023-04-17T17:00:00-04:00 2023-04-06 17:00:00 2023-04-17 21:00:00 2023-04-17 21:00:00 2023-04-06T13:00:00-04:00 2023-04-17T17:00:00-04:00 America/New_York America/New_York datetime 2023-04-06 01:00:00 2023-04-17 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Transforming Supply Chain Management and Performance Analysis (Virtual/Instructor-led)]]> 27233 Course Description

This course is the first in the four-course Supply Chain Analytics Professional certificate program. It prepares you to apply leading-edge analytical methods and technology enablers across the supply chain. You’ll learn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you’ll learn about data cleansing, exploratory data analysis, and visualization. You’ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.

The online version of the course is comprised of (4) half-day instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1657282949 2022-07-08 12:22:29 1681476584 2023-04-14 12:49:44 0 0 event Learn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you’ll learn about data cleansing, exploratory data analysis, and visualization. You’ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.

]]>
2023-05-15T13:00:00-04:00 2023-05-18T17:00:00-04:00 2023-05-18T17:00:00-04:00 2023-05-15 17:00:00 2023-05-18 21:00:00 2023-05-18 21:00:00 2023-05-15T13:00:00-04:00 2023-05-18T17:00:00-04:00 America/New_York America/New_York datetime 2023-05-15 01:00:00 2023-05-18 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

]]>
<![CDATA[Course webpage within the SCL website]]>
<![CDATA[ISyE Seminar Speaker - Tony Cai]]> 36374 Title:

Optimal Statistical Estimation under Nonstatistical Constraints

Abstract:

In the conventional statistical framework, a major goal is to develop optimal statistical procedures based on the sample size and statistical model. However, in many contemporary applications, non-statistical concerns such as privacy and communication constraints associated with the statistical procedures become crucial. This raises a fundamental question in data science: how can we make optimal statistical inference under these non-statistical constraints?

In this talk, we explore recent advances in differentially private learning and distributed learning under communication constraints in a few specific settings. Our results demonstrate novel and interesting phenomena and suggest directions for further investigation.

Bio:

Education:

Academic Appointments:

Administrative Appointment:

Editorial Appointments:

Honors & Awards:

Research Interests:

Publications: Papers can be downloaded here.

Professional Society Membership:

 

]]> mwelch39 1 1681138238 2023-04-10 14:50:38 1681138238 2023-04-10 14:50:38 0 0 event In the conventional statistical framework, a major goal is to develop optimal statistical procedures based on the sample size and statistical model. However, in many contemporary applications, non-statistical concerns such as privacy and communication constraints associated with the statistical procedures become crucial. This raises a fundamental question in data science: how can we make optimal statistical inference under these non-statistical constraints?

In this talk, we explore recent advances in differentially private learning and distributed learning under communication constraints in a few specific settings. Our results demonstrate novel and interesting phenomena and suggest directions for further investigation.


 

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2023-04-14T11:30:00-04:00 2023-04-14T12:30:00-04:00 2023-04-14T12:30:00-04:00 2023-04-14 15:30:00 2023-04-14 16:30:00 2023-04-14 16:30:00 2023-04-14T11:30:00-04:00 2023-04-14T12:30:00-04:00 America/New_York America/New_York datetime 2023-04-14 11:30:00 2023-04-14 12:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[ISyE Statistics Seminar - Tracy Ke]]> 36358 Abstract: Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. In this talk, I will review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, I will introduce Topic-SCORE (Ke and Wang, 2022), a statistical approach to topic modeling, and discuss how to use it to analyze MADStat - a dataset on statistical publications that we collected and cleaned on our own. The application of Topic-SCORE and other methods on MADStat leads to interesting findings. For example, 11 representative topics in statis- tics are identified. For each journal, the evolution of topic weights over time can be visualized, and these results are used to analyze the trends in statistical research. In particular, we propose a new statistical model for ranking the citation impacts of 11 topics, and we also build a cross- topic citation graph to illustrate how research results on different topics spread to one another.

]]> chumphrey30 1 1681132968 2023-04-10 13:22:48 1681134594 2023-04-10 13:49:54 0 0 event Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. In this talk, I will review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, I will introduce Topic-SCORE (Ke and Wang, 2022), a statistical approach to topic modeling, and discuss how to use it to analyze MADStat - a dataset on statistical publications that we collected and cleaned on our own.

]]>
2023-04-18T11:00:00-04:00 2023-04-18T12:00:00-04:00 2023-04-18T12:00:00-04:00 2023-04-18 15:00:00 2023-04-18 16:00:00 2023-04-18 16:00:00 2023-04-18T11:00:00-04:00 2023-04-18T12:00:00-04:00 America/New_York America/New_York datetime 2023-04-18 11:00:00 2023-04-18 12:00:00 America/New_York America/New_York datetime <![CDATA[]]> 670482 670482 image <![CDATA[4.18.2023.Tracy_.Ke_.PNG]]> image/png 1681133898 2023-04-10 13:38:18 1681133898 2023-04-10 13:38:18
<![CDATA[SCL IRC Seminar: Supply Chain and Logistics Innovation Showcase]]> 27233 The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested faculty, students and corporate partners as well as the general public. If you are interested in attending any of the sessions, please review the below information and register online.

SESSION OVERVIEW

We will hear from two seasoned founders of supply chain organizations who navigated the ups and downs of building a company from scratch. They will share their personal experiences and insights into the challenges and successes and their early experiences of being part of the Advanced Technology Development Center (ATDC).

SESSION SPEAKERS

Barbara Jones of Freeing Returns and Michael Malakhov of Carpool Logistics (Panelists). Alex Rhodeen, Supply Chain Catalyst, ATDC (Moderator)

Register Online for upcoming SCL IRC seminars

In-person attendance to our SCL IRC sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program. If you are a member of the general public attending in-person and wish to order lunch, the cost to attend is $5 per session. Virtual attendance is always free*.

*Please see our registration page relating to taking advantage of the optional in-person lunch.

If you have any questions, please email event@scl.gatech.edu.

]]> Andy Haleblian 1 1674860127 2023-01-27 22:55:27 1680202967 2023-03-30 19:02:47 0 0 event The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested SCL faculty, students and corporate partners as well as the general public.

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2023-04-20T13:00:00-04:00 2023-04-20T14:30:00-04:00 2023-04-20T14:30:00-04:00 2023-04-20 17:00:00 2023-04-20 18:30:00 2023-04-20 18:30:00 2023-04-20T13:00:00-04:00 2023-04-20T14:30:00-04:00 America/New_York America/New_York datetime 2023-04-20 01:00:00 2023-04-20 02:30:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

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670380 670380 image <![CDATA[GTSCL-SCLIRC20230416_16by9.jpg]]> image/jpeg 1680202688 2023-03-30 18:58:08 1680202688 2023-03-30 18:58:08 <![CDATA[Register Online for upcoming SCLIRC seminars]]>
<![CDATA[ISyE Seminar Speaker - Daniel Bienstock]]> 36374 Title:

Solving ACOPF Problems

 

Abstract: 

The ACOPF problem concerns the optimal selection of an operating point for an electrical power grid.  Even though the problem was initially formulated in the early 1960s, it is now gaining increased prominence due to impending changes in the electrical power delivery, which are placing increased pressure on simple approximations now used in operations.  We will begin the talk by describing our participation in an ongoing and previous instances of an ARPA-E-run competition on ACOPF, which uses modern and highly extended (and realistic) formulations for the problem, together with very large-size and realistic data sets with demanding computational requirements.  We will then explore challenges in implementing robust and fast ACOPF solvers using standardized software (the meaning for this statement will be made clear in the talk).  Finally we will conclude with some observations on purely linear formulations for ACOPF.  This is joint work with several authors.

 

Bio:

Daniel Bienstock is Liu Family Professor of Operations Research, with joint appointments in Applied Math and Electrical Engineering, at Columbia University.  His work focuses on high-performance algorithms for nonconvex optimization, with focus on large-scale cases; and with an additional focus on problems arising in engineering.  He became an Informs Fellow in 2013 and received the Khachiyan Prize in Optimization in 2022.  He received a PhD from MIT in Operations Research.

]]> mwelch39 1 1679592035 2023-03-23 17:20:35 1679605029 2023-03-23 20:57:09 0 0 event The ACOPF problem concerns the optimal selection of an operating point for an electrical power grid.  Even though the problem was initially formulated in the early 1960s, it is now gaining increased prominence due to impending changes in the electrical power delivery, which are placing increased pressure on simple approximations now used in operations.  We will begin the talk by describing our participation in an ongoing and previous instances of an ARPA-E-run competition on ACOPF, which uses modern and highly extended (and realistic) formulations for the problem, together with very large-size and realistic data sets with demanding computational requirements.  We will then explore challenges in implementing robust and fast ACOPF solvers using standardized software (the meaning for this statement will be made clear in the talk).  Finally we will conclude with some observations on purely linear formulations for ACOPF.  This is joint work with several authors.

]]>
2023-03-31T11:30:00-04:00 2023-03-31T12:30:00-04:00 2023-03-31T12:30:00-04:00 2023-03-31 15:30:00 2023-03-31 16:30:00 2023-03-31 16:30:00 2023-03-31T11:30:00-04:00 2023-03-31T12:30:00-04:00 America/New_York America/New_York datetime 2023-03-31 11:30:00 2023-03-31 12:30:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar Speaker - Shane Henderson]]> 36374 Title:

Modeling the Impact of Community First Responders

Body: 

Patient survival from out-of-hospital cardiac arrest (OHCA) can be improved by augmenting traditional ambulance response with the dispatch of community first responders (volunteers) who are alerted via an app. How many volunteers are needed, from where should volunteers be recruited, and how should they be dispatched? We use a combination of Poisson point process modeling and convex optimization to address the first two questions; the right areas from which to recruit are not always obvious, because volunteers recruited from one area may spend time in various areas across a city. We use a combination of dynamic programming and decision trees to answer the last question, balancing the goal of a fast response to the current patient with the need to avoid disengagement of volunteers that arises when multiple volunteers respond. A case study for Auckland, New Zealand demonstrates the ideas.

 This is joint work with Pieter van den Berg, Océane Fourmentraux, Caroline Jagtenberg, and Hemeng (Maggie) Li

Bio:

Professor Shane G. Henderson holds the Charles W. Lake, Jr. Chair in Productivity in the School of Operations Research and Information Engineering (ORIE) at Cornell University. His research interests include discrete-event simulation, simulation optimization, emergency services planning and transportation. He is the editor in chief of the open-access journal Stochastic Systems. He is an INFORMS Fellow and a co-recipient of the INFORMS Wagner Prize for his work on bike-sharing programs. He has served as Director of the School of ORIE, as chair of the INFORMS Applied Probability Society, and as simulation area editor for Operations Research. He has previously held positions in the Department of Industrial and Operations Engineering at the University of Michigan and the Department of Engineering Science at the University of Auckland. He likes cats, climbing walls, biking, Harry Potter and being a Dad.

]]> mwelch39 1 1679592847 2023-03-23 17:34:07 1679605013 2023-03-23 20:56:53 0 0 event Patient survival from out-of-hospital cardiac arrest (OHCA) can be improved by augmenting traditional ambulance response with the dispatch of community first responders (volunteers) who are alerted via an app. How many volunteers are needed, from where should volunteers be recruited, and how should they be dispatched? We use a combination of Poisson point process modeling and convex optimization to address the first two questions; the right areas from which to recruit are not always obvious, because volunteers recruited from one area may spend time in various areas across a city. We use a combination of dynamic programming and decision trees to answer the last question, balancing the goal of a fast response to the current patient with the need to avoid disengagement of volunteers that arises when multiple volunteers respond. A case study for Auckland, New Zealand demonstrates the ideas.

 

This is joint work with Pieter van den Berg, Océane Fourmentraux, Caroline Jagtenberg, and Hemeng (Maggie) Li

]]>
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<![CDATA[ IEN Soft Lithography Short Course]]> 34760

The Institute for Electronics and Nanotechnology (IEN) at Georgia Tech will offer a short course on Soft Lithography for Microfluidics on April 13 - 14, 2023. This course module is designed for individuals interested in hands-on training in the fabrication of microfluidic devices using the soft lithography technique. This two-day intensive short course will be structured to assume no prior knowledge of the technologies by the participants. The course agenda is evenly divided between laboratory hands-on sessions, including SU-8 master mold creation using photolithography, and PDMS device fabrication in the IEN cleanroom, and supporting lectures.  The goal of this course is to impart a basic understanding of soft lithography for microfluidic applications as practiced in academia and industry.

Spring 2023 Rates

Georgia Tech attendee - $150
Academic or government attendee - $300
Industry attendee - $600

* Lunch and break refreshments are included

Learn more about the course and register.
]]> Laurie Haigh 1 1679080614 2023-03-17 19:16:54 1679080938 2023-03-17 19:22:18 0 0 event The Institute for Electronics and Nanotechnology (IEN) at Georgia Tech will offer a short course on Soft Lithography for Microfluidics on April 13 - 14, 2023.

]]>
2023-04-13T09:00:00-04:00 2023-04-13T17:00:00-04:00 2023-04-13T17:00:00-04:00 2023-04-13 13:00:00 2023-04-13 21:00:00 2023-04-13 21:00:00 2023-04-13T09:00:00-04:00 2023-04-13T17:00:00-04:00 America/New_York America/New_York datetime 2023-04-13 09:00:00 2023-04-13 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> Paul Joseph
Principal Research Scientist
Institute for Electronics and Nanotechnology
paul.joseph@ien.gatech.edu
404.894.3360

]]>
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(Note: This download will not currently work on windows outlook. We are working to correct the issue.)

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<![CDATA[ISyE Seminar- Ralph Smith]]> 36358 Abstract: Engineering and biological models generally have a number of parameters which are nonidentifiable in the sense that they are not uniquely determined by measured responses.  Furthermore, the computational cost of high-fidelity simulation codes often precludes their direct use for Bayesian model calibration and uncertainty propagation.  In this presentation, we will discuss techniques to isolate influential parameters for subsequent surrogate model construction, Bayesian inference and uncertainty propagation.  For parameter selection, we will discuss advantages and shortcomings of global sensitivity analysis to isolate influential inputs and detail the use of parameter subset selection and active subspace techniques as an alternative.  We will also discuss the manner in which Bayesian calibration on active subspaces can be used to quantify uncertainties in physical parameters.  These techniques will be illustrated for models arising in nuclear power plant design and quantitative systems pharmacology (QSP), as well as models for transductive materials.

 

 

Biography: Ralph C. Smith joined the North Carolina State University faculty in 1998 where he is presently a Distinguished University Professor of Mathematics.  He is co-author of the research monograph Smart Material Structures: Modeling, Estimation and Control and author of the books Smart Material Systems: Model Development and Uncertainty Quantification: Theory, Implementation, and Applications.  He is on the editorial boards of the Journal of Intelligent Material Systems and Structures and the SIAM/ASA Journal on Uncertainty Quantification. He is the recipient of the 2016 ASME Adaptive Structures and Material Systems Prize and the SPIE 2017 Smart Structures and Materials Lifetime Achievement, and he was named a SIAM Fellow in 2018 and an ASME Fellow in 2022. His research areas include mathematical modeling of smart material systems, numerical analysis and methods for physical systems, Bayesian model calibration, sensitivity analysis, control, and uncertainty quantification for physical and biological systems.

]]> chumphrey30 1 1678302615 2023-03-08 19:10:15 1678371790 2023-03-09 14:23:10 0 0 event 2023-03-27T12:00:00-04:00 2023-03-27T13:15:00-04:00 2023-03-27T13:15:00-04:00 2023-03-27 16:00:00 2023-03-27 17:15:00 2023-03-27 17:15:00 2023-03-27T12:00:00-04:00 2023-03-27T13:15:00-04:00 America/New_York America/New_York datetime 2023-03-27 12:00:00 2023-03-27 01:15:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Statistics Seminar- Zhuoran Yang]]> 36358 Abstract:

We study offline reinforcement learning under a novel model called strategic MDP, which characterizes the strategic interactions between a principal and a sequence of myopic agents with private types. Due to the bilevel structure and private types, strategic MDP involves information asymmetry between the principal and the agents. We focus on the offline RL problem, where the goal is to learn the optimal policy of the principal concerning a target population of agents based on a pre-collected dataset that consists of historical interactions. The unobserved private types confound such a dataset as they affect both the rewards and observations received by the principal. We propose a novel algorithm, Pessimistic policy Learning with Algorithmic iNstruments (PLAN), which leverages the ideas of instrumental variable regression and the pessimism principle to learn a near-optimal principal's policy in the context of general function approximation. Our algorithm is based on the critical observation that the principal's actions serve as valid instrumental variables. In particular, under a partial coverage assumption on the offline dataset, we prove that PLAN outputs a nearly optimal policy at a root-N statistical rate, where N is the number of trajectories. We further apply our framework to some special cases of strategic MDP, including strategic regression, strategic bandit, and noncompliance in recommendation systems. This is joint work with Mengxin Yu and Jianqing Fan.

 

Bio:

Zhuoran Yang is an Assistant Professor of Statistics and Data Science at Yale University, starting in July 2022. His research interests lie in the interface between machine learning, statistics, and optimization. He is particularly interested in the foundations of reinforcement learning, representation learning, and deep learning. Before joining Yale, Zhuoran worked as a postdoctoral researcher at the University of California, Berkeley, advised by Michael. I. Jordan. Prior to that, he obtained his Ph.D. from the Department of Operations Research and Financial Engineering at Princeton University, co-advised by Jianqing Fan and Han Liu. He received his bachelor’s degree in Mathematics from Tsinghua University in 2015.

]]> chumphrey30 1 1678302925 2023-03-08 19:15:25 1678302993 2023-03-08 19:16:33 0 0 event 2023-03-15T15:11:00-04:00 2023-03-15T15:11:00-04:00 2023-03-15T15:11:00-04:00 2023-03-15 19:11:00 2023-03-15 19:11:00 2023-03-15 19:11:00 2023-03-15T15:11:00-04:00 2023-03-15T15:11:00-04:00 America/New_York America/New_York datetime 2023-03-15 03:11:00 2023-03-15 03:11:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[Leadership Development Seminar/Webinar Series: Featuring Josh Roberts]]> 27233 Josh Roberts is an energetic, results-oriented healthcare executive with over 20 years of industry and management consulting experience leading and managing large-scale enterprise transformation. He has demonstrated the ability to develop and implement innovative and sustainable business solutions while cultivating sponsorship and advocacy across all organizational levels. He has deep experience relative to hospital operations, physician practice management/alignment, and healthcare strategy formulation/execution.

Currently, Mr. Roberts serves as the Chief Operating Officer (COO) for Piedmont Atlanta Hospital. He has a passion for improving healthcare delivery. He feels a sense of purpose from helping people solve problems and gets his energy from teaching and coaching others.

Mr. Roberts received his MBA from the University of Georgia and his B.S. degree from the Georgia Institute of Technology in Industrial Engineering.

We look forward to having you attend the event in person or online!

Zoom Meeting
https://gatech.zoom.us/j/93695197551?pwd=ZXVqRW84L0htVHQ4K0pIUnlTKzhnQT09
Meeting ID: 936 9519 7551
Passcode: 432854

]]> Andy Haleblian 1 1678205330 2023-03-07 16:08:50 1678205535 2023-03-07 16:12:15 0 0 event 2023-02-16T16:30:00-05:00 2023-02-16T17:00:00-05:00 2023-02-16T17:00:00-05:00 2023-02-16 21:30:00 2023-02-16 22:00:00 2023-02-16 22:00:00 2023-02-16T16:30:00-05:00 2023-02-16T17:00:00-05:00 America/New_York America/New_York datetime 2023-02-16 04:30:00 2023-02-16 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu

]]>
666492 666492 image <![CDATA[Josh Roberts, Chief Operating Officer (COO), Piedmont Atlanta Hospital]]> image/jpeg 1678205309 2023-03-07 16:08:29 1678205309 2023-03-07 16:08:29
<![CDATA[Leadership Development Seminar/Webinar Series: Featuring Ron Nash]]> 27233 Ron Nash is the Managing Director of Nash Technology Group and the author of “The Making of a Business Leader.” Previously, he served as Sr. Advisor for Transformation and Reform for the US Department of Defense. He was the CEO and Board Member at Pivot3, a leader in the hyper-converged infrastructure segment.

Ron has successfully developed and grown multiple companies in the broader technology industry. As a Partner at InterWest Partners, he invested in new technology start-up companies and served as a director to help chart their growth and increase their market value. He led multiple companies as their top executive through challenging turnaround periods to business success. His executive experience spans the range from small start-up companies to multi-billion dollar global corporations.

Ron received his M.S. from The University of Texas at Dallas in Management and his B.S. degree from the Georgia Institute of Technology in Industrial Engineering.

We look forward to seeing you at the seminar/webinar!

Join Zoom Meeting
https://gatech.zoom.us/j/99337158510?pwd=Y3pFRk1EZTJaV28zMWxDbmwra0syZz09
Meeting ID: 993 3715 8510
Passcode: 780822

]]> Andy Haleblian 1 1677548876 2023-02-28 01:47:56 1677788132 2023-03-02 20:15:32 0 0 event 2023-03-09T16:30:00-05:00 2023-03-09T17:00:00-05:00 2023-03-09T17:00:00-05:00 2023-03-09 21:30:00 2023-03-09 22:00:00 2023-03-09 22:00:00 2023-03-09T16:30:00-05:00 2023-03-09T17:00:00-05:00 America/New_York America/New_York datetime 2023-03-09 04:30:00 2023-03-09 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu

]]>
666221 666221 image <![CDATA[Ron Nash, Managing Director, Nash Technology Group]]> image/jpeg 1677548617 2023-02-28 01:43:37 1677548617 2023-02-28 01:43:37
<![CDATA[ISyE Seminar - Alyssa Kody]]> 34977 Title:

Wildfire Risk Mitigation and Data-Driven Methods for Electric Power Systems
 

Abstract:

The electric power grid of the future faces many challenges, including rapidly increasing quantities of renewable generation and growing threats from extreme weather events, which necessitate the development of new computational tools. The first part of this talk will focus on one extreme weather event: elevated wildfire ignition risk. Wildfire risk mitigation is a critical consideration in regions like the western United States, where, historically, electric power systems have ignited some of the most destructive wildfires. To reduce the risk of igniting a wildfire, power system operators preemptively de-energize high-risk power lines during extreme wildfire conditions as part of “Public Safety Power Shutoff” (PSPS) events. However, PSPS events can also result in significant amounts of load shedding, leading to the need for new operation and planning decision-making algorithms for power systems experiencing high wildfire risk. The second part of the talk will focus on the specialized and targeted use of data-driven methods to increase the accuracy and computation speeds of power systems decision-making algorithms. The talk will conclude by outlining opportunities for the use of targeted data-driven methods to aid in wildfire risk mitigation algorithms, and for resilience decision-making for power systems in general.
 

Bio:

Alyssa Kody is a Maria Goeppert Mayer Postdoctoral Fellow in the Energy Systems Division at Argonne National Laboratory in Lemont, Illinois. Her research focuses on developing control and optimization algorithms for power and energy systems. She was recently named a 2022 Rising Star in EECS. She received her Ph.D. in Electrical Engineering from the University of Michigan in Ann Arbor in 2019, where her thesis was on developing control systems for self-powered technologies. Her graduate work was supported by a National Science Foundation Graduate Research Fellowship and a Rackham Merit Fellowship.

]]> Julie Smith 1 1676985610 2023-02-21 13:20:10 1676985610 2023-02-21 13:20:10 0 0 event Abstract:

The electric power grid of the future faces many challenges, including rapidly increasing quantities of renewable generation and growing threats from extreme weather events, which necessitate the development of new computational tools. The first part of this talk will focus on one extreme weather event: elevated wildfire ignition risk. Wildfire risk mitigation is a critical consideration in regions like the western United States, where, historically, electric power systems have ignited some of the most destructive wildfires. To reduce the risk of igniting a wildfire, power system operators preemptively de-energize high-risk power lines during extreme wildfire conditions as part of “Public Safety Power Shutoff” (PSPS) events. However, PSPS events can also result in significant amounts of load shedding, leading to the need for new operation and planning decision-making algorithms for power systems experiencing high wildfire risk. The second part of the talk will focus on the specialized and targeted use of data-driven methods to increase the accuracy and computation speeds of power systems decision-making algorithms. The talk will conclude by outlining opportunities for the use of targeted data-driven methods to aid in wildfire risk mitigation algorithms, and for resilience decision-making for power systems in general.

 

]]>
2023-03-06T12:00:00-05:00 2023-03-06T13:00:00-05:00 2023-03-06T13:00:00-05:00 2023-03-06 17:00:00 2023-03-06 18:00:00 2023-03-06 18:00:00 2023-03-06T12:00:00-05:00 2023-03-06T13:00:00-05:00 America/New_York America/New_York datetime 2023-03-06 12:00:00 2023-03-06 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE - Building ]]>
<![CDATA[ISyE Stats Seminar- Andrew Brown]]> 36358 Bio: Andrew Brown holds a BS in Applied Mathematics from Georgia Tech and earned his MS and PhD in Statistics from the University of Georgia under the guidance of Gauri Datta and Nicole Lazar. He subsequently took a faculty position in the School of Mathematical and Statistical Sciences at Clemson University, where he is now an Associate Professor. His primary research interests are in uncertainty quantification / computer experiments, Bayesian computation, and neuroimaging data analysis. This is in addition to some interdisciplinary work he has been involved with, including seroprevalence mapping in parasitology, group testing, engineering design, and risk assessment. He was a visiting research fellow at SAMSI for the program on Challenges in Computational Neuroscience, and has served as elected treasurer of the Industrial Statistics section of ISBA, secretary for the UQ interest group of the ASA, and President of the South Carolina chapter of the ASA. His work has been supported by the National Science Foundation and the Department of Education.

 

Abstract: Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g, gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease.

]]> chumphrey30 1 1676561602 2023-02-16 15:33:22 1676561602 2023-02-16 15:33:22 0 0 event Bio: Andrew Brown holds a BS in Applied Mathematics from Georgia Tech and earned his MS and PhD in Statistics from the University of Georgia under the guidance of Gauri Datta and Nicole Lazar. He subsequently took a faculty position in the School of Mathematical and Statistical Sciences at Clemson University, where he is now an Associate Professor. His primary research interests are in uncertainty quantification / computer experiments, Bayesian computation, and neuroimaging data analysis. This is in addition to some interdisciplinary work he has been involved with, including seroprevalence mapping in parasitology, group testing, engineering design, and risk assessment. He was a visiting research fellow at SAMSI for the program on Challenges in Computational Neuroscience, and has served as elected treasurer of the Industrial Statistics section of ISBA, secretary for the UQ interest group of the ASA, and President of the South Carolina chapter of the ASA. His work has been supported by the National Science Foundation and the Department of Education.

 

Abstract: Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g, gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease.

]]>
2023-04-12T15:00:00-04:00 2023-04-12T16:00:00-04:00 2023-04-12T16:00:00-04:00 2023-04-12 19:00:00 2023-04-12 20:00:00 2023-04-12 20:00:00 2023-04-12T15:00:00-04:00 2023-04-12T16:00:00-04:00 America/New_York America/New_York datetime 2023-04-12 03:00:00 2023-04-12 04:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar - Dr. Brandon Pitts]]> 36374 Title:

The era of increasingly intelligent technologies:

Insights into human-automation interaction schemes across different environments, contexts, and tasks

 

Abstract:

Automation and artificial intelligence (AI) have begun to penetrate every area of human life including work, transportation, healthcare, and leisure environments. These advancements promise many benefits, such as improving public safety, providing convenience, extending human abilities, and enabling mobility. However, as automation and AI become increasingly integrated into our daily activities, there are several societal challenges and unanswered research questions that must be addressed related to the roles and responsibilities of humans within human-automation systems, the impact of automation on human behavior and performance, and the perception of (potential) users regarding the utility and usability of intelligent systems. This presentation will discuss a series of research projects aimed at developing and/or evaluating various autonomous systems in different knowledge work environments. Studies involve: 1) predictions of situation awareness using physiological sensing in automated vehicles, 2) assessments of workload and performance with increasing levels of automation support for X-ray screening tasks, 3) evaluations of automatic speech-to-text tools for communicating weather information in aviation, and 4) evaluations of a fully autonomous, universally-designed vehicle prototype for people with travel-limiting disabilities. Findings from this research are expected to contribute to the broader discussion on how to thoughtfully design collaborative human-automation systems that successfully leverage the unique strengths and capabilities of each acting agent.

 

Bio:

Dr. Brandon J. Pitts is an Assistant Professor in the School of Industrial Engineering at Purdue University, West Lafayette, IN. At Purdue, he is also Director of the Next-generation Human-systems and Cognitive Engineering (NHanCE) Lab, Faculty Associate with the Center on Aging and the Life Course (CALC), and Co-Director of the Federal Aviation Administration (FAA) Center of Excellence for Technical Training and Human Performance (TTHP). His research interests include human factors, cognitive engineering, human-automation interaction, cyber-physical-human systems, interface design, gerontechnology, and inclusive design in complex transportation and work environments, such as driving and aviation. In 2022, Dr. Pitts’ co-led team, EASI RIDER, was named the 1st place winner of the U.S. Department of Transportation (DOT) Inclusive Design Challenge (IDC) for their life-size autonomous vehicle solution that seeks to enable independent and seamless travel for individuals with disabilities. His research has been funded by sponsors such as the National Science Foundation (NSF), DOT, FAA, and Ford Motor Company. Dr. Pitts completed a B.S. in Industrial Engineering at Louisiana State University in 2010, and a M.S.E and Ph.D. in Industrial and Operations Engineering at the University of Michigan (UM), Ann Arbor, MI in 2013 and 2016, respectively. Prior to his faculty appointment, he was a Research Fellow in the UM Center for Healthcare Engineering and Patient Safety (CHEPS). He is also a registered Engineer Intern (E.I.T).

]]> mwelch39 1 1676560014 2023-02-16 15:06:54 1676560014 2023-02-16 15:06:54 0 0 event Automation and artificial intelligence (AI) have begun to penetrate every area of human life including work, transportation, healthcare, and leisure environments. These advancements promise many benefits, such as improving public safety, providing convenience, extending human abilities, and enabling mobility. However, as automation and AI become increasingly integrated into our daily activities, there are several societal challenges and unanswered research questions that must be addressed related to the roles and responsibilities of humans within human-automation systems, the impact of automation on human behavior and performance, and the perception of (potential) users regarding the utility and usability of intelligent systems. This presentation will discuss a series of research projects aimed at developing and/or evaluating various autonomous systems in different knowledge work environments. Studies involve: 1) predictions of situation awareness using physiological sensing in automated vehicles, 2) assessments of workload and performance with increasing levels of automation support for X-ray screening tasks, 3) evaluations of automatic speech-to-text tools for communicating weather information in aviation, and 4) evaluations of a fully autonomous, universally-designed vehicle prototype for people with travel-limiting disabilities. Findings from this research are expected to contribute to the broader discussion on how to thoughtfully design collaborative human-automation systems that successfully leverage the unique strengths and capabilities of each acting agent.

]]>
2023-02-24T13:00:00-05:00 2023-02-24T14:00:00-05:00 2023-02-24T14:00:00-05:00 2023-02-24 18:00:00 2023-02-24 19:00:00 2023-02-24 19:00:00 2023-02-24T13:00:00-05:00 2023-02-24T14:00:00-05:00 America/New_York America/New_York datetime 2023-02-24 01:00:00 2023-02-24 02:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[Online Information Session: Supply Chain Analytics Professional Certificate]]> 27233 About Our Session

Join us online February 16 from 1-2pm ET to learn about our 4-course series offered through Georgia Tech Professional Education. The information session will discuss certificate requirements, course curriculum, key components, and learning outcomes for the Supply Chain Analytics series.

If you cannot join us live, still register to receive the slide deck presentation via email after the event.

Session Host

Darrell Kent
Instructor @GT Supply Chain Analytics

Darrell Kent is the professional education manager for ORTEC and the lead instructor for the Georgia Tech Supply Chain & Logistics Institute’s Supply Chain Analytics Professional Certificate program. He brings more than 15 years of senior experience helping companies deploy machine learning, mathematical optimization, and other advanced analytics solutions as part of their wider digital transformation efforts, including in relation to pricing management, customer segmentation, demand forecasting, resource planning, network design, visual detection systems for automated yard management, predictive maintenance, machine learning-based booking control, and dispatch automation and optimization. His instructional style focuses on helping learners develop practical skills that can be immediately used to address real supply chain challenges in their organizations.

]]> Andy Haleblian 1 1675810322 2023-02-07 22:52:02 1675949643 2023-02-09 13:34:03 0 0 event Join us online February 16 from 1-2pm ET to learn about our 4-course series offered through Georgia Tech Professional Education. The information session will discuss certificate requirements, course curriculum, key components, and learning outcomes for the Supply Chain Analytics series.

]]>
2023-02-16T14:00:00-05:00 2023-02-16T15:00:00-05:00 2023-02-16T15:00:00-05:00 2023-02-16 19:00:00 2023-02-16 20:00:00 2023-02-16 20:00:00 2023-02-16T14:00:00-05:00 2023-02-16T15:00:00-05:00 America/New_York America/New_York datetime 2023-02-16 02:00:00 2023-02-16 03:00:00 America/New_York America/New_York datetime <![CDATA[]]> 665583 665583 image <![CDATA[Online Information Session: Supply Chain Analytics Professional Certificate]]> image/jpeg 1675810018 2023-02-07 22:46:58 1675810018 2023-02-07 22:46:58 <![CDATA[Register Online for the SCA Information Session]]>
<![CDATA[ISyE Seminar - Linda Boyle]]> 36374 Title:

A framework for modeling human-vehicle interactions with increasingly autonomous systems

Abstract:

Modeling human-vehicle interactions requires an understanding of the human behavior. The model development needs to capture human’s interaction with other people, the environment, and their surroundings.  A challenge in model development is the ability to accurately predict human behavior, particularly in complex environments that include other human road users, such as pedestrians and bicyclists. In this presentation, a framework is provided to better quantify and predict interactive human-vehicle decision-making, which can then be used to better inform the algorithms for advanced driver assistance systems (ADAS).

Bio:

Linda Ng Boyle is Professor in Industrial & Systems Engineering at the University of Washington, Seattle. She has a joint appointment in Civil & Environmental Engineering. She has degrees from the University of Buffalo (BS) and University of Washington (MS, PhD).  She is a member of the National Academies Board of Human System Integration and co-author of the textbook, “Designing for People: An Introduction to Human Factors Engineering”.

 

]]> mwelch39 1 1675688397 2023-02-06 12:59:57 1675688397 2023-02-06 12:59:57 0 0 event Modeling human-vehicle interactions requires an understanding of the human behavior. The model development needs to capture human’s interaction with other people, the environment, and their surroundings.  A challenge in model development is the ability to accurately predict human behavior, particularly in complex environments that include other human road users, such as pedestrians and bicyclists. In this presentation, a framework is provided to better quantify and predict interactive human-vehicle decision-making, which can then be used to better inform the algorithms for advanced driver assistance systems (ADAS).

]]>
2023-02-17T12:30:00-05:00 2023-02-17T13:30:00-05:00 2023-02-17T13:30:00-05:00 2023-02-17 17:30:00 2023-02-17 18:30:00 2023-02-17 18:30:00 2023-02-17T12:30:00-05:00 2023-02-17T13:30:00-05:00 America/New_York America/New_York datetime 2023-02-17 12:30:00 2023-02-17 01:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[ISyE Seminar - Zhimei Ren]]> 34977 Title: 

Stable Variable Selection with Knockoffs 

Abstract: 

A common problem in many modern statistical applications is to find a set of important variables—from a pool of many candidates—that explain the response of interest. For this task, model-X knockoffs offers a general framework that can leverage any feature importance measure to produce a variable selection algorithm: it discovers true effects while rigorously controlling the number or fraction of false positives, paving the way for reproducible scientific discoveries. The model-X knockoffs, however, is a randomized procedure that relies on the one-time construction of synthetic (random) variables. Different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is not desirable for the reproducibility of the reported results. 

In this talk, I will introduce derandomization schemes that aggregate the selection results across multiple runs of the knockoffs algorithm to yield stable selection. In the first part, I will present a derandomization scheme that controls the number of false positives, i.e., the per family error rate (PFER) and the k family-wise error rate (k-FWER). In the second part, I will talk about an alternative derandomization scheme with provable false discovery rate (FDR) control. Equipped with these derandomization steps, the knockoffs framework provides a powerful tool for making reproducible scientific discoveries. The proposed methods are evaluated on both simulated and real data, demonstrating comparable power and dramatically lower selection variability when compared with the original model-X knockoffs.

Bio: 

Zhimei Ren is a postdoctoral researcher in the Statistics Department at the University of Chicago, advised by Professor Rina Foygel Barber. Before joining the University of Chicago, she obtained her Ph.D. in Statistics from Stanford University, under the supervision of Professor Emmanuel Candès. Her research interests lie broadly in multiple hypothesis testing, distribution-free inference, causal inference, survival analysis and data-driven decision-making.

]]> Julie Smith 1 1675197832 2023-01-31 20:43:52 1675197832 2023-01-31 20:43:52 0 0 event Abstract: 

A common problem in many modern statistical applications is to find a set of important variables—from a pool of many candidates—that explain the response of interest. For this task, model-X knockoffs offers a general framework that can leverage any feature importance measure to produce a variable selection algorithm: it discovers true effects while rigorously controlling the number or fraction of false positives, paving the way for reproducible scientific discoveries. The model-X knockoffs, however, is a randomized procedure that relies on the one-time construction of synthetic (random) variables. Different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is not desirable for the reproducibility of the reported results. 

]]>
2023-02-13T12:00:00-05:00 2023-02-13T13:00:00-05:00 2023-02-13T13:00:00-05:00 2023-02-13 17:00:00 2023-02-13 18:00:00 2023-02-13 18:00:00 2023-02-13T12:00:00-05:00 2023-02-13T13:00:00-05:00 America/New_York America/New_York datetime 2023-02-13 12:00:00 2023-02-13 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Jovan Julien]]> 34977 Title:

Bridging an Information Divide: Clinically Relevant Models of Alcohol Consumption and Liver Disease to Inform Dynamic and Interpretable Community Alcohol Policies

Abstract:

Alcohol-attributable liver disease (ALD) rates have been on the rise globally and in the United States over the past two decades and harmful alcohol use contributes to more than 200 disease and injury conditions. Each disease event carries a significant cost for the individual, their community, and society at large. Due to the unobserved nature of early disease states and limited data on disease prevalence in the general population, most models of ALD and alcohol-use disorders focus on the treatment of late-stage disease including liver cirrhosis and liver cancer without the ability to consider harm reduction strategies available at the population and individual level.

This talk will focus on a clinically relevant model of ALD that incorporates the regenerative abilities of the liver, with the objective of projecting the impact of current drinking patterns on disease morbidity and mortality in the US population and the impact of societal and individual level interventions on projected disease burden and healthcare costs. The first portion of the talk will focus on building an alcohol consumption and liver disease model incorporating current consumption data that can be leveraged for decision analysis by stake holders at the individual, clinical, and population level. The second part will consider the potential of harm reduction strategies and alcohol policy to positively impact the future burden of disease in the United States. A data collection scheme will be proposed to parameterize a personalized disease progression and decision-making model.

Bio:

Jovan Julien is currently on a postdoctoral fellowship at Massachusetts General Hospital's Institute for Technology Assessment and Harvard Medical School, where they are currently working on modeling the impact of breathing modulation and other meditation and mindfulness techniques on disease mitigation strategies. Their broader research interests are in predictive health models that can inform wellness interventions and policy at the individual, interpersonal, and systemic levels to limit and eventually reverse the growth rate in per capita spending on healthcare while improving long-term outcomes. Jovan received their B.Sc in Biomedical Engineering from Brown University, and a master’s in health systems engineering and PhD degree in Operations Research from Georgia Institute of Technology's H. Milton Stewart School of Industrial & Systems Engineering. Their studies were supported with a Health Policy Research Fellowship sponsored by the Robert Wood Johnson Foundation.

]]> Julie Smith 1 1675168994 2023-01-31 12:43:14 1675169019 2023-01-31 12:43:39 0 0 event Abstract:

Alcohol-attributable liver disease (ALD) rates have been on the rise globally and in the United States over the past two decades and harmful alcohol use contributes to more than 200 disease and injury conditions. Each disease event carries a significant cost for the individual, their community, and society at large. Due to the unobserved nature of early disease states and limited data on disease prevalence in the general population, most models of ALD and alcohol-use disorders focus on the treatment of late-stage disease including liver cirrhosis and liver cancer without the ability to consider harm reduction strategies available at the population and individual level.

This talk will focus on a clinically relevant model of ALD that incorporates the regenerative abilities of the liver, with the objective of projecting the impact of current drinking patterns on disease morbidity and mortality in the US population and the impact of societal and individual level interventions on projected disease burden and healthcare costs. The first portion of the talk will focus on building an alcohol consumption and liver disease model incorporating current consumption data that can be leveraged for decision analysis by stake holders at the individual, clinical, and population level. The second part will consider the potential of harm reduction strategies and alcohol policy to positively impact the future burden of disease in the United States. A data collection scheme will be proposed to parameterize a personalized disease progression and decision-making model.

]]>
2023-02-15T12:00:00-05:00 2023-02-15T13:00:00-05:00 2023-02-15T13:00:00-05:00 2023-02-15 17:00:00 2023-02-15 18:00:00 2023-02-15 18:00:00 2023-02-15T12:00:00-05:00 2023-02-15T13:00:00-05:00 America/New_York America/New_York datetime 2023-02-15 12:00:00 2023-02-15 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Yale Herer]]> 36374 TITLE

An asymptotic perspective on risk pooling: Limitations and relationship to transshipments.

 

ABSTRACT

In this talk we provide a novel perspective on risk pooling approaches by characterizing and comparing their asymptotic performance, highlighting the conditions under which one approach dominates the other. More specifically, we determine the inventory policy and the expected total costs of systems under physical and information pooling as the number of locations grows. We show that physical pooling dominates information pooling in settings with no additional per-item and per-location costs for operating the centralized system. In the presence of such costs, however, information pooling becomes a viable alternative to physical pooling. Through asymptotic analysis, we also address the grouping problem, the division of a given set of non-identical locations into an ordered collection of mutually exclusive and collectively exhaustive subsets of predetermined sizes and demonstrate that homogeneous groups, comprising locations with similar demand volatility, achieve a lower expected total cost. Finally, the convergence of the expected total costs and the base stock levels under the two pooling approaches is demonstrated through a simple numerical illustration. Our analysis supports the assertion that it is important to consider not only the individual characteristics of each location in isolation, but also the interactions among them, when designing pooling systems.

 

BIO

Yale T. Herer, BS (1986), MS (1990), PhD (1990), Cornell University, Department of Operations Research and Industrial Engineering. Yale is an associate professor in the Faculty of Industrial Engineering and Management at the Technion—Israel Institute of Technology where, since 2018, he serves as Vice Dean of Programs of Study. Yale has worked for several industrial concerns, both as a consultant and as an advisor to project groups. He also serves as an associate editor for Naval Research Logistics and has served on the editorial staff of IIE Transactions and Operations Research Letters. Yale has successfully planned and executed four conferences, including the 2010 annual conference for the Manufacturing and Service Operations Management Society (MSOM). Yale’s research interest can be broadly defined as covering Production Planning and Control. More recently Yale has focused his research on the area of Supply Chain Management, especially when integrated with transshipments or other responsive operational activities. Yale has won various prizes including a 1996 IIE Transactions Best Paper Award, the 2002 Mitchner Award in Quality Sciences and Quality Management, a 2008 IBM Faculty Award, and INFORM’s 2013 Daniel H. Wagner Prize for Excellence in Operations Research Practice.

]]> mwelch39 1 1675086405 2023-01-30 13:46:45 1675086405 2023-01-30 13:46:45 0 0 event In this talk we provide a novel perspective on risk pooling approaches by characterizing and comparing their asymptotic performance, highlighting the conditions under which one approach dominates the other. More specifically, we determine the inventory policy and the expected total costs of systems under physical and information pooling as the number of locations grows. We show that physical pooling dominates information pooling in settings with no additional per-item and per-location costs for operating the centralized system. In the presence of such costs, however, information pooling becomes a viable alternative to physical pooling. Through asymptotic analysis, we also address the grouping problem, the division of a given set of non-identical locations into an ordered collection of mutually exclusive and collectively exhaustive subsets of predetermined sizes and demonstrate that homogeneous groups, comprising locations with similar demand volatility, achieve a lower expected total cost. Finally, the convergence of the expected total costs and the base stock levels under the two pooling approaches is demonstrated through a simple numerical illustration. Our analysis supports the assertion that it is important to consider not only the individual characteristics of each location in isolation, but also the interactions among them, when designing pooling systems.

 

 

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2023-02-01T12:30:00-05:00 2023-02-01T13:30:00-05:00 2023-02-01T13:30:00-05:00 2023-02-01 17:30:00 2023-02-01 18:30:00 2023-02-01 18:30:00 2023-02-01T12:30:00-05:00 2023-02-01T13:30:00-05:00 America/New_York America/New_York datetime 2023-02-01 12:30:00 2023-02-01 01:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[SCL IRC Seminar: Towards a Circular Carbon Economy: Plants do it – why don't we?]]> 27233 The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested faculty, students and corporate partners as well as the general public. If you are interested in attending any of the sessions, please review the below information and register online.

SESSION OVERVIEW

Our current energy systems are undergoing a significant shift from fossil carbon burning to the utilization of current sunlight.  There is the misconception that this shift also implies that we have to stop using carbon-based fuels as energy carriers.  What mix of energy carriers will be optimal for our future energy systems, and possibly more importantly what mix can we use during the next 30-40 years as we transition from fossil to renewable is still a very open question.  In this talk I will describe how we might couple renewable energy to carbon-based fuels through systems of direct air capture of CO2 and subsequent conversion.  Even within this space there is significant potential diversity of molecules that can be used, for example methanol is currently gaining favor in the shipping industry, but it could be that traditional hydrocarbons are a better fit due to the existing infrastructure.

SESSION SPEAKER

Matthew Realff, Professor and David Wang Sr Fellow, School of Chemical and Biomolecular Engineering 

Register Online for upcoming SCL IRC seminars

In-person attendance to our SCL IRC sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program. If you are a member of the general public attending in-person, the cost to attend is $5 per session which includes a boxed lunch*. Virtual attendance is always free.

Please see our registration page relating to taking advantage of the optional in-person lunch.

If you have any questions, please email event@scl.gatech.edu.

]]> Andy Haleblian 1 1674859776 2023-01-27 22:49:36 1674859785 2023-01-27 22:49:45 0 0 event The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested SCL faculty, students and corporate partners as well as the general public.

]]>
2023-03-16T13:00:00-04:00 2023-03-16T14:30:00-04:00 2023-03-16T14:30:00-04:00 2023-03-16 17:00:00 2023-03-16 18:30:00 2023-03-16 18:30:00 2023-03-16T13:00:00-04:00 2023-03-16T14:30:00-04:00 America/New_York America/New_York datetime 2023-03-16 01:00:00 2023-03-16 02:30:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

]]>
665218 665218 image <![CDATA[SCL IRC Seminar: Towards a Circular Carbon Economy: Plants do it – why don't we?]]> image/jpeg 1674859744 2023-01-27 22:49:04 1674859744 2023-01-27 22:49:04 <![CDATA[Register Online for upcoming SCLIRC seminars]]>
<![CDATA[SCL IRC Seminar: Re-imagining the Future of the Construction Industry]]> 27233 The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested faculty, students and corporate partners as well as the general public. If you are interested in attending any of the sessions, please review the below information and register online.

SESSION OVERVIEW

In this talk, we will share a vision for the future of the built-environment, which is smart, inter-connected, and both user-and environmental-conscious. We will also discuss the challenges that need to be overcome to realize this vision and the innovative trends and emerging technologies that will facilitate the transformation of the construction industry.

SESSION SPEAKER

Pardis Pishdad-Bozorgi, Associate Professor | Graduate Program Director | Director, Smart Built EcoSystem (Smart Bees) Laboratory

Register Online for upcoming SCL IRC seminars

In-person attendance to our SCL IRC sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program. If you are a member of the general public attending in-person, the cost to attend is $5 per session which includes a boxed lunch*. Virtual attendance is always free.

Please see our registration page relating to taking advantage of the optional in-person lunch.

If you have any questions, please email event@scl.gatech.edu.

]]> Andy Haleblian 1 1674591400 2023-01-24 20:16:40 1674591407 2023-01-24 20:16:47 0 0 event The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested SCL faculty, students and corporate partners as well as the general public.

]]>
2023-02-23T13:00:00-05:00 2023-02-23T14:30:00-05:00 2023-02-23T14:30:00-05:00 2023-02-23 18:00:00 2023-02-23 19:30:00 2023-02-23 19:30:00 2023-02-23T13:00:00-05:00 2023-02-23T14:30:00-05:00 America/New_York America/New_York datetime 2023-02-23 01:00:00 2023-02-23 02:30:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

]]>
665065 665065 image <![CDATA[SCL IRC Seminar: Re-imagining the Future of the Construction Industry with Dr. Pardis Pishdad-Bozorgi]]> image/jpeg 1674591372 2023-01-24 20:16:12 1674841786 2023-01-27 17:49:46 <![CDATA[Register Online for upcoming SCLIRC seminars]]>
<![CDATA[ISyE Seminar - Paul Gölz]]> 34977 Title:

Fair, Representative, and Transparent Algorithms for Citizens’ Assemblies

Abstract:

Globally, an alternative approach to democracy is gaining momentum: citizens’ assemblies, in which randomly selected constituents discuss policy questions and propose solutions. Domain experts have two conflicting requirements on the selection of these assemblies: (1) assemblies should reflect the demographics of the population, and (2) all constituents should have equal chances of being selected. In this talk, I will describe work on designing and analyzing randomized selection algorithms that favorably trade off these objectives. I will share experiences with deploying these algorithms on our online platform Panelot and discuss what we learned from practitioners in the process of adoption. Finally, I will explore how these lessons sparked work on other aspects of citizens’ assemblies, such as making the random selection process transparent and managing the discussions within the assembly.

Bio:

Paul Gölz is a postdoctoral researcher at the School of Engineering and Applied Sciences at Harvard. He received his Ph.D. in computer science from Carnegie Mellon University under the supervision of Ariel Procaccia. Paul studies democratic decision-making and the fair allocation of resources, using tools from algorithms, optimization, and artificial intelligence. Algorithms developed in his work are now deployed to select citizens’ assemblies around the world and to allocate refugees for a major US resettlement agency.

]]> Julie Smith 1 1674236259 2023-01-20 17:37:39 1674236259 2023-01-20 17:37:39 0 0 event Abstract:

Globally, an alternative approach to democracy is gaining momentum: citizens’ assemblies, in which randomly selected constituents discuss policy questions and propose solutions. Domain experts have two conflicting requirements on the selection of these assemblies: (1) assemblies should reflect the demographics of the population, and (2) all constituents should have equal chances of being selected. In this talk, I will describe work on designing and analyzing randomized selection algorithms that favorably trade off these objectives. I will share experiences with deploying these algorithms on our online platform Panelot and discuss what we learned from practitioners in the process of adoption. Finally, I will explore how these lessons sparked work on other aspects of citizens’ assemblies, such as making the random selection process transparent and managing the discussions within the assembly.

]]>
2023-02-07T12:00:00-05:00 2023-02-07T13:00:00-05:00 2023-02-07T13:00:00-05:00 2023-02-07 17:00:00 2023-02-07 18:00:00 2023-02-07 18:00:00 2023-02-07T12:00:00-05:00 2023-02-07T13:00:00-05:00 America/New_York America/New_York datetime 2023-02-07 12:00:00 2023-02-07 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Karen Smilowitz]]> 36374 TITLE

Revisiting School District Design: A Stream-based Approach

Karen Smilowitz, James N. and Margie M. Krebs Professor in Industrial Engineering and Management Sciences, McCormick School of Engineering; Professor of Operations, Kellogg School of Management, Northwestern University

ABSTRACT

Operations research methods have been used to improve operations in public school systems for over fifty years.  The talk will explore connections between evolving issues in public education and advances in optimization, computing and geographic information systems, beginning with early work motivated by Supreme Court decisions to desegregate schools.  The talk will focus specifically on the school district design problem. We introduce a new compact formulation that incorporates multiple assignment decisions simultaneously by assigning students in small geographic units to sets of schools (e.g., elementary, middle, high school) and programs (e.g., bilingual education) with single composite variables, referred to as “streams”. This compact formulation incorporates advances in district design modeling from the literature and extends the decision-making capabilities of such models. We tie these extensions to education literature and policies at specific school districts. This new formulation is computationally efficient, easily reconfigurable for evolving problem specifications, and facilitates improved communication with stakeholders.  To illustrate these capabilities, we present a case study from a partnership focused on district redesign to address historic inequities in access to education.

BIO

Dr. Karen Smilowitz is the James N. and Margie M. Krebs Professor in Industrial Engineering and Management Science at Northwestern University, with a joint appointment in the Operations group at the Kellogg School of Management.  Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications.  She has been instrumental in promoting the use of operations research within the humanitarian and nonprofit sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets.  Dr. Smilowitz is the Editor-in-Chief of Transportation Science and a Fellow of the INFORMS society.

 

]]> mwelch39 1 1674160372 2023-01-19 20:32:52 1674160372 2023-01-19 20:32:52 0 0 event Operations research methods have been used to improve operations in public school systems for over fifty years.  The talk will explore connections between evolving issues in public education and advances in optimization, computing and geographic information systems, beginning with early work motivated by Supreme Court decisions to desegregate schools.  The talk will focus specifically on the school district design problem. We introduce a new compact formulation that incorporates multiple assignment decisions simultaneously by assigning students in small geographic units to sets of schools (e.g., elementary, middle, high school) and programs (e.g., bilingual education) with single composite variables, referred to as “streams”. This compact formulation incorporates advances in district design modeling from the literature and extends the decision-making capabilities of such models. We tie these extensions to education literature and policies at specific school districts. This new formulation is computationally efficient, easily reconfigurable for evolving problem specifications, and facilitates improved communication with stakeholders.  To illustrate these capabilities, we present a case study from a partnership focused on district redesign to address historic inequities in access to education.

]]>
2023-01-27T12:30:00-05:00 2023-01-27T13:30:00-05:00 2023-01-27T13:30:00-05:00 2023-01-27 17:30:00 2023-01-27 18:30:00 2023-01-27 18:30:00 2023-01-27T12:30:00-05:00 2023-01-27T13:30:00-05:00 America/New_York America/New_York datetime 2023-01-27 12:30:00 2023-01-27 01:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[ISyE Seminar - Maryam Zahabi ]]> 34977 Title:

Cognitive Workload Assessment of Upper-Limb Prosthetic Devices

Abstract:

Limb amputation can cause severe functional disability for the performance of activities of daily living (ADLs). Amputees use prosthetic devices on a regular basis to perform ADLs. Prosthetic devices require substantial amount of cognitive resources, which can lead to device rejection. However, prior research was mainly focused on measuring physical performance of using prosthetic devices. Assessing cognitive workload of prostheses is critical to ensure device usability. This study investigated models for classifying cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features, metrics, and tasks. The proposed algorithms can help manufacturers and clinicians predict cognitive workload of future EMG-based prosthetic devices in early design phases.

Bio:

Maryam Zahabi is an assistant professor in the Wm Michael Barnes ’64 department of industrial and systems engineering at Texas A&M University. Her research focuses on human performance modeling with applications in assistive technologies and surface transportation. She received her PhD in industrial and systems engineering from North Carolina State University in 2017. Dr. Zahabi’s research has received support from agencies including the NSF, DARPA, and U.S. DOT. She is also the recipient of the 2021 NSF CAREER Award. Dr. Zahabi has published over 35 journal papers in the human systems engineering area and serves an associate editor for IEEE Transactions on Human-Machine Systems journal.

]]> Julie Smith 1 1674148430 2023-01-19 17:13:50 1674148430 2023-01-19 17:13:50 0 0 event Abstract:

Limb amputation can cause severe functional disability for the performance of activities of daily living (ADLs). Amputees use prosthetic devices on a regular basis to perform ADLs. Prosthetic devices require substantial amount of cognitive resources, which can lead to device rejection. However, prior research was mainly focused on measuring physical performance of using prosthetic devices. Assessing cognitive workload of prostheses is critical to ensure device usability. This study investigated models for classifying cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features, metrics, and tasks. The proposed algorithms can help manufacturers and clinicians predict cognitive workload of future EMG-based prosthetic devices in early design phases.

]]>
2023-02-09T12:00:00-05:00 2023-02-09T13:00:00-05:00 2023-02-09T13:00:00-05:00 2023-02-09 17:00:00 2023-02-09 18:00:00 2023-02-09 18:00:00 2023-02-09T12:00:00-05:00 2023-02-09T13:00:00-05:00 America/New_York America/New_York datetime 2023-02-09 12:00:00 2023-02-09 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Constance Crozier]]> 34977 Title:

Decarbonization of the power sector with human-in-the-loop

Abstract:

The variability of renewable generation is a barrier to the decarbonization of the power sector. Existing methods for coping with uncertainty in power systems focus on the supply side (e.g using energy storage, power imports, or supplementing with controllable fossil generation). However, many of the emerging technologies which consumer power have inherent flexibility, meaning control of the demand-side will be possible. This talk will cover the integration of residential demand flexibility into power systems, with the objective of offsetting variability from renewable generation. The focus will largely be on domestic electric vehicle charging. The first part of the talk will focus on building decision models to quantify the aggregated flexibility of charging. A data-driven approach will be introduced that is based on clustering of conventional vehicle usage data. The second part will focus on control strategies for large numbers of distributed flexible resources. An optimization scheme will be introduced which preserves consumer privacy and equity, while protecting local network components.

Bio:

Constance received both M.Eng and PhD degrees from the University of Oxford. Her PhD focused on understanding the impact that electric vehicle charging will have on power systems. She is currently a postdoc at CU Boulder, where she has been working on the ARPA-E Grid Optimization Competition. Her broader research interests are in realizing the potential of demand flexibility in power systems. 

 

]]> Julie Smith 1 1673359582 2023-01-10 14:06:22 1673365877 2023-01-10 15:51:17 0 0 event Abstract

The variability of renewable generation is a barrier to the decarbonization of the power sector. Existing methods for coping with uncertainty in power systems focus on the supply side (e.g using energy storage, power imports, or supplementing with controllable fossil generation). However, many of the emerging technologies which consumer power have inherent flexibility, meaning control of the demand-side will be possible. This talk will cover the integration of residential demand flexibility into power systems, with the objective of offsetting variability from renewable generation. First, the classical optimal power flow problem will be introduced and the challenges of scaling it to include controllable generation will be discussed. Then two methods will be introduced which both aim to coordinate flexible resources in a scalable manner that preserves consumer privacy. The first method uses formal optimization to determine price signals, while the second uses multi-agent reinforcement learning.

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2023-02-02T12:00:00-05:00 2023-02-02T13:00:00-05:00 2023-02-02T13:00:00-05:00 2023-02-02 17:00:00 2023-02-02 18:00:00 2023-02-02 18:00:00 2023-02-02T12:00:00-05:00 2023-02-02T13:00:00-05:00 America/New_York America/New_York datetime 2023-02-02 12:00:00 2023-02-02 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Tianyi Peng]]> 34977 Title:

Experimentation Platforms and Learning Treatment Effects in Panels

Abstract:

Experiments in brick-and-mortar retail are contaminated for myriad reasons. Pragmatic inference in such settings is more akin to learning from observational data, as opposed to the typical setup one might consider for a carefully designed randomized experiment. So motivated, we consider the problem of causal inference in panels with general intervention patterns that may depend on the historical data. We provide a novel, near-complete solution to this problem that allows for rate-optimal recovery of treatment effects. Our work generalizes the outcome model of the difference-in-difference paradigm and expands the applicability of the synthetic-control paradigm. In doing so, we provide a novel de-biasing analysis that addresses the low-rank matrix regression with non-random intervention patterns and noise; a non-trivial feature of independent interest.  Our algorithms form the core of a new testing platform we co-developed with a USD 100B drink company, which increased revenue by millions of dollars monthly in Mexico alone. 

Bio: 

Tianyi Peng is a Ph.D. student at MIT. He is advised by Vivek Farias, and also mentored by Andrew Li. He is broadly interested in developing algorithms for learning and inference in large-scale dynamic decision-making systems. In particular, he is interested in developing next-generation experimentation platforms, which provide scalable, low-cost solutions for discovering beneficial strategies/policies. In translating these ideas, he is engaged with Anheuser-Busch InBev, Takeda Pharmaceuticals, TikTok, and Liberty Mutual. His work has been recognized as a finalist for the MSOM Student Paper Competition (2022), and has won the INFORMS Daniel H. Wagner Prize (2022), Applied Probability Society Best Student Paper Prize (2022), Jeff McGill Student Paper Award (2022) and the best thesis award at Tsinghua where he graduated with the 2017 Yao Class. 

]]> Julie Smith 1 1671646849 2022-12-21 18:20:49 1671719724 2022-12-22 14:35:24 0 0 event Abstract:

Experiments in brick-and-mortar retail are contaminated for myriad reasons. Pragmatic inference in such settings is more akin to learning from observational data, as opposed to the typical setup one might consider for a carefully designed randomized experiment. So motivated, we consider the problem of causal inference in panels with general intervention patterns that may depend on the historical data. We provide a novel, near-complete solution to this problem that allows for rate-optimal recovery of treatment effects. Our work generalizes the outcome model of the difference-in-difference paradigm and expands the applicability of the synthetic-control paradigm. In doing so, we provide a novel de-biasing analysis that addresses the low-rank matrix regression with non-random intervention patterns and noise; a non-trivial feature of independent interest.  Our algorithms form the core of a new testing platform we co-developed with a USD 100B drink company, which increased revenue by millions of dollars monthly in Mexico alone. 

]]>
2023-01-12T12:00:00-05:00 2023-01-12T13:00:00-05:00 2023-01-12T13:00:00-05:00 2023-01-12 17:00:00 2023-01-12 18:00:00 2023-01-12 18:00:00 2023-01-12T12:00:00-05:00 2023-01-12T13:00:00-05:00 America/New_York America/New_York datetime 2023-01-12 12:00:00 2023-01-12 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE - Building ]]>
<![CDATA[ISyE Seminar - Huseyin Topaloglu]]> 34977 Title:

Joint Inventory Allocation and Assortment Personalization with Performance Guarantees

Abstract:

In this talk, we give approximation algorithms for a joint inventory allocation and assortment personalization problem motivated by an online retail setting. In our problem, we have a limited amount of storage capacity that needs to be allocated among multiple products to serve customers that arrive over a selling horizon. At the beginning of the selling horizon, we decide how many units of each product to stock. Over the selling horizon, customers arrive at the platform one by one to make a purchase. Based on the remaining inventories of the products and the information available on the arriving customer, we offer a personalized assortment of products to each customer. The customer either makes a choice within the offered assortment or leaves without a purchase. Our goal is to decide how many units of each product to stock at the beginning of the selling horizon and to find a policy to figure out which personalized assortment to offer to each arriving customer to maximize the total expected revenue over the selling horizon. Our problem is motivated by same-day-delivery applications in online retail, where the retailer needs to allocate the limited storage capacity in an urban warehouse among different variants in a product category, while having the capability of offering personalized assortments to customers to make better use of remaining inventories. Allocating the storage capacity among the products requires tackling a combinatorial problem, whereas finding an assortment personalization policy requires approximating a dynamic program with a high-dimensional state variable. When the choices of the customers are governed by the multinomial logit model, we give a constant-factor approximation algorithm for this joint inventory allocation and assortment personalization problem. Under a general choice model, we give an algorithm that is asymptotically optimal as the storage capacity gets large. In the latter result, the demand can be scaled in an arbitrary fashion along with the storage capacity. This is joint work with Yicheng Bai, Omar El Housni and Paat Rusmevichientong.

Bio:

Huseyin Topaloglu is the Howard and Eleanor Morgan Professor in the School of Operations Research and Information Engineering at Cornell Tech. He holds a Ph.D. in Operations Research and Financial Engineering from Princeton. His recent research focuses on constructing tractable solution methods for large-scale network revenue management problems and building approximation strategies for retail assortment planning. Huseyin Topaloglu is currently serving as an area editor for Analytics in Operations area at Manufacturing and Service Operations Management.

]]> Julie Smith 1 1668454768 2022-11-14 19:39:28 1669997478 2022-12-02 16:11:18 0 0 event Abstract:

In this talk, we give approximation algorithms for a joint inventory allocation and assortment personalization problem motivated by an online retail setting. In our problem, we have a limited amount of storage capacity that needs to be allocated among multiple products to serve customers that arrive over a selling horizon. At the beginning of the selling horizon, we decide how many units of each product to stock. Over the selling horizon, customers arrive at the platform one by one to make a purchase. Based on the remaining inventories of the products and the information available on the arriving customer, we offer a personalized assortment of products to each customer. The customer either makes a choice within the offered assortment or leaves without a purchase. Our goal is to decide how many units of each product to stock at the beginning of the selling horizon and to find a policy to figure out which personalized assortment to offer to each arriving customer to maximize the total expected revenue over the selling horizon. Our problem is motivated by same-day-delivery applications in online retail, where the retailer needs to allocate the limited storage capacity in an urban warehouse among different variants in a product category, while having the capability of offering personalized assortments to customers to make better use of remaining inventories. Allocating the storage capacity among the products requires tackling a combinatorial problem, whereas finding an assortment personalization policy requires approximating a dynamic program with a high-dimensional state variable. When the choices of the customers are governed by the multinomial logit model, we give a constant-factor approximation algorithm for this joint inventory allocation and assortment personalization problem. Under a general choice model, we give an algorithm that is asymptotically optimal as the storage capacity gets large. In the latter result, the demand can be scaled in an arbitrary fashion along with the storage capacity. This is joint work with Yicheng Bai, Omar El Housni and Paat Rusmevichientong.

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2022-12-01T12:00:00-05:00 2022-12-01T13:00:00-05:00 2022-12-01T13:00:00-05:00 2022-12-01 17:00:00 2022-12-01 18:00:00 2022-12-01 18:00:00 2022-12-01T12:00:00-05:00 2022-12-01T13:00:00-05:00 America/New_York America/New_York datetime 2022-12-01 12:00:00 2022-12-01 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[SCL IRC Seminar: Robotics Research and Perspectives]]> 27233 The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested faculty, students and corporate partners as well as the general public. If you are interested in attending any of the sessions, please review the below information and register online.

SESSION OVERVIEW

Robotic systems have been very successful in performing tasks where the inputs are well defined and known in advance. Automotive and electronic manufacturing are the classic success stories where robotic systems have demonstrated incredible value for the industry. In unstructured manufacturing, the inputs of the system can vary significantly, but the outputs of the system are defined. Food and agriculture production are examples of this type of unstructured manufacturing problem, but traditional manufacturing is also moving in this direction as robotics move beyond welding and painting. This presentation will give examples of systems that integrate advanced perception and control technologies into robotic systems to perform complex tasks like cutting, grasping, and manipulation of objects in an unstructured environment.

SESSION SPEAKER

Gary McMurray, Principal Research Engineer

Register Online for upcoming SCL IRC seminars

In-person attendance to our SCL IRC sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program. If you are a member of the general public attending in-person, the cost to attend is $5 per session which includes a boxed lunch*. Virtual attendance is always free.

Please see our registration page relating to taking advantage of the optional in-person lunch.

If you have any questions, please email event@scl.gatech.edu.

]]> Andy Haleblian 1 1669751549 2022-11-29 19:52:29 1669751560 2022-11-29 19:52:40 0 0 event The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested SCL faculty, students and corporate partners as well as the general public.

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2023-01-24T13:00:00-05:00 2023-01-24T14:30:00-05:00 2023-01-24T14:30:00-05:00 2023-01-24 18:00:00 2023-01-24 19:30:00 2023-01-24 19:30:00 2023-01-24T13:00:00-05:00 2023-01-24T14:30:00-05:00 America/New_York America/New_York datetime 2023-01-24 01:00:00 2023-01-24 02:30:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

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663510 663510 image <![CDATA[SCL IRC Seminar: Robotics Research and Perspectives]]> image/jpeg 1669751506 2022-11-29 19:51:46 1669751506 2022-11-29 19:51:46 <![CDATA[Register Online for upcoming SCLIRC seminars]]>
<![CDATA[BBISS Seminar Series - Valerie Thomas - 12/1/22]]> 27233 Current Methods for Life Cycle Assessment of Low-Carbon Transportation Fuels

Valerie Thomas, Ph.D., Anderson-Interface Chair of Natural Systems, Professor, H. Milton School of Industrial and Systems Engineering, joint appointment in the School of Public Policy, Georgia Institute of Technology

Decmber 1, 2022, 3 - 4 PM ET
Hybrid Event - Teams Link
Georgia Tech Exhibition Hall, Room 222, Buckhead Room
Refreshments will be served. Door prize for in-person participants.

Abstract: This talk will present the results of a recent National Academies study chaired by Dr. Thomas, which looks into the methods used to calculate the climate impacts of various low-carbon transportation fuels for life cycle assessments. The implications and opportunities for sustainability research at Georgia Tech will also be discussed.

]]> Andy Haleblian 1 1669680127 2022-11-29 00:02:07 1669736054 2022-11-29 15:34:14 0 0 event This talk will present the results of a recent National Academies study chaired by Dr. Thomas, as well as implications and opportunities for sustainability research at Georgia Tech.

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2022-12-01T16:00:00-05:00 2022-12-01T17:00:00-05:00 2022-12-01T17:00:00-05:00 2022-12-01 21:00:00 2022-12-01 22:00:00 2022-12-01 22:00:00 2022-12-01T16:00:00-05:00 2022-12-01T17:00:00-05:00 America/New_York America/New_York datetime 2022-12-01 04:00:00 2022-12-01 05:00:00 America/New_York America/New_York datetime <![CDATA[Venue & Parking Information]]> Susan Ryan, Program and Operations Manager, BBISS

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663483 663483 image <![CDATA[BBISS Speaker Series Speaker - Valerie Thomas]]> image/jpeg 1669735110 2022-11-29 15:18:30 1669735118 2022-11-29 15:18:38 <![CDATA[Teams Link for Virtual Participants]]> <![CDATA[Event Listing via GT Calendar]]>
<![CDATA[SCL January 2023 Supply Chain Days]]> 27233 Georgia Tech Supply Chain students, please join us for spring Supply Chain Days! We will be hosting both an On Campus (Jan 26) and a Virtual session (Jan 27). Please note that you need to register separately for each event to attend.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).
 

EVENT DETAILS

On Campus/In-Person (ISyE Main Building Atrium)

Thursday, Jan 26 | 11am - 2pm ET

Virtual/Online (Career Fair Plus)

Friday, Jan 27 | 9am - 3pm ET

 

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday for a list of attending organizations and links to register.

 

]]> Andy Haleblian 1 1668698006 2022-11-17 15:13:26 1668698021 2022-11-17 15:13:41 0 0 event Georgia Tech Supply Chain students, please join us for our spring Supply Chain Days! We will be hosting both an On Campus (Nov 1) and a Virtual session (Nov 2). Please note that you need to register separately for each event to attend.

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2023-01-26T12:00:00-05:00 2023-01-27T16:00:00-05:00 2023-01-27T16:00:00-05:00 2023-01-26 17:00:00 2023-01-27 21:00:00 2023-01-27 21:00:00 2023-01-26T12:00:00-05:00 2023-01-27T16:00:00-05:00 America/New_York America/New_York datetime 2023-01-26 12:00:00 2023-01-27 04:00:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

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663277 663277 image <![CDATA[SCL January 2022 Supply Chain Days]]> image/jpeg 1668697961 2022-11-17 15:12:41 1668697961 2022-11-17 15:12:41 <![CDATA[Register online to attend (for Georgia Tech students)]]> <![CDATA[Supply Chain and Logistics Institute website]]>
<![CDATA[ISyE Seminar - Sophie Yu]]> 34977 Title:

Efficient network alignment at Otter's tree-counting threshold via counting chandeliers
 

Abstract:

Given a pair of networks, the problem of network alignment or graph matching refers to finding the underlying vertex correspondence that maximally aligns the edges. This is a ubiquitous problem arising in a variety of applications across diverse fields, such as network privacy, computational biology, computer vision, and natural language processing.  Network alignment is an instance of the notoriously difficult quadratic assignment problem (QAP), which is NP-hard to solve or approximate.

Despite the worst-case computational hardness of QAP, I will present a computationally efficient network alignment algorithm based on counting a special family of trees. When the two networks are Erdős–Rényi random graphs with correlated edges through the hidden vertex correspondence, we show that our algorithm correctly matches all but a vanishing fraction of vertices with high probability as soon as the edge correlation exceeds the square root of Otter's constant. Moreover, we further upgrade the almost exact recovery to exact recovery whenever it is information-theoretically possible. This is the first polynomial-time algorithm that achieves exact and almost exact matching with an explicit constant correlation for both dense and sparse networks. 

Here is the paper link: https://arxiv.org/pdf/2209.12313.pdf


Bio: 

Sophie Yu is a fifth-year Ph.D. Candidate in the field of Decision Sciences in the Fuqua School of Business at Duke University, under Prof. Jiaming Xu. She visited the Simons Institute for the Theory of Computing as a visiting graduate student in Fall 2021. She received her MS in statistical and economic modeling under Prof. Jerry Reiter from Duke University in 2017, and her BS in Economics from Renmin University of China in 2015.

Her research interests focus on data analysis, algorithm design, and performance evaluation in large-scale networks and stochastic systems. Her works draw inspiration from real-world business, engineering, and natural sciences problems that can be modeled into large and complex networks. She has explored a range of topics, from the fundamental limits and efficient algorithms on network alignment to online platform policy design with bounded regret and data confidentiality protection. 

]]> Julie Smith 1 1668606488 2022-11-16 13:48:08 1668606488 2022-11-16 13:48:08 0 0 event Abstract:

Given a pair of networks, the problem of network alignment or graph matching refers to finding the underlying vertex correspondence that maximally aligns the edges. This is a ubiquitous problem arising in a variety of applications across diverse fields, such as network privacy, computational biology, computer vision, and natural language processing.  Network alignment is an instance of the notoriously difficult quadratic assignment problem (QAP), which is NP-hard to solve or approximate.

Despite the worst-case computational hardness of QAP, I will present a computationally efficient network alignment algorithm based on counting a special family of trees. When the two networks are Erdős–Rényi random graphs with correlated edges through the hidden vertex correspondence, we show that our algorithm correctly matches all but a vanishing fraction of vertices with high probability as soon as the edge correlation exceeds the square root of Otter's constant. Moreover, we further upgrade the almost exact recovery to exact recovery whenever it is information-theoretically possible. This is the first polynomial-time algorithm that achieves exact and almost exact matching with an explicit constant correlation for both dense and sparse networks. 

Here is the paper link: https://arxiv.org/pdf/2209.12313.pdf

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2022-12-05T12:00:00-05:00 2022-12-05T13:00:00-05:00 2022-12-05T13:00:00-05:00 2022-12-05 17:00:00 2022-12-05 18:00:00 2022-12-05 18:00:00 2022-12-05T12:00:00-05:00 2022-12-05T13:00:00-05:00 America/New_York America/New_York datetime 2022-12-05 12:00:00 2022-12-05 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Hansheng Jiang]]> 34977 Title:

Pricing Analytics Under Heterogeneous Consumer Behaviors

 

Abstract:

We consider intertemporal pricing in the presence of reference effects and consumer heterogeneity. Our research question encompasses how to estimate heterogeneous consumer reference effects from data and how to efficiently compute the optimal pricing policy. Understanding reference effects is essential for designing pricing policies in modern retailing. Our work contributes to this area by incorporating consumer heterogeneity under arbitrary distributions. We propose a mixed logit demand model that allows arbitrary joint distributions of valuations, responsiveness to prices, and responsiveness to reference prices among consumers. We use a nonparametric estimation method to learn consumer heterogeneity from transaction data. Further, we formulate the pricing optimization as an infinite horizon dynamic programming problem and solve it by applying a modified policy iteration algorithm. Moreover, we investigate the structure of optimal pricing policies and prove the sub-optimality of constant pricing policies even when all consumers are loss-averse according to the classical definition. Our numerical studies show that our estimation and optimization framework improves the expected revenue of retailers via accounting for heterogeneity. We validate our model using real data from JD.com, a large E-commerce retailer, and find empirical evidence of consumer heterogeneity. In practice, ignoring consumer heterogeneity may lead to a significant loss of revenue. Furthermore, heterogeneous reference effects offer a strong motive for promotions and price fluctuations.

 

Bio:

Hansheng Jiang is a final-year Ph.D. candidate in the Department of Industrial Engineering and Operations Research at UC Berkeley, co-advised by Zuo-Jun Max Shen and Aditya Guntuboyina. Her research focuses on developing methodologies and algorithms for sequential and data-driven decision-making, especially in the presence of human behaviors. Her recent works address real-world problems in retailing platforms, on-demand shared mobility systems, and supply chain management. She is a recipient of the Berkeley Fellowship, a winner of the IISA best student paper award in theory and methodology, and a finalist of the MSOM data-driven research challenge.

]]> Julie Smith 1 1668433694 2022-11-14 13:48:14 1668455669 2022-11-14 19:54:29 0 0 event Abstract:

We consider intertemporal pricing in the presence of reference effects and consumer heterogeneity. Our research question encompasses how to estimate heterogeneous consumer reference effects from data and how to efficiently compute the optimal pricing policy. Understanding reference effects is essential for designing pricing policies in modern retailing. Our work contributes to this area by incorporating consumer heterogeneity under arbitrary distributions. We propose a mixed logit demand model that allows arbitrary joint distributions of valuations, responsiveness to prices, and responsiveness to reference prices among consumers. We use a nonparametric estimation method to learn consumer heterogeneity from transaction data. Further, we formulate the pricing optimization as an infinite horizon dynamic programming problem and solve it by applying a modified policy iteration algorithm. Moreover, we investigate the structure of optimal pricing policies and prove the sub-optimality of constant pricing policies even when all consumers are loss-averse according to the classical definition. Our numerical studies show that our estimation and optimization framework improves the expected revenue of retailers via accounting for heterogeneity. We validate our model using real data from JD.com, a large E-commerce retailer, and find empirical evidence of consumer heterogeneity. In practice, ignoring consumer heterogeneity may lead to a significant loss of revenue. Furthermore, heterogeneous reference effects offer a strong motive for promotions and price fluctuations.

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2022-11-28T12:00:00-05:00 2022-11-28T13:00:00-05:00 2022-11-28T13:00:00-05:00 2022-11-28 17:00:00 2022-11-28 18:00:00 2022-11-28 18:00:00 2022-11-28T12:00:00-05:00 2022-11-28T13:00:00-05:00 America/New_York America/New_York datetime 2022-11-28 12:00:00 2022-11-28 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE - Building ]]>
<![CDATA[ISyE Statistics Seminar - Jay Batroff]]> 36358 Bio: Jay Bartroff joined the University of Texas at Austin's Statistics & Data Sciences Department in January 2022 as Professor and Associate Chair. Prior to that he was Professor of Mathematics and Vice-Chair for Statistics at the University of Southern California for 15 years.  Before that he was an NSF postdoc in the Stanford Statistics Department, following his PhD at Caltech and his undergraduate degree at U.C. Berkeley.  His research interests include sequential analysis, multiple testing, Stein's method, and a variety of biomedical applications including clinical trial design and methods for wearable alcohol biosensors. Jay's research has been supported by the NSF, NIH, FDA, and NSA.  His publications include a textbook on sequential methods coauthored with Lai and Shih, published by Springer.

 

Abstract: We present an efficient method of calculating exact confidence intervals for the hypergeometric number of successes. The method inverts minimum-width acceptance intervals after shifting them to make their endpoints nondecreasing while preserving their level. The resulting set of confidence intervals achieves minimum possible average width, and even in comparison with confidence sets not required to be intervals it attains the minimum possible cardinality most of the time, and always within 1. The method compares favorably with existing methods not only in the size of the intervals but also in the time required to compute them. A similar approach can be taken for optimal confidence intervals for an unknown population size, such as in capture-recapture problems.

]]> chumphrey30 1 1666191302 2022-10-19 14:55:02 1667923324 2022-11-08 16:02:04 0 0 event Bio: Jay Bartroff joined the University of Texas at Austin's Statistics & Data Sciences Department in January 2022 as Professor and Associate Chair. Prior to that he was Professor of Mathematics and Vice-Chair for Statistics at the University of Southern California for 15 years.  Before that he was an NSF postdoc in the Stanford Statistics Department, following his PhD at Caltech and his undergraduate degree at U.C. Berkeley.  His research interests include sequential analysis, multiple testing, Stein's method, and a variety of biomedical applications including clinical trial design and methods for wearable alcohol biosensors. Jay's research has been supported by the NSF, NIH, FDA, and NSA.  His publications include a textbook on sequential methods coauthored with Lai and Shih, published by Springer.

 

Abstract: We present an efficient method of calculating exact confidence intervals for the hypergeometric number of successes. The method inverts minimum-width acceptance intervals after shifting them to make their endpoints nondecreasing while preserving their level. The resulting set of confidence intervals achieves minimum possible average width, and even in comparison with confidence sets not required to be intervals it attains the minimum possible cardinality most of the time, and always within 1. The method compares favorably with existing methods not only in the size of the intervals but also in the time required to compute them. A similar approach can be taken for optimal confidence intervals for an unknown population size, such as in capture-recapture problems.

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2022-11-10T13:00:00-05:00 2022-11-10T14:00:00-05:00 2022-11-10T14:00:00-05:00 2022-11-10 18:00:00 2022-11-10 19:00:00 2022-11-10 19:00:00 2022-11-10T13:00:00-05:00 2022-11-10T14:00:00-05:00 America/New_York America/New_York datetime 2022-11-10 01:00:00 2022-11-10 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Statistics Seminar- Ming Yuan]]> 36358 Bio: Ming Yuan is Professor of Statistics at Columbia University. He was previously Senior Investigator in Virology at Morgridge Institute for Research and Professor of Statistics at University of Wisconsin at Madison, and prior to that Coca-Cola Junior Professor of Industrial and Systems Engineering at Georgia Institute of Technology. His research and teaching interests lie broadly in statistics and its interface with other quantitative and computational fields such as optimization, machine learning, computational biology and financial engineering. He has over 100 scientific publications in applied mathematics, computer science, electrical engineering, financial econometrics, medical informations, optimization, and statistics among others.

He has served as the program secretary of the Institute for Mathematical Statistics (IMS), and was a member of the advisory board for the Quality, Statistics and Reliability (QSR) section of the Institute for Operations Research and the Management Sciences (INFORMS). He is also a co-Editor of The Annals of Statistics and has been serving on numerous editorial boards. He was named a Medallion Lecturer of IMS in 2018, and a recipient of the John van Ryzin Award (2004; International Biometrics Society), CAREER Award (2009; US National Science Foundation), the Guy Medal in Bronze (2014; Royal Statistical Society), and the Leo Breiman Junior Researcher Award (2017; the Statistical Learning and Data Mining section of the American Statistical Association).

 

 

Abstract: Matrix perturbation bounds developed by Weyl, Davis, Kahan and Wedin and others play a central role in many statistical and machine learning problems. I shall discuss some of the recent progresses in developing similar bounds for higher order tensors. I will highlight the intriguing differences from matrices, and explore their implications in spectral learning problems.

]]> chumphrey30 1 1667578450 2022-11-04 16:14:10 1667923285 2022-11-08 16:01:25 0 0 event Bio: Ming Yuan is Professor of Statistics at Columbia University. He was previously Senior Investigator in Virology at Morgridge Institute for Research and Professor of Statistics at University of Wisconsin at Madison, and prior to that Coca-Cola Junior Professor of Industrial and Systems Engineering at Georgia Institute of Technology. His research and teaching interests lie broadly in statistics and its interface with other quantitative and computational fields such as optimization, machine learning, computational biology and financial engineering. He has over 100 scientific publications in applied mathematics, computer science, electrical engineering, financial econometrics, medical informations, optimization, and statistics among others.

He has served as the program secretary of the Institute for Mathematical Statistics (IMS), and was a member of the advisory board for the Quality, Statistics and Reliability (QSR) section of the Institute for Operations Research and the Management Sciences (INFORMS). He is also a co-Editor of The Annals of Statistics and has been serving on numerous editorial boards. He was named a Medallion Lecturer of IMS in 2018, and a recipient of the John van Ryzin Award (2004; International Biometrics Society), CAREER Award (2009; US National Science Foundation), the Guy Medal in Bronze (2014; Royal Statistical Society), and the Leo Breiman Junior Researcher Award (2017; the Statistical Learning and Data Mining section of the American Statistical Association).

 

Abstract: Matrix perturbation bounds developed by Weyl, Davis, Kahan and Wedin and others play a central role in many statistical and machine learning problems. I shall discuss some of the recent progresses in developing similar bounds for higher order tensors. I will highlight the intriguing differences from matrices, and explore their implications in spectral learning problems.

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2022-11-17T13:00:00-05:00 2022-11-17T14:00:00-05:00 2022-11-17T14:00:00-05:00 2022-11-17 18:00:00 2022-11-17 19:00:00 2022-11-17 19:00:00 2022-11-17T13:00:00-05:00 2022-11-17T14:00:00-05:00 America/New_York America/New_York datetime 2022-11-17 01:00:00 2022-11-17 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar - Mark Squillante]]> 36374 Title:

Optimal Transport and Minimax Optimization

Abstract:

We first consider a motivational perspective on the mathematical foundations of learning and decision making, spanning a broad spectrum from various fields of mathematics through various applications of learning and decision making. Within this context, we then focus on some recent advances in optimal transport, probability, minimax optimization and computational methods that support the mathematical foundations of learning. This includes a brief overview of minimax formulations and theoretical results for a couple of motivating problems related to statistical convergence and transfer learning. We next consider distributionally robust optimization formulations and solutions of motivating problems related to model generalization and input model uncertainty, together with associated theoretical results and efficient algorithms that balance fundamental tradeoffs between computation and stochastic error. Empirical results further demonstrate and quantify the significant benefits of our solution approaches over previous related work in learning model generalization and nonconvex portfolio choice modeling under cumulative prospect theory.

Bio:

Mark S. Squillante is a Distinguished Research Staff Member and the Manager of Foundations of Probability, Dynamics, and Control within the Mathematical Sciences of IBM Research at the Thomas J. Watson Research Center. He has been an adjunct faculty member in the School of Operations Research and Information Engineering at Cornell Tech and the School of Engineering and Applied Science at Columbia University. His research interests broadly concern mathematical foundations of the analysis, modeling and optimization of the design and control of complex systems under uncertainty, and their broad applications. Mark is an elected Fellow of INFORMS, ACM, IEEE and AAIA, and recipient of the (Biennial) Best Publication in Applied Probability Award (INFORMS Applied Probability Society), the Daniel H. Wagner Prize (INFORMS), 9 best paper awards, 27 major IBM technical awards, and 40 IBM invention awards. He currently serves as Editor-in-Chief of Stochastic Models, as Chair of IFIP Working Group 7.3, on the INFORMS Subdivisions Council, and on the Board of Directors of the American Automatic Control Council. He received a Ph.D. degree from the University of Washington.

]]> mwelch39 1 1667825392 2022-11-07 12:49:52 1667841895 2022-11-07 17:24:55 0 0 event Abstract:

We first consider a motivational perspective on the mathematical foundations of learning and decision making, spanning a broad spectrum from various fields of mathematics through various applications of learning and decision making. Within this context, we then focus on some recent advances in optimal transport, probability, minimax optimization and computational methods that support the mathematical foundations of learning. This includes a brief overview of minimax formulations and theoretical results for a couple of motivating problems related to statistical convergence and transfer learning. We next consider distributionally robust optimization formulations and solutions of motivating problems related to model generalization and input model uncertainty, together with associated theoretical results and efficient algorithms that balance fundamental tradeoffs between computation and stochastic error. Empirical results further demonstrate and quantify the significant benefits of our solution approaches over previous related work in learning model generalization and nonconvex portfolio choice modeling under cumulative prospect theory.

 

 

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2022-11-11T12:30:00-05:00 2022-11-11T13:30:00-05:00 2022-11-11T13:30:00-05:00 2022-11-11 17:30:00 2022-11-11 18:30:00 2022-11-11 18:30:00 2022-11-11T12:30:00-05:00 2022-11-11T13:30:00-05:00 America/New_York America/New_York datetime 2022-11-11 12:30:00 2022-11-11 01:30:00 America/New_York America/New_York datetime <![CDATA[Main 228]]>
<![CDATA[SCL Course: Category Management and Sourcing Leadership (Virtual/Instructor-led)]]> 27233 Course Description

Category Management and Sourcing Leadership is designed to deepen participants' knowledge base of core activities in the procurement & supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation & selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This "hands on" delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.

Who Should Attend

This course is ideal for sourcing initiative leaders, procurement professionals, project managers, finance analyst, contract managers and all procurement & supply management-related professionals involved with bid package development, bid package analysis, negotiations preparation, contracting and supplier selection activity.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1667830874 2022-11-07 14:21:14 1667830884 2022-11-07 14:21:24 0 0 event This course is designed to deepen participants' knowledge base of core activities in the procurement & supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation & selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This "hands on" delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.

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2023-03-27T14:00:00-04:00 2023-03-30T15:30:00-04:00 2023-03-30T15:30:00-04:00 2023-03-27 18:00:00 2023-03-30 19:30:00 2023-03-30 19:30:00 2023-03-27T14:00:00-04:00 2023-03-30T15:30:00-04:00 America/New_York America/New_York datetime 2023-03-27 02:00:00 2023-03-30 03:30:00 America/New_York America/New_York datetime <![CDATA[]]> EMAIL: info@scl.gatech.edu or CALL: (404) 385-3501 between 9:00a.m. and 4:00p.m., Eastern time.

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[ISyE Seminar - Julie Ivy, Ph.D]]> 36374 Title:

Does your model reflect the needs of the user? Meaningful Data-driven Multi-criteria Decision Modeling: Elicitation of Preference among Multiple Criteria in Food Distribution by Food Banks

Abstract:

Decision making to satisfy the basic human needs of health, food, and education is complex. In 2020, more than 38 million people, including 12 million children, in the U.S. were food insecure. By 2021, 53 million people sought help from food banks and community programs to feed their families. Food banks are nonprofit organizations that collect and distribute food donations to food-insecure populations in their service regions. Food banks are challenged with juggling multiple criteria such as equity, effectiveness, and efficiency when making distribution decisions. Models that assume predetermined weights on multiple criteria may produce inaccurate results as the preference of food banks over these criteria may vary over time, and as a function of supply and demand. In collaboration with our food bank partner in North Carolina, we develop a single-period, weighted multi-criteria optimization model that provides the decision-maker the flexibility to capture their preferences over the three criteria of equity, effectiveness, and efficiency, and explore the resulting trade-offs. We introduce a novel algorithm to elicit the inherent preference of a food bank by analyzing its actions within a single-period. The algorithm does not require direct interaction with the decision-maker. The non-interactive nature of this algorithm is especially significant for humanitarian organizations such as food banks which lack the resources to interact with modelers on a regular basis. We explore the implications of different decision-maker preferences for the criteria on distribution policies.

Bio:

Julie Simmons Ivy, Ph.D., is a Professor and Fitts Faculty Fellow of Health Systems Engineering in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University with extensive background in decision making under conditions of uncertainty using stochastic and statistical modeling. She received her B.S. and Ph.D. in Industrial and Operations Engineering from the University of Michigan. She also received her M.S. in Industrial and Systems Engineering from Georgia Tech. She is an active member of the Institute of Operations Research and Management Science (INFORMS), Dr. Ivy served as the 2007 Chair (President) of the INFORMS Health Applications Society and the 2012 – 13 President for the INFORMS Minority Issues Forum. Recently, Dr. Ivy was elected as a 2022 INFORMS Fellow. Dr. Ivy’s research seeks to model complex interactions and quantitatively capture the impact of different factors, objectives, system dynamics, intervention options and policies on outcomes with the goal of improving decision quality. In particular, Dr. Ivy has extensive background in the application of systems science methods, including the analysis and modeling of large data sets, to hunger relief and health decision making. This research has made an impact on how researchers and practitioners address complex societal issues, such as health disparities, public health preparedness, hunger relief, student performance, and personalized medical decision-making and has been funded by the CDC, NSF, and NIH.

]]> mwelch39 1 1666611844 2022-10-24 11:44:04 1666611844 2022-10-24 11:44:04 0 0 event Abstract:

Decision making to satisfy the basic human needs of health, food, and education is complex. In 2020, more than 38 million people, including 12 million children, in the U.S. were food insecure. By 2021, 53 million people sought help from food banks and community programs to feed their families. Food banks are nonprofit organizations that collect and distribute food donations to food-insecure populations in their service regions. Food banks are challenged with juggling multiple criteria such as equity, effectiveness, and efficiency when making distribution decisions. Models that assume predetermined weights on multiple criteria may produce inaccurate results as the preference of food banks over these criteria may vary over time, and as a function of supply and demand. In collaboration with our food bank partner in North Carolina, we develop a single-period, weighted multi-criteria optimization model that provides the decision-maker the flexibility to capture their preferences over the three criteria of equity, effectiveness, and efficiency, and explore the resulting trade-offs. We introduce a novel algorithm to elicit the inherent preference of a food bank by analyzing its actions within a single-period. The algorithm does not require direct interaction with the decision-maker. The non-interactive nature of this algorithm is especially significant for humanitarian organizations such as food banks which lack the resources to interact with modelers on a regular basis. We explore the implications of different decision-maker preferences for the criteria on distribution policies.

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2022-10-28T12:30:00-04:00 2022-10-28T13:30:00-04:00 2022-10-28T13:30:00-04:00 2022-10-28 16:30:00 2022-10-28 17:30:00 2022-10-28 17:30:00 2022-10-28T12:30:00-04:00 2022-10-28T13:30:00-04:00 America/New_York America/New_York datetime 2022-10-28 12:30:00 2022-10-28 01:30:00 America/New_York America/New_York datetime <![CDATA[Main 228]]>
<![CDATA[ISyE Statistics Seminar Speaker- Ralph C. Smith, North Carolina State University ]]> 36358 Abstract: For many complex physical and biological models, the computational cost of high-fidelity simulation codes precludes their direct use for Bayesian model calibration and uncertainty propagation.  For example, nuclear power plant codes can take hours to days for a single run.  Furthermore, the models often have tens to thousands of inputs -- comprised of parameters, initial conditions, or boundary conditions -- many of which are unidentifiable in the sense that they cannot be uniquely determined using measured responses. In this presentation, we will discuss techniques to isolate influential inputs for subsequent surrogate model construction for Bayesian inference and uncertainty propagation.  For input selection, we will discuss advantages and shortcomings of global sensitivity analysis to isolate influential inputs and detail the use of parameter subset selection and active subspace techniques to determine low-dimensional input spaces.  We will also discuss the manner in which Bayesian calibration on active subspaces can be used to quantify uncertainties in physical parameters.  These techniques will be illustrated for models arising in nuclear power plant design and quantitative systems pharmacology (QSP), as well as models for transductive materials.

 

 

Biography: Ralph C. Smith joined the North Carolina State University faculty in 1998 where he is presently a Distinguished University Professor of Mathematics.  He is co-author of the research monograph Smart Material Structures: Modeling, Estimation and Control and author of the books Smart Material Systems: Model Development and Uncertainty Quantification: Theory, Implementation, and Applications.  He is on the editorial boards of the Journal of Intelligent Material Systems and Structures and the SIAM/ASA Journal on Uncertainty Quantification. He is the recipient of the 2016 ASME Adaptive Structures and Material Systems Prize and the SPIE 2017 Smart Structures and Materials Lifetime Achievement, and he was named a SIAM Fellow in 2018. His research areas include mathematical modeling of smart material systems, numerical analysis and methods for physical systems, Bayesian model calibration, sensitivity analysis, control, and uncertainty quantification for physical and biological systems.

]]> chumphrey30 1 1666191788 2022-10-19 15:03:08 1666191788 2022-10-19 15:03:08 0 0 event Abstract: For many complex physical and biological models, the computational cost of high-fidelity simulation codes precludes their direct use for Bayesian model calibration and uncertainty propagation.  For example, nuclear power plant codes can take hours to days for a single run.  Furthermore, the models often have tens to thousands of inputs -- comprised of parameters, initial conditions, or boundary conditions -- many of which are unidentifiable in the sense that they cannot be uniquely determined using measured responses. In this presentation, we will discuss techniques to isolate influential inputs for subsequent surrogate model construction for Bayesian inference and uncertainty propagation.  For input selection, we will discuss advantages and shortcomings of global sensitivity analysis to isolate influential inputs and detail the use of parameter subset selection and active subspace techniques to determine low-dimensional input spaces.  We will also discuss the manner in which Bayesian calibration on active subspaces can be used to quantify uncertainties in physical parameters.  These techniques will be illustrated for models arising in nuclear power plant design and quantitative systems pharmacology (QSP), as well as models for transductive materials.

 

 

Biography: Ralph C. Smith joined the North Carolina State University faculty in 1998 where he is presently a Distinguished University Professor of Mathematics.  He is co-author of the research monograph Smart Material Structures: Modeling, Estimation and Control and author of the books Smart Material Systems: Model Development and Uncertainty Quantification: Theory, Implementation, and Applications.  He is on the editorial boards of the Journal of Intelligent Material Systems and Structures and the SIAM/ASA Journal on Uncertainty Quantification. He is the recipient of the 2016 ASME Adaptive Structures and Material Systems Prize and the SPIE 2017 Smart Structures and Materials Lifetime Achievement, and he was named a SIAM Fellow in 2018. His research areas include mathematical modeling of smart material systems, numerical analysis and methods for physical systems, Bayesian model calibration, sensitivity analysis, control, and uncertainty quantification for physical and biological systems.

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2022-11-03T13:00:00-04:00 2022-11-03T14:00:00-04:00 2022-11-03T14:00:00-04:00 2022-11-03 17:00:00 2022-11-03 18:00:00 2022-11-03 18:00:00 2022-11-03T13:00:00-04:00 2022-11-03T14:00:00-04:00 America/New_York America/New_York datetime 2022-11-03 01:00:00 2022-11-03 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[SCL IRC Seminar: The Logistics of Space Exploration]]> 27233 The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested faculty, students and corporate partners as well as the general public. If you are interested in attending any of the sessions, please review the below information and register online.

SESSION OVERVIEW

In this talk, we will discuss the recent progress in logistics-inspired modeling and optimization for space mission design. We will demonstrate several examples of the applications of mathematical optimization to spacecraft and mission design. We will further discuss the methods to evaluate and analyze the design and operational strategies for in-space infrastructure systems in the contexts of human/robotic space exploration, on-orbit servicing, and satellite constellation missions.

SESSION SPEAKER

Koki Ho, Associate Professor, Aerospace Engineering

Register Online for upcoming SCL IRC seminars

In-person attendance to our SCL IRC sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program. If you are a member of the general public attending in-person, the cost to attend is $5 per session which includes a boxed lunch*. Virtual attendance is always free.

Please see our registration page relating to taking advantage of the optional in-person lunch.

If you have any questions, please email event@scl.gatech.edu.

]]> Andy Haleblian 1 1665777145 2022-10-14 19:52:25 1666119526 2022-10-18 18:58:46 0 0 event The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested SCL faculty, students and corporate partners as well as the general public.

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2022-11-17T13:00:00-05:00 2022-11-17T14:30:00-05:00 2022-11-17T14:30:00-05:00 2022-11-17 18:00:00 2022-11-17 19:30:00 2022-11-17 19:30:00 2022-11-17T13:00:00-05:00 2022-11-17T14:30:00-05:00 America/New_York America/New_York datetime 2022-11-17 01:00:00 2022-11-17 02:30:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

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662166 662166 image <![CDATA[SCL IRC Seminar: The Logistics of Space Exploration]]> image/jpeg 1665780697 2022-10-14 20:51:37 1666119545 2022-10-18 18:59:05 <![CDATA[Register Online for upcoming SCLIRC seminars]]>
<![CDATA[ISyE Seminar - Carri Chan]]> 36374 TITLE:

Prediction-Driven Surge Planning with Application in the Emergency Department

ABSTRACT: Determining emergency department (ED) nurse staffing decisions to balance the quality of service and staffing cost can be extremely challenging, especially when there is a high level of uncertainty in patient-demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by utilizing demand forecasts. In this work, we study a two-stage prediction- driven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus demand stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Lastly, we develop a unified framework that combines parameter estimation, real- time demand forecasts, and capacity sizing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework can reduce annual staffing costs by 11%–16% ($2 M–$3 M) while guaranteeing timely access to care. Joint work with Yue Hu and Jing Dong.

Bio: Carri W. Chan is the John A. Howard Professor of Business and the Faculty Director of the Healthcare and Pharmaceutical Management Program at Columbia Business School. Her research is in the area of healthcare operations management. Her primary focus is in data-driven modeling of complex stochastic systems, efficient algorithmic design for queuing systems, dynamic control of stochastic processing systems, and econometric analysis of healthcare systems. Her research combines empirical and stochastic modeling to develop evidence-based approaches to improve patient flow through hospitals. She has worked with clinicians and administrators in numerous hospital systems including Northern California Kaiser Permanente, New York Presbyterian, and Montefiore Medical Center. She is the recipient of a 2014 NSF CAREER award, the 2016 POMS Wickham Skinner Early Career Award, and the 2019 MSOM Young Scholar Prize. She currently serves as a co-Department Editor for the Healthcare Management Department at Management Science. She received her BS in electrical engineering from MIT and MS and Ph.D. in electrical engineering from Stanford University.

 

 

 

]]> mwelch39 1 1666012361 2022-10-17 13:12:41 1666012361 2022-10-17 13:12:41 0 0 event Determining emergency department (ED) nurse staffing decisions to balance the quality of service and staffing cost can be extremely challenging, especially when there is a high level of uncertainty in patient-demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by utilizing demand forecasts. In this work, we study a two-stage prediction- driven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus demand stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Lastly, we develop a unified framework that combines parameter estimation, real- time demand forecasts, and capacity sizing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework can reduce annual staffing costs by 11%–16% ($2 M–$3 M) while guaranteeing timely access to care. Joint work with Yue Hu and Jing Dong.

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2022-11-04T12:30:00-04:00 2022-11-04T13:30:00-04:00 2022-11-04T13:30:00-04:00 2022-11-04 16:30:00 2022-11-04 17:30:00 2022-11-04 17:30:00 2022-11-04T12:30:00-04:00 2022-11-04T13:30:00-04:00 America/New_York America/New_York datetime 2022-11-04 12:30:00 2022-11-04 01:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[Ad Hoc Seminar - Professor Jean Pauphilet]]> 36374 Title:

Hospital-wide Inpatient Flow: Optimization vs. Recommendation

Abstract:

 In an attempt to coordinate and optimize hospital operations across all services in real-time, we develop a multistage adaptive robust optimization model, informed by data and ML predictions, that unifies the entire bed assignment process while accounting for present and future inpatient flows, discharges, and bed requests. On simulations calibrated for a 600-bed institution, our optimization model was solved in seconds, reduced off-service placement by 24% on average, and boarding delays by 31%-46%.
If deployed in the hospital, however, the benefit will likely be much lower. Among others, the fact that nurses can override the recommendation made by our algorithm can negatively impact performance. In the second half of the talk, we will theoretically study the extent to which this partial adherence phenomenon (a) impacts performance, and (b) should influence the design of the algorithmic recommendation in the first place. Indeed, the best decisions are not necessarily the best advice.

Bio:

Jean is an Assistant Professor of Management Science and Operations at London Business School. His research focuses on large-scale discrete optimization, robust optimization, and machine learning, with applications to healthcare operations. His work has been published in the likes of Operations Research, Mathematical Programming, and M&SOM, and recognized by many awards, including the INFORMS Pierskalla, George E. Nicholson, and Computing Society best student paper awards. Jean received a Ph.D. in Operations Research from MIT and a Diplôme d'ingénieur from Ecole Polytechnique (Paris). 

]]> mwelch39 1 1665073409 2022-10-06 16:23:29 1665073409 2022-10-06 16:23:29 0 0 event  In an attempt to coordinate and optimize hospital operations across all services in real-time, we develop a multistage adaptive robust optimization model, informed by data and ML predictions, that unifies the entire bed assignment process while accounting for present and future inpatient flows, discharges, and bed requests. On simulations calibrated for a 600-bed institution, our optimization model was solved in seconds, reduced off-service placement by 24% on average, and boarding delays by 31%-46%.
If deployed in the hospital, however, the benefit will likely be much lower. Among others, the fact that nurses can override the recommendation made by our algorithm can negatively impact performance. In the second half of the talk, we will theoretically study the extent to which this partial adherence phenomenon (a) impacts performance, and (b) should influence the design of the algorithmic recommendation in the first place. Indeed, the best decisions are not necessarily the best advice.
 

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2022-10-11T12:00:00-04:00 2022-10-11T13:00:00-04:00 2022-10-11T13:00:00-04:00 2022-10-11 16:00:00 2022-10-11 17:00:00 2022-10-11 17:00:00 2022-10-11T12:00:00-04:00 2022-10-11T13:00:00-04:00 America/New_York America/New_York datetime 2022-10-11 12:00:00 2022-10-11 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building]]>
<![CDATA[ISyE Seminar - Prof. J. Cole Smith]]> 36374 Title:

Asymmetric Stochastic Shortest-Path Interdiction Favoring the Evader

 
Abstract: 

This work was completed with Dr. Di Nguyen, a professor at University College Dublin. We discuss a two-stage shortest-path interdiction problem between an interdictor and an evader, in which the cost for an evader to use each arc is given by the arc’s base cost plus an additional cost if the arc is attacked by the interdictor. The interdictor acts first to attack a subset of arcs, and then the evader traverses the network using a shortest path. In the problem we study, the interdictor does not know the exact value of each base cost, but instead only knows the (nonnegative uniform) distributions of each arc’s base cost. The evader observes both the subset of arcs attacked by the interdictor and the true base cost values before traversing the network, and is thus at an advantage. The interdictor seeks to maximize evader’s shortest-path costs, but the choice of objective is a key consideration. We examine ideas underscoring how the interdictor could maximize the expected objective that an evader will incur, and then more generally explore the maximization of the evader’s conditional value-at-risk, given some specified risk parameter. 
 
Bio sketch:

 Dr. J. Cole Smith is Dean of the College of Engineering and Computer Science at Syracuse University. Prior to that role, he served as an Associate Provost for Academic Initiatives and as Department Chair of Industrial Engineering at Clemson University. His research regards mathematical optimization models and algorithms, especially those arising in combinatorial optimization. Dr. Smith’s awards include the Young Investigator Award from the ONR, the Hamid K. Elden Outstanding Young Industrial Engineer in Education award, the Operations Research Division Teaching Award, the 2014 Glover-Klingman prize for best paper in Networks, and the best paper award from IIE Transactions in 2007. He became a Fellow of IISE in 2018, and serves as the INFORMS Vice President of Publications.

 

]]> mwelch39 1 1664807130 2022-10-03 14:25:30 1665067581 2022-10-06 14:46:21 0 0 event Abstract: 

This work was completed with Dr. Di Nguyen, a professor at University College Dublin. We discuss a two-stage shortest-path interdiction problem between an interdictor and an evader, in which the cost for an evader to use each arc is given by the arc’s base cost plus an additional cost if the arc is attacked by the interdictor. The interdictor acts first to attack a subset of arcs, and then the evader traverses the network using a shortest path. In the problem we study, the interdictor does not know the exact value of each base cost, but instead only knows the (nonnegative uniform) distributions of each arc’s base cost. The evader observes both the subset of arcs attacked by the interdictor and the true base cost values before traversing the network, and is thus at an advantage. The interdictor seeks to maximize evader’s shortest-path costs, but the choice of objective is a key consideration. We examine ideas underscoring how the interdictor could maximize the expected objective that an evader will incur, and then more generally explore the maximization of the evader’s conditional value-at-risk, given some specified risk parameter. 

 

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2022-10-07T12:30:00-04:00 2022-10-07T13:30:00-04:00 2022-10-07T13:30:00-04:00 2022-10-07 16:30:00 2022-10-07 17:30:00 2022-10-07 17:30:00 2022-10-07T12:30:00-04:00 2022-10-07T13:30:00-04:00 America/New_York America/New_York datetime 2022-10-07 12:30:00 2022-10-07 01:30:00 America/New_York America/New_York datetime <![CDATA[Main 228]]>
<![CDATA[ISyE Seminar - Vineet Goyal]]> 34977 Title:

From Disjoint Bilinear Optimization to Affine Policies in Dynamic Robust Optimization

Abstract:

Affine policies are widely used as a solution approach in dynamic robust optimization where computing an optimal adjustable solution is usually intractable. While the worst case performance of affine policies can be significantly bad, the empirical performance is observed to be near-optimal for a large class of problem instances. This work aims to address this stark-contrast between the worst-case and the empirical performance of affine policies.

In particular, we study the performance of affine policies for a two-stage adjustable robust optimization problem under an important class of uncertainty sets, namely the budget of uncertainty and intersection of budget of uncertainty sets. We show that surprisingly affine policies provide nearly the best possible approximation matching the hardness of approximation for this class of uncertainty sets. Our analysis is based on first designing an LP based approximation for a general disjoint bilinear optimization problem over packing polytopes (the separation problem for our two-stage problem). Based on this, we present an LP-restriction for the two-stage problem that we relate to affine policies  and show that it gives an $O(\log n \log L/(\log\log n \log\log L))$-approximation (where $n$ is the number of decision variables, and $L$ is the number of budget constraints describing the uncertainty set). This significantly improves over prior known bounds for the performance of affine policies and nearly matches the hardness of approximation. As a byproduct, we also obtain a significantly faster LP to compute near-optimal affine policies.

This talk is based on joint work with Ayoub Foussoul and Omar El Housni.

Bio:

Vineet Goyal is Associate Professor in the Industrial Engineering and Operations Research Department at Columbia University where he joined in 2010. He received his Bachelor's degree in Computer Science from Indian Institute of Technology, Delhi in 2003 and his Ph.D. in Algorithms, Combinatorics and Optimization (ACO) from Carnegie Mellon University in 2008. Before coming to Columbia, he spent two years as a Postdoctoral Associate at the Operations Research Center at MIT. He is interested in the design of efficient and robust data-driven algorithms for large scale dynamic optimization problems with applications in  revenue management and healthcare. He received the 2021 INFORMS Revenue Management and Pricing Section prize and 2019 MSOM Society Best Paper in Operations Research Prize. His research has been supported by grants from NSF, DARPA and the industry including the NSF CAREER Award and faculty research awards from Google, IBM, Adobe and Amazon.  

]]> Julie Smith 1 1664371619 2022-09-28 13:26:59 1664371619 2022-09-28 13:26:59 0 0 event Abstract:

Affine policies are widely used as a solution approach in dynamic robust optimization where computing an optimal adjustable solution is usually intractable. While the worst case performance of affine policies can be significantly bad, the empirical performance is observed to be near-optimal for a large class of problem instances. This work aims to address this stark-contrast between the worst-case and the empirical performance of affine policies.

]]>
2022-09-30T12:30:00-04:00 2022-09-30T13:30:00-04:00 2022-09-30T13:30:00-04:00 2022-09-30 16:30:00 2022-09-30 17:30:00 2022-09-30 17:30:00 2022-09-30T12:30:00-04:00 2022-09-30T13:30:00-04:00 America/New_York America/New_York datetime 2022-09-30 12:30:00 2022-09-30 01:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[SCL November 2022 Supply Chain Days]]> 27233 Georgia Tech Supply Chain students, please join us for our second fall Supply Chain Days! We will be hosting both an On Campus (Nov 1) and a Virtual session (Nov 2). Please note that you need to register separately for each event to attend.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).
 

EVENT DETAILS

On Campus/In-Person (ISyE Main Building Atrium)

Tuesday, Nov 1 | 10am - 1pm ET

Virtual/Online (Career Fair Plus)

Wednesday, Nov 2 | 9am - 3pm ET

 

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday for a list of attending organizations and links to register.

 

]]> Andy Haleblian 1 1663885455 2022-09-22 22:24:15 1663885461 2022-09-22 22:24:21 0 0 event Georgia Tech Supply Chain students, please join us for our spring Supply Chain Days! We will be hosting both an On Campus (Nov 1) and a Virtual session (Nov 2). Please note that you need to register separately for each event to attend.

]]>
2022-11-01T11:00:00-04:00 2022-11-02T16:00:00-04:00 2022-11-02T16:00:00-04:00 2022-11-01 15:00:00 2022-11-02 20:00:00 2022-11-02 20:00:00 2022-11-01T11:00:00-04:00 2022-11-02T16:00:00-04:00 America/New_York America/New_York datetime 2022-11-01 11:00:00 2022-11-02 04:00:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

]]>
661477 661477 image <![CDATA[SCL November 2022 Supply Chain Days]]> image/jpeg 1663885303 2022-09-22 22:21:43 1663885308 2022-09-22 22:21:48 <![CDATA[Register online to attend (for Georgia Tech students)]]> <![CDATA[Supply Chain and Logistics Institute website]]>
<![CDATA[ISyE Seminar - Angelia Nedich]]> 34977 Title:

Penalty Methods for Large-Scale Constrained Optimization Problems

Abstract:

The optimization problems with a large number of constraints are emerging in many application domains such as optimal control, reinforcement learning, and statistical learning, and artificial intelligence, in general. The challenges posed by the size of the problems in these applications resulted in prolific research in the domain of optimization theory and algorithms. Many refinements and accelerations of various (mainly) first-order methods have been proposed and studied, majority of which solves a penalized re-formulation of the original problem in order to cope with the large number of constraints. This talk will focus on problems with linear constraints and Huber-type penalty approach. Convergence behavior and efficiency of the algorithm will be addressed, as well as some supporting theory.

Bio:

Angelia Nedich has a Ph.D. from Moscow State University, Moscow, Russia, in Computational Mathematics and Mathematical Physics (1994), and a Ph.D. from Massachusetts Institute of Technology, Cambridge, USA in Electrical and Computer Science Engineering (2002). She has worked as a senior engineer in BAE Systems North America, Advanced Information Technology Division at Burlington, MA. Currently, she is a faculty member of the school of Electrical, Computer and Energy Engineering at Arizona State University at Tempe. Prior to joining Arizona State University, she has been a Willard Scholar faculty member at the University of Illinois at Urbana-Champaign. She is a recipient (jointly with her co-authors) of the Best Paper Award at the Winter Simulation Conference 2013 and the Best Paper Award at the International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt) 2015. Her general research interest is in optimization, large scale complex systems dynamics, variational inequalities and games.

]]> Julie Smith 1 1663247436 2022-09-15 13:10:36 1663247500 2022-09-15 13:11:40 0 0 event Abstract:

The optimization problems with a large number of constraints are emerging in many application domains such as optimal control, reinforcement learning, and statistical learning, and artificial intelligence, in general. The challenges posed by the size of the problems in these applications resulted in prolific research in the domain of optimization theory and algorithms. Many refinements and accelerations of various (mainly) first-order methods have been proposed and studied, majority of which solves a penalized re-formulation of the original problem in order to cope with the large number of constraints. This talk will focus on problems with linear constraints and Huber-type penalty approach. Convergence behavior and efficiency of the algorithm will be addressed, as well as some supporting theory.

]]>
2022-09-23T12:30:00-04:00 2022-09-23T13:30:00-04:00 2022-09-23T13:30:00-04:00 2022-09-23 16:30:00 2022-09-23 17:30:00 2022-09-23 17:30:00 2022-09-23T12:30:00-04:00 2022-09-23T13:30:00-04:00 America/New_York America/New_York datetime 2022-09-23 12:30:00 2022-09-23 01:30:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[SCL IRC Seminar: Smart Vehicle Data Collection and Spatial Analysis with ML for Green, Energy-efficient, Cost-effective and Safe Logistics]]> 27233 The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested faculty, students and corporate partners as well as the general public. If you are interested in attending any of the sessions, please review the below information and register online.

SESSION OVERVIEW

There are great opportunities to apply emerging technologies, including smart sensors, spatial-temporal analysis, and Artificial Intelligence to provide energy-efficient, eco-friendly, and safe transportation solutions. Dr. Tsai will present his research team’s work on collaborating with federal and state transportation agencies, as well as multinational automobile and logistics companies. This work aims to provide an integrated solution to monitor, predict, and optimize vehicle logistics, energy-emission efficiency, and safety by studying the interaction between vehicles and the transportation infrastructure using the developed GT smart data collection and advanced computing framework.

SESSION SPEAKER

Yi-Chang James Tsai, Professor, Civil and Environmental Engineering

Register Online for upcoming SCL IRC seminars

In-person attendance to our SCL IRC sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program. If you are a GT student who would like to attend in person and order lunch, we ask that you pay a $5 feeIf you are a member of the general public attending in-person, the cost to attend is $25 per session which includes a boxed lunch*. Virtual attendance is always free.

Please see our registration page relating to taking advantage of the optional in-person lunch.

If you have any questions, please email event@scl.gatech.edu.

]]> Andy Haleblian 1 1661975513 2022-08-31 19:51:53 1661976122 2022-08-31 20:02:02 0 0 event The Supply Chain and Logistics Institute hosts a series of monthly seminars open to interested SCL faculty, students and corporate partners as well as the general public. 

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2022-09-29T13:00:00-04:00 2022-09-29T14:30:00-04:00 2022-09-29T14:30:00-04:00 2022-09-29 17:00:00 2022-09-29 18:30:00 2022-09-29 18:30:00 2022-09-29T13:00:00-04:00 2022-09-29T14:30:00-04:00 America/New_York America/New_York datetime 2022-09-29 01:00:00 2022-09-29 02:30:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

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660753 660753 image <![CDATA[SCL IRC Seminar: Smart Vehicle Data Collection and Spatial Analysis]]> image/jpeg 1661975506 2022-08-31 19:51:46 1661975506 2022-08-31 19:51:46 <![CDATA[Register Online for upcoming SCLIRC seminars]]>
<![CDATA[ISyE Seminar - Michael Weylandt]]> 34977 Title:

Multivariate Analysis of Large-Scale Network Series

 

Abstract:

Networks are an increasingly common representation of real-world phenomena, able to succinctly describe social dynamics, communications infrastructure, genetic mechanisms, and more. In many applications, multiple views on the same network structure are available, each of which captures a different aspect of the same underlying phenomenon. For example, in multi-subject neuroimaging, independently estimated functional networks can be combined to identify common and generalizable patterns in the brain's response to stimuli. The scope and scale of these and other types of networks give rise to a host of computational and statistical challenges, taxing classical approaches with their ultra-high-dimensionality, small sample sizes, and expensive computation. In the first part of this talk, I develop a novel framework for principal components analysis of a population of networks based on a new class of semi-symmetric tensor decompositions. This Network PCA framework allows us to identify and isolate core patterns which capture network dynamics in a significantly reduced space, enabling more efficient computation and improved statistical estimation in downstream tasks. I also develop a new proof technique for tensors and higher-order power iterations to establish statistical consistency for these challenging non-convex optimization problems. I demonstrate the utility of this framework through applications to trend identification, variance analysis, and changepoint detection on an extended analysis of voting dynamics at the US Supreme Court. In the second half of this talk, I discuss several related problems in unsupervised statistical learning for multiple networks, highlighting my current and future research directions.

 

Bio:

Michael Weylandt is currently an Intelligence Community Postdoctoral Fellow, working with George Michailidis at the University of Florida. His work focuses on statistical machine learning methodology and computation for highly-structured data, with a particular focus on network data and time series. His work has been recognized with best paper awards from the American Statistical Association Sections in Statistical Learning and Data Science and in Business & Economic Statistics. He has served as a mentor in the Google Summer of Code program for 7 years on behalf of the R Foundation for Statistical Computing and previously held an NSF Graduate Research Fellowship. Prior to beginning his Ph.D. studies, he worked at Morgan Stanley as a quantitative analyst, focusing on derivatives pricing and financial risk management. He received a Bachelor's of Science in Engineering from Princeton University in 2008 and a Ph.D. in Statistics from Rice University in 2020.

]]> Julie Smith 1 1661866380 2022-08-30 13:33:00 1661866380 2022-08-30 13:33:00 0 0 event Abstract:

Networks are an increasingly common representation of real-world phenomena, able to succinctly describe social dynamics, communications infrastructure, genetic mechanisms, and more. In many applications, multiple views on the same network structure are available, each of which captures a different aspect of the same underlying phenomenon. For example, in multi-subject neuroimaging, independently estimated functional networks can be combined to identify common and generalizable patterns in the brain's response to stimuli. The scope and scale of these and other types of networks give rise to a host of computational and statistical challenges, taxing classical approaches with their ultra-high-dimensionality, small sample sizes, and expensive computation. In the first part of this talk, I develop a novel framework for principal components analysis of a population of networks based on a new class of semi-symmetric tensor decompositions. This Network PCA framework allows us to identify and isolate core patterns which capture network dynamics in a significantly reduced space, enabling more efficient computation and improved statistical estimation in downstream tasks. I also develop a new proof technique for tensors and higher-order power iterations to establish statistical consistency for these challenging non-convex optimization problems. I demonstrate the utility of this framework through applications to trend identification, variance analysis, and changepoint detection on an extended analysis of voting dynamics at the US Supreme Court. In the second half of this talk, I discuss several related problems in unsupervised statistical learning for multiple networks, highlighting my current and future research directions.

]]>
2022-09-06T12:00:00-04:00 2022-09-06T13:00:00-04:00 2022-09-06T13:00:00-04:00 2022-09-06 16:00:00 2022-09-06 17:00:00 2022-09-06 17:00:00 2022-09-06T12:00:00-04:00 2022-09-06T13:00:00-04:00 America/New_York America/New_York datetime 2022-09-06 12:00:00 2022-09-06 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Statistical Seminar Speaker- Tan Bui-Thanh]]> 36358 Bio

Tan Bui-Thanh is an associate professor, and the endowed William J Murray Jr. Fellow in Engineering No. 4, of the Oden Institute for Computational Engineering & Sciences, and the Department of Aerospace Engineering & Engineering mechanics at the university of Texas at Austin. Bui-Thanh obtained his PhD from the Massachusetts Institute of Technology in 2007, Master of Sciences from the Singapore MIT-Alliance in 2003, and Bachelor of Engineering from the Ho Chi Minh City University of Technology (DHBK) in 2001. He has decades of experience and expertise on multidisciplinary research across the boundaries of different branches of computational science, engineering, and mathematics. Bui-Thanh is a former elected vice president of the SIAM Texas-Louisiana Section, and currently the elected secretary of the SIAM SIAG/CSE. Bui-Thanh was an NSF early CAREER recipient, the Oden Institute distinguished research award, and a two-time winner of the Moncrief Faculty Challenging award.

 

 

Abstract

Deep Learning (DL) by design is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties (such as stability, conservation, and positivity) and desired accuracy need to be achieved. DL methods in their original forms are not capable of respecting the underlying mathematical models or achieving desired accuracy even in big-data regimes. On the other hand, many data-driven science and engineering problems, such as inverse problems, typically have limited experimental or observational data, and DL would overfit the data in this case. Leveraging information encoded in the underlying mathematical models, we argue, not only compensates missing information in low data regimes but also provides opportunities to equip DL methods with the underlying physics and hence obtaining higher accuracy. This talk introduces a Tikhonov Network (TNet) that is capable of learning Tikhonov regularized inverse problems. We present and provide intuitions for our formulations for general nonlinear problems. We rigorously show that our TNet approach can learn information encoded in the underlying mathematical models, and thus can produce consistent or equivalent inverse solutions, while naive purely data-based counterparts cannot. Furthermore, we theoretically study the error estimate between TNet and Tikhhonov inverse solutions and under which conditions they are the same. Extension to statistical inverse problems will also be presented.

]]> chumphrey30 1 1661355503 2022-08-24 15:38:23 1661355579 2022-08-24 15:39:39 0 0 event Bio

Tan Bui-Thanh is an associate professor, and the endowed William J Murray Jr. Fellow in Engineering No. 4, of the Oden Institute for Computational Engineering & Sciences, and the Department of Aerospace Engineering & Engineering mechanics at the university of Texas at Austin. Bui-Thanh obtained his PhD from the Massachusetts Institute of Technology in 2007, Master of Sciences from the Singapore MIT-Alliance in 2003, and Bachelor of Engineering from the Ho Chi Minh City University of Technology (DHBK) in 2001. He has decades of experience and expertise on multidisciplinary research across the boundaries of different branches of computational science, engineering, and mathematics. Bui-Thanh is a former elected vice president of the SIAM Texas-Louisiana Section, and currently the elected secretary of the SIAM SIAG/CSE. Bui-Thanh was an NSF early CAREER recipient, the Oden Institute distinguished research award, and a two-time winner of the Moncrief Faculty Challenging award.

 

 

Abstract

Deep Learning (DL) by design is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties (such as stability, conservation, and positivity) and desired accuracy need to be achieved. DL methods in their original forms are not capable of respecting the underlying mathematical models or achieving desired accuracy even in big-data regimes. On the other hand, many data-driven science and engineering problems, such as inverse problems, typically have limited experimental or observational data, and DL would overfit the data in this case. Leveraging information encoded in the underlying mathematical models, we argue, not only compensates missing information in low data regimes but also provides opportunities to equip DL methods with the underlying physics and hence obtaining higher accuracy. This talk introduces a Tikhonov Network (TNet) that is capable of learning Tikhonov regularized inverse problems. We present and provide intuitions for our formulations for general nonlinear problems. We rigorously show that our TNet approach can learn information encoded in the underlying mathematical models, and thus can produce consistent or equivalent inverse solutions, while naive purely data-based counterparts cannot. Furthermore, we theoretically study the error estimate between TNet and Tikhhonov inverse solutions and under which conditions they are the same. Extension to statistical inverse problems will also be presented.

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2022-09-15T13:00:00-04:00 2022-09-15T14:00:00-04:00 2022-09-15T14:00:00-04:00 2022-09-15 17:00:00 2022-09-15 18:00:00 2022-09-15 18:00:00 2022-09-15T13:00:00-04:00 2022-09-15T14:00:00-04:00 America/New_York America/New_York datetime 2022-09-15 01:00:00 2022-09-15 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Statistical Seminar Speaker - Zhaoran Wang ]]> 36358 Bio

Zhaoran Wang is an assistant professor at Northwestern University, working at the interface of machine learning, statistics, and optimization. He is the recipient of the AISTATS (Artificial Intelligence and Statistics Conference) notable paper award, ASA (American Statistical Association) best student paper in statistical learning and data mining, INFORMS (Institute for Operations Research and the Management Sciences) best student paper finalist in data mining, Microsoft Ph.D. Fellowship, Simons-Berkeley/J.P. Morgan AI Research Fellowship, Amazon Machine Learning Research Award, and NSF CAREER Award.

 

Abstract

Coupled with powerful function approximators such as deep neural networks, reinforcement learning (RL) achieves tremendous empirical successes. However, its theoretical understandings lag behind. In particular, it remains unclear how to provably attain the optimal policy with a finite regret or sample complexity. In this talk, we will present the two sides of the same coin, which demonstrates an intriguing duality between optimism and pessimism.
– In the online setting, we aim to learn the optimal policy by actively interacting with the environment. To strike a balance between exploration and exploitation, we propose an optimistic least-squares value iteration algorithm, which achieves a \sqrt{T} regret in the presence of linear, kernel, and neural function approximators.
– In the offline setting, we aim to learn the optimal policy based on a dataset collected a priori. Due to a lack of active interactions with the environment, we suffer from the insufficient coverage of the dataset. To maximally exploit the dataset, we propose a pessimistic least-squares value iteration algorithm, which achieves a minimax-optimal sample complexity.

]]> chumphrey30 1 1661355210 2022-08-24 15:33:30 1661355228 2022-08-24 15:33:48 0 0 event Bio

Zhaoran Wang is an assistant professor at Northwestern University, working at the interface of machine learning, statistics, and optimization. He is the recipient of the AISTATS (Artificial Intelligence and Statistics Conference) notable paper award, ASA (American Statistical Association) best student paper in statistical learning and data mining, INFORMS (Institute for Operations Research and the Management Sciences) best student paper finalist in data mining, Microsoft Ph.D. Fellowship, Simons-Berkeley/J.P. Morgan AI Research Fellowship, Amazon Machine Learning Research Award, and NSF CAREER Award.

 

Abstract

Coupled with powerful function approximators such as deep neural networks, reinforcement learning (RL) achieves tremendous empirical successes. However, its theoretical understandings lag behind. In particular, it remains unclear how to provably attain the optimal policy with a finite regret or sample complexity. In this talk, we will present the two sides of the same coin, which demonstrates an intriguing duality between optimism and pessimism.
– In the online setting, we aim to learn the optimal policy by actively interacting with the environment. To strike a balance between exploration and exploitation, we propose an optimistic least-squares value iteration algorithm, which achieves a \sqrt{T} regret in the presence of linear, kernel, and neural function approximators.
– In the offline setting, we aim to learn the optimal policy based on a dataset collected a priori. Due to a lack of active interactions with the environment, we suffer from the insufficient coverage of the dataset. To maximally exploit the dataset, we propose a pessimistic least-squares value iteration algorithm, which achieves a minimax-optimal sample complexity.

]]>
2022-09-01T13:00:00-04:00 2022-09-01T14:00:00-04:00 2022-09-01T14:00:00-04:00 2022-09-01 17:00:00 2022-09-01 18:00:00 2022-09-01 18:00:00 2022-09-01T13:00:00-04:00 2022-09-01T14:00:00-04:00 America/New_York America/New_York datetime 2022-09-01 01:00:00 2022-09-01 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Statistical Seminar Speaker- Fan Li]]> 36358 Bio

Fan Li is a professor in the Departments of Statistical Science, and Biostatistics and Bioinformatics at Duke University. Her primary research interest is statistical methods for causal inference, with applications to clinical trials, health and social sciences. She has developed the overlap weighting method. She also works on the interface of causal inference and machine learning, Bayesian analysis and missing data. She is an associate editor of Journal of the American Statistical Association, Bayesian Analysis, and Observational Studies.

 

Abstract

In pragmatic cluster randomized experiments, units are often recruited after the random cluster assignment. This can lead to post-randomization selection bias, inducing systematic differences in baseline characteristics of the recruited patients between intervention and control arms. We clarify that in such situations there are two different causal estimands of average treatment effects, one on the overall population and one on the recruited population, which require different data and strategies to identify. We specify the conditions under which cluster randomization implies individual randomization. We show that under the assumption of ignorable recruitment, the average treatment effect on the recruited population can be consistently estimated from the recruited sample. While the average treatment effect on the overall population is generally not identifiable from the recruited sample alone, a meaningful weighted estimand on the overall population can be consistently estimated via applying a simple weighting scheme to the recruited sample. This estimand corresponds to the subpopulation of units who would be recruited into the study regardless of the assignment. We also develop a sensitivity analysis method for checking the ignorable recruitment assumption. The proposed methods are illustrated via a real world application in cardiology.

]]> chumphrey30 1 1661354863 2022-08-24 15:27:43 1661354863 2022-08-24 15:27:43 0 0 event Bio

Fan Li is a professor in the Departments of Statistical Science, and Biostatistics and Bioinformatics at Duke University. Her primary research interest is statistical methods for causal inference, with applications to clinical trials, health and social sciences. She has developed the overlap weighting method. She also works on the interface of causal inference and machine learning, Bayesian analysis and missing data. She is an associate editor of Journal of the American Statistical Association, Bayesian Analysis, and Observational Studies.

 

Abstract

In pragmatic cluster randomized experiments, units are often recruited after the random cluster assignment. This can lead to post-randomization selection bias, inducing systematic differences in baseline characteristics of the recruited patients between intervention and control arms. We clarify that in such situations there are two different causal estimands of average treatment effects, one on the overall population and one on the recruited population, which require different data and strategies to identify. We specify the conditions under which cluster randomization implies individual randomization. We show that under the assumption of ignorable recruitment, the average treatment effect on the recruited population can be consistently estimated from the recruited sample. While the average treatment effect on the overall population is generally not identifiable from the recruited sample alone, a meaningful weighted estimand on the overall population can be consistently estimated via applying a simple weighting scheme to the recruited sample. This estimand corresponds to the subpopulation of units who would be recruited into the study regardless of the assignment. We also develop a sensitivity analysis method for checking the ignorable recruitment assumption. The proposed methods are illustrated via a real world application in cardiology.

]]>
2022-10-27T13:00:00-04:00 2022-10-27T14:00:00-04:00 2022-10-27T14:00:00-04:00 2022-10-27 17:00:00 2022-10-27 18:00:00 2022-10-27 18:00:00 2022-10-27T13:00:00-04:00 2022-10-27T14:00:00-04:00 America/New_York America/New_York datetime 2022-10-27 01:00:00 2022-10-27 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Statistical Seminar- Yu Yi]]> 36358 Bio

I am a Reader in the Department of Statistics, University of Warwick and a Turing Fellow at the Alan Turing Institute, previously an Associate Professor in the University of Warwick, a Lecturer in the University of Bristol, a postdoc of Professor Richard Samworth and a graduate student of Professor Zhiliang Ying. I obtained my academic degrees from Fudan University (B.Sc. in Mathematics, June 2009 and Ph.D. in Mathematical Statistics, June 2013).

 

 

 

Abstract 

 

This paper concerns about the limiting distributions of change point
estimators, in a high-dimensional linear regression time series context, where
a regression object $(y_t, X_t) \in \mathbb{R} \times \mathbb{R}^p$ is observed
at every time point $t \in \{1, \ldots, n\}$. At unknown time points, called
change points, the regression coefficients change, with the jump sizes measured
in $\ell_2$-norm. We provide limiting distributions of the change point
estimators in the regimes where the minimal jump size vanishes and where it
remains a constant. We allow for both the covariate and noise sequences to be
temporally dependent, in the functional dependence framework, which is the
first time seen in the change point inference literature. We show that a
block-type long-run variance estimator is consistent under the functional
dependence, which facilitates the practical implementation of our derived
limiting distributions. We also present a few important byproducts of their own
interest, including a novel variant of the dynamic programming algorithm to
boost the computational efficiency, consistent change point localisation rates
under functional dependence and a new Bernstein inequality for data possessing
functional dependence.  The paper is available at http://arxiv.org/abs/2207.12453

]]> chumphrey30 1 1661354522 2022-08-24 15:22:02 1661354622 2022-08-24 15:23:42 0 0 event Bio

I am a Reader in the Department of Statistics, University of Warwick and a Turing Fellow at the Alan Turing Institute, previously an Associate Professor in the University of Warwick, a Lecturer in the University of Bristol, a postdoc of Professor Richard Samworth and a graduate student of Professor Zhiliang Ying. I obtained my academic degrees from Fudan University (B.Sc. in Mathematics, June 2009 and Ph.D. in Mathematical Statistics, June 2013).

 

 

 

Abstract 

 

This paper concerns about the limiting distributions of change point
estimators, in a high-dimensional linear regression time series context, where
a regression object $(y_t, X_t) \in \mathbb{R} \times \mathbb{R}^p$ is observed
at every time point $t \in \{1, \ldots, n\}$. At unknown time points, called
change points, the regression coefficients change, with the jump sizes measured
in $\ell_2$-norm. We provide limiting distributions of the change point
estimators in the regimes where the minimal jump size vanishes and where it
remains a constant. We allow for both the covariate and noise sequences to be
temporally dependent, in the functional dependence framework, which is the
first time seen in the change point inference literature. We show that a
block-type long-run variance estimator is consistent under the functional
dependence, which facilitates the practical implementation of our derived
limiting distributions. We also present a few important byproducts of their own
interest, including a novel variant of the dynamic programming algorithm to
boost the computational efficiency, consistent change point localisation rates
under functional dependence and a new Bernstein inequality for data possessing
functional dependence.  The paper is available at http://arxiv.org/abs/2207.12453

]]>
2022-10-06T13:00:00-04:00 2022-10-06T14:00:00-04:00 2022-10-06T14:00:00-04:00 2022-10-06 17:00:00 2022-10-06 18:00:00 2022-10-06 18:00:00 2022-10-06T13:00:00-04:00 2022-10-06T14:00:00-04:00 America/New_York America/New_York datetime 2022-10-06 01:00:00 2022-10-06 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Statistics Seminar- Matias Cattaneo]]> 36358  Bio

Matias D. Cattaneo is a Professor of Operations Research and Financial Engineering (ORFE) at Princeton University, where he is also an Associated Faculty in the Department of Economics, the Center for Statistics and Machine Learning (CSML), and the Program in Latin American Studies (PLAS). His research spans econometrics, statistics, data science and decision science, with particular interests in program evaluation and causal inference. Most of his work is interdisciplinary and motivated by quantitative problems in the social, behavioral, and biomedical sciences. As part of his main research agenda, he has developed novel semi-/non-parametric, high-dimensional, and machine learning inference procedures with demonstrably superior robustness to tuning parameter and other implementation choices. Matias was elected Fellow of the Institute of Mathematical Statistics (IMS) in 2022. He also serves in the editorial boards of the Journal of the American Statistical AssociationEconometricaOperations ResearchEconometric Theory, the Econometrics Journal, and the Journal of Causal Inference. In addition, Matias is an Amazon Scholar, and has advised several governmental, multilateral, non-profit, and for-profit organizations around the world.

Matias earned a Ph.D. in Economics in 2008 and an M.A. in Statistics in 2005 from the University of California at Berkeley. He also completed an M.A. in Economics at Universidad Torcuato Di Tella in 2003 and a B.A. in Economics at Universidad de Buenos Aires in 2000. Prior to joining Princeton University in 2019, he was a faculty member in the departments of economics and statistics at the University of Michigan.

Matias was born and raised in Buenos Aires, Argentina. He is married to Rocio Titiunik, and they have two daughters.

 

 

 

 

 

 

 

                                                         Abstract

Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators taking the form of dyadic empirical processes. Our main contributions include the minimaxoptimal uniform convergence rate of the dyadic kernel density estimator, along with strong approximation results for the associated standardized and Studentized t-processes. A consistent variance estimator enables the construction of valid and feasible uniform confidence bands for the unknown density function. A crucial feature of dyadic distributions is that they may be “degenerate” at certain points in the support of the data, a property making our analysis somewhat delicate. Nonetheless our methods for uniform inference remain robust to the potential presence of such points. For implementation purposes, we discuss procedures based on positive semi-definite covariance estimators, mean squared error optimal bandwidth selectors and robust bias-correction techniques. We illustrate the empirical finite-sample performance of our methods both in simulations and with real-world data. Our technical results concerning strong approximations and maximal inequalities are of potential independent interest. Keywords: dyadic data, networks, kernel density estimation, minimaxity, strong approximation.

]]> chumphrey30 1 1661354141 2022-08-24 15:15:41 1661354192 2022-08-24 15:16:32 0 0 event  Bio

Matias D. Cattaneo is a Professor of Operations Research and Financial Engineering (ORFE) at Princeton University, where he is also an Associated Faculty in the Department of Economics, the Center for Statistics and Machine Learning (CSML), and the Program in Latin American Studies (PLAS). His research spans econometrics, statistics, data science and decision science, with particular interests in program evaluation and causal inference. Most of his work is interdisciplinary and motivated by quantitative problems in the social, behavioral, and biomedical sciences. As part of his main research agenda, he has developed novel semi-/non-parametric, high-dimensional, and machine learning inference procedures with demonstrably superior robustness to tuning parameter and other implementation choices. Matias was elected Fellow of the Institute of Mathematical Statistics (IMS) in 2022. He also serves in the editorial boards of the Journal of the American Statistical AssociationEconometricaOperations ResearchEconometric Theory, the Econometrics Journal, and the Journal of Causal Inference. In addition, Matias is an Amazon Scholar, and has advised several governmental, multilateral, non-profit, and for-profit organizations around the world.

Matias earned a Ph.D. in Economics in 2008 and an M.A. in Statistics in 2005 from the University of California at Berkeley. He also completed an M.A. in Economics at Universidad Torcuato Di Tella in 2003 and a B.A. in Economics at Universidad de Buenos Aires in 2000. Prior to joining Princeton University in 2019, he was a faculty member in the departments of economics and statistics at the University of Michigan.

Matias was born and raised in Buenos Aires, Argentina. He is married to Rocio Titiunik, and they have two daughters.

 

 

 

 

 

 

 

                                                         Abstract

Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators taking the form of dyadic empirical processes. Our main contributions include the minimaxoptimal uniform convergence rate of the dyadic kernel density estimator, along with strong approximation results for the associated standardized and Studentized t-processes. A consistent variance estimator enables the construction of valid and feasible uniform confidence bands for the unknown density function. A crucial feature of dyadic distributions is that they may be “degenerate” at certain points in the support of the data, a property making our analysis somewhat delicate. Nonetheless our methods for uniform inference remain robust to the potential presence of such points. For implementation purposes, we discuss procedures based on positive semi-definite covariance estimators, mean squared error optimal bandwidth selectors and robust bias-correction techniques. We illustrate the empirical finite-sample performance of our methods both in simulations and with real-world data. Our technical results concerning strong approximations and maximal inequalities are of potential independent interest. Keywords: dyadic data, networks, kernel density estimation, minimaxity, strong approximation.

]]>
2022-09-22T13:00:00-04:00 2022-09-22T14:00:00-04:00 2022-09-22T14:00:00-04:00 2022-09-22 17:00:00 2022-09-22 18:00:00 2022-09-22 18:00:00 2022-09-22T13:00:00-04:00 2022-09-22T14:00:00-04:00 America/New_York America/New_York datetime 2022-09-22 01:00:00 2022-09-22 02:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[LeeAnn and Walter Muller Distinguished Lecture Series - Michael I. Jordan, University of California, Berkeley]]> 33939 David Mitchell 1 1661187822 2022-08-22 17:03:42 1661187822 2022-08-22 17:03:42 0 0 event 2022-10-13T16:00:00-04:00 2022-10-13T17:00:00-04:00 2022-10-13T17:00:00-04:00 2022-10-13 20:00:00 2022-10-13 21:00:00 2022-10-13 21:00:00 2022-10-13T16:00:00-04:00 2022-10-13T17:00:00-04:00 America/New_York America/New_York datetime 2022-10-13 04:00:00 2022-10-13 05:00:00 America/New_York America/New_York datetime <![CDATA[]]> David Mitchell

Communications Manager

david.mitchell@isye.gatech.edu

]]>
660377 660377 image <![CDATA[Michael I. Jordan]]> image/jpeg 1661187521 2022-08-22 16:58:41 1661187521 2022-08-22 16:58:41
<![CDATA[ISyE Statistics Seminar- Jay Bartroff]]> 36358 Abstract

 

 

Bio

]]> chumphrey30 1 1660827508 2022-08-18 12:58:28 1660827508 2022-08-18 12:58:28 0 0 event Abstract

  • We present an efficient method of calculating exact confidence intervals for the hypergeometric number of successes. The method inverts minimum-width acceptance intervals after shifting them to make their endpoints nondecreasing while preserving their level. The resulting set of confidence intervals achieves minimum possible average width, and even in comparison with confidence sets not required to be intervals it attains the minimum possible cardinality most of the time, and always within 1. The method compares favorably with existing methods not only in the size of the intervals but also in the time required to compute them. A similar approach can be taken for optimal confidence intervals for an unknown population size, such as in capture-recapture problems.

 

 

Bio

  • Jay Bartroff joined the University of Texas at Austin's Statistics & Data Sciences Department in January 2022 as Professor and Associate Chair. Prior to that he was Professor of Mathematics and Vice-Chair for Statistics at the University of Southern California for 15 years.  Before that he was an NSF postdoc in the Stanford Statistics Department, following his PhD at Caltech and his undergraduate degree at U.C. Berkeley.  His research interests include sequential analysis, multiple testing, Stein's method, and a variety of biomedical applications including clinical trial design and methods for wearable alcohol biosensors. Jay's research has been supported by the NSF, NIH, FDA, and NSA.  His publications include a textbook on sequential methods coauthored with Lai and Shih, published by Springer.

 

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2022-08-25T09:40:00-04:00 2022-08-25T09:40:00-04:00 2022-08-25T09:40:00-04:00 2022-08-25 13:40:00 2022-08-25 13:40:00 2022-08-25 13:40:00 2022-08-25T09:40:00-04:00 2022-08-25T09:40:00-04:00 America/New_York America/New_York datetime 2022-08-25 09:40:00 2022-08-25 09:40:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Department Seminar-Erick Moreno Centeno]]> 36086 Abstract:

Solving sparse linear systems is core to solving linear programs and other optimization problems. Exactly solving linear programs and systems is necessary for some applications (e.g., theoretical results, feasibility problems, military applications, applications with hefty costs, ill-conditioned problems, etc.). To address this, we are developing the Sparse Exact (SPEX) Factorization Framework: a high-performance, well-documented, and highly robust suite of algorithms and software. This talk will focus on the (mostly) untold story behind this research and its theoretical foundations and briefly discuss recent developments and computational results.

 

Bio:

Dr. Erick Moreno-Centeno is the Associate Professor and Donna and Jim Furber '64 Faculty Fellow at the Wm Michael Barnes '64 Department of Industrial and Systems Engineering and the Eppright University Professor in Undergraduate Teaching Excellence at Texas A&M University. He earned his M.S. and Ph.D. degrees in Industrial Engineering & Operations Research and his M.S. degree in Computer Science, all from the University of California at Berkeley. He received his B.S. in Industrial Physics Engineering from ITESM Campus Monterrey, Mexico. Dr. Moreno's research focuses on optimization methods free of round-off errors and the design and analysis of new combinatorial optimization algorithms. He was honored with the Dr. Hamed K. Eldin Outstanding Early Career Industrial Engineer in Academia Award (2016) and the INFORMS Computing Society Prize (2021). He serves as Associate Editor for the journals Networks, IISE Transactions, and Energy Systems.  Dr. Moreno teaches optimization courses, and his passion for teaching has been honored with numerous awards, most notably the Institute of Industrial Engineers' Operations Research Division Teaching Award.

]]> yrollins3 1 1659973241 2022-08-08 15:40:41 1659973241 2022-08-08 15:40:41 0 0 event Abstract:

Solving sparse linear systems is core to solving linear programs and other optimization problems. Exactly solving linear programs and systems is necessary for some applications (e.g., theoretical results, feasibility problems, military applications, applications with hefty costs, ill-conditioned problems, etc.). To address this, we are developing the Sparse Exact (SPEX) Factorization Framework: a high-performance, well-documented, and highly robust suite of algorithms and software. This talk will focus on the (mostly) untold story behind this research and its theoretical foundations and briefly discuss recent developments and computational results.

 

Bio:

Dr. Erick Moreno-Centeno is the Associate Professor and Donna and Jim Furber '64 Faculty Fellow at the Wm Michael Barnes '64 Department of Industrial and Systems Engineering and the Eppright University Professor in Undergraduate Teaching Excellence at Texas A&M University. He earned his M.S. and Ph.D. degrees in Industrial Engineering & Operations Research and his M.S. degree in Computer Science, all from the University of California at Berkeley. He received his B.S. in Industrial Physics Engineering from ITESM Campus Monterrey, Mexico. Dr. Moreno's research focuses on optimization methods free of round-off errors and the design and analysis of new combinatorial optimization algorithms. He was honored with the Dr. Hamed K. Eldin Outstanding Early Career Industrial Engineer in Academia Award (2016) and the INFORMS Computing Society Prize (2021). He serves as Associate Editor for the journals Networks, IISE Transactions, and Energy Systems.  Dr. Moreno teaches optimization courses, and his passion for teaching has been honored with numerous awards, most notably the Institute of Industrial Engineers' Operations Research Division Teaching Award.

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2022-08-26T12:30:00-04:00 2022-08-26T13:30:00-04:00 2022-08-26T13:30:00-04:00 2022-08-26 16:30:00 2022-08-26 17:30:00 2022-08-26 17:30:00 2022-08-26T12:30:00-04:00 2022-08-26T13:30:00-04:00 America/New_York America/New_York datetime 2022-08-26 12:30:00 2022-08-26 01:30:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[SCL September 2022 Supply Chain Days]]> 27233 Georgia Tech Supply Chain students, please join us for our second fall Supply Chain Days! We will be hosting both an On Campus (Sept 8) and a Virtual session (Sept 9). Please note that you need to register separately for each event to attend.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).
 

EVENT DETAILS

On Campus/In-Person (ISyE Main Building Atrium)

Thursday, September 8 | 10am-1pm ET

Virtual/Online (Career Fair Plus)

Friday, September 9 | 9am - 3pm ET

 

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday for a list of attending organizations and links to register.

 

]]> Andy Haleblian 1 1659480421 2022-08-02 22:47:01 1659480558 2022-08-02 22:49:18 0 0 event Georgia Tech Supply Chain students, please join us for our spring Supply Chain Days! We will be hosting both an On Campus (Sept 8) and a Virtual session (Sept 9). Please note that you need to register separately for each event to attend.

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2022-09-08T11:00:00-04:00 2022-09-09T16:00:00-04:00 2022-09-09T16:00:00-04:00 2022-09-08 15:00:00 2022-09-09 20:00:00 2022-09-09 20:00:00 2022-09-08T11:00:00-04:00 2022-09-09T16:00:00-04:00 America/New_York America/New_York datetime 2022-09-08 11:00:00 2022-09-09 04:00:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

]]>
659866 659866 image <![CDATA[SCL September 2022 Supply Chain Days]]> image/jpeg 1659480466 2022-08-02 22:47:46 1659480476 2022-08-02 22:47:56 <![CDATA[Register online to attend (for Georgia Tech students)]]> <![CDATA[Supply Chain and Logistics Institute website]]>
<![CDATA[SCL Course: Engineering the Warehouse (Virtual/Instructor-led)]]> 27233 COURSE DESCRIPTION

The requirement for high levels of customer service, increasing numbers of SKUs and high labor costs have dramatically increased the complexity of warehouse operations. It is no longer sufficient to manage a warehouse based on a simple, arbitrary “ABC” classification of SKUs, which treats all those in a category as if they were identical. Instead, each decision – such as where to store or where to pick product – must be based on careful engineering and economic analysis. Each SKU must identify its own cheapest, fastest path through the warehouse to the customer and then compete with all the other SKUs for the necessary resources. This results in warehouse operations that are finely tuned to patterns of customer orders and maximally efficient. Learn the concepts necessary to address modern warehouse trade-offs between space and time in optimizing and managing your warehouse.

Essential learning for those who are seeking cost reductions through better handling methods. Also valuable for those who must replace, upgrade, or add material handling equipment. The two-day course will include case examples and a guided exercise to ensure mastery of the techniques presented.

WHO SHOULD ATTEND

Supply chain and logistics consultants, supply chain engineers and analysts, facility engineers, and warehouse supervisors and team leaders

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

WHAT IS COVERED

COURSE MATERIALS

COURSE PREREQUISITES

None.

CERTIFICATE INFORMATION

This course is part of the Distribution Operations Analysis & Design (DOAD) Certificate.

]]> Andy Haleblian 1 1659123366 2022-07-29 19:36:06 1659123385 2022-07-29 19:36:25 0 0 event The requirement for high levels of customer service, increasing numbers of SKUs and high labor costs have dramatically increased the complexity of warehouse operations. It is no longer sufficient to manage a warehouse based on a simple, arbitrary “ABC” classification of SKUs, which treats all those in a category as if they were identical. Instead, each decision – such as where to store or where to pick product – must be based on careful engineering and economic analysis.

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2023-02-13T12:00:00-05:00 2023-02-16T18:00:00-05:00 2023-02-16T18:00:00-05:00 2023-02-13 17:00:00 2023-02-16 23:00:00 2023-02-16 23:00:00 2023-02-13T12:00:00-05:00 2023-02-16T18:00:00-05:00 America/New_York America/New_York datetime 2023-02-13 12:00:00 2023-02-16 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Machine Learning Applications for Supply Chain Planning (Virtual/Instructor-led)]]> 27233 Course Description

This course is the third in the four-course Supply Chain Analytics Professional certificate program. It introduces the field of machine learning, an area where algorithms learn patterns from data to support proactive decision making, as it applies to supply chain management. You’ll use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You’ll use Python and PowerBI to create and analyze regression, clustering, and classification models.

The online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars (April 18, 19, 20, 21 | 1-5pm ET) and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1621284042 2021-05-17 20:40:42 1655407174 2022-06-16 19:19:34 0 0 event An introduction to the field of machine learning as it applies to supply chain management. You’ll then use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance.

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2022-08-08T14:00:00-04:00 2022-08-11T18:00:00-04:00 2022-08-11T18:00:00-04:00 2022-08-08 18:00:00 2022-08-11 22:00:00 2022-08-11 22:00:00 2022-08-08T14:00:00-04:00 2022-08-11T18:00:00-04:00 America/New_York America/New_York datetime 2022-08-08 02:00:00 2022-08-11 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Supply Chain Optimization and Prescriptive Analytics (Virtual/Instructor-led)]]> 27233 Course Description

This course is the fourth in the 4-course Supply Chain Analytics Professional certificate program. It incorporates learning advanced analytics and mathematical optimization to find solutions for supply chain problems. You’ll learn how to use linear programming, mixed integer programming, and heuristics to conduct prescriptive analytics related to production processes, distribution networks, and routing. The course serves as a capstone for the program by culminating in a hackathon where you’ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.

The online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars (May 16, 17, 18, 19 | 1-5pm ET) and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1623868040 2021-06-16 18:27:20 1655407151 2022-06-16 19:19:11 0 0 event Learn advanced analytics and mathematical optimization to find solutions for supply chain problems. The course also serves as a capstone for the Supply Chain Analytics Professional certificate program by culminating in a hackathon where you’ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.

]]>
2022-09-12T14:00:00-04:00 2022-09-15T18:00:00-04:00 2022-09-15T18:00:00-04:00 2022-09-12 18:00:00 2022-09-15 22:00:00 2022-09-15 22:00:00 2022-09-12T14:00:00-04:00 2022-09-15T18:00:00-04:00 America/New_York America/New_York datetime 2022-09-12 02:00:00 2022-09-15 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

]]>
<![CDATA[Course webpage within the SCL website]]>
<![CDATA[Webinar: Leadership for Transformative Change]]> 27233 Tarun Mohan Lal is an experienced healthcare leader with a passion for innovation, transformation and community support to improve lives.

Currently, Tarun serves as the Chief Analytics and Solutions Officer and Vice President at Atrium Health Navicent, where he provides oversight to the analytics and performance improvement functions across the system of care. Originally from India, Tarun earned a Bachelor of Science degree in Industrial and Systems Engineering from Manipal University and a Master of Science in Industrial ngineering from Texas A&M University. He will be graduating with a Doctorate in System Science and Industrial Engineering with
focus on Healthcare from the State University of New York.

Tarun is author of several publications and a strong advocate of community support and mentorship. He serves as president-elect and diplomate of the Society for Health Systems, and as a member of the Board of Directors of several organizations, including New Leaf Behavioral Health and The Bridge International.

National Academy of Engineering/Discover-E named Tarun as one of the 13 New Faces of Engineering. He was also recognized by the Institute of Industrial and Systems Engineers as an Outstanding Early Career Young Professional in Industry. In 2021, Tarun was awarded the Management Award by the Society for Engineering and Management systems for his contributions with Pandemic Management in south central Georgia.

]]> Andy Haleblian 1 1652986095 2022-05-19 18:48:15 1653158978 2022-05-21 18:49:38 0 0 event Tarun Mohan Lal discussed the importance of recognizing leadership is a journey of continuous improvement, courage and learning as part of the Health System Leadership Webinar series hosted by the Center for Health and Humanitarian Logistics.

]]>
2021-10-21T15:00:00-04:00 2021-10-21T15:30:00-04:00 2021-10-21T15:30:00-04:00 2021-10-21 19:00:00 2021-10-21 19:30:00 2021-10-21 19:30:00 2021-10-21T15:00:00-04:00 2021-10-21T15:30:00-04:00 America/New_York America/New_York datetime 2021-10-21 03:00:00 2021-10-21 03:30:00 America/New_York America/New_York datetime <![CDATA[]]> Center for Health and Humanitarian Systems
chhs@gatech.edu

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658371 658371 image <![CDATA[Tarun Mohan Lal]]> image/jpeg 1652985279 2022-05-19 18:34:39 1652985279 2022-05-19 18:34:39
<![CDATA[Webinar: Systems Engineering and Quality Improvement in Healthcare]]> 27233 During the webinar, Victoria Jordan discussed her career path and experiences working in healthcare quality.

Victoria Jordan, Ph.D. is the Vice President of Quality and Patient Safety at Emory Healthcare. In her role, she develops, plans, coordinates and leads the implementation of quality improvement efforts, regulatory compliance, certifications, policy management, infection prevention, patient safety, and clinical data analytics across Emory Healthcare. This includes strategic oversight of quality initiatives across all of Emory's hospitals, its many primary care clinics and specialty clinics.

Previously she served as the Executive Director of Performance Improvement at MD Anderson Cancer Center in Houston, TX, and as the Director of Performance Improvement at Vanderbilt University Medical Center in Nashville, TN. Dr. Jordan received her MS and Ph.D. degrees in industrial and systems engineering from Auburn University, and her MBA from the Ohio State University.

]]> Andy Haleblian 1 1652986642 2022-05-19 18:57:22 1653158956 2022-05-21 18:49:16 0 0 event Victoria Jordan discussed her career path and experiences working in healthcare quality as part of the Health System Leadership Webinar series hosted by the Center for Health and Humanitarian Logistics.

]]>
2021-09-16T15:00:00-04:00 2021-09-16T15:30:00-04:00 2021-09-16T15:30:00-04:00 2021-09-16 19:00:00 2021-09-16 19:30:00 2021-09-16 19:30:00 2021-09-16T15:00:00-04:00 2021-09-16T15:30:00-04:00 America/New_York America/New_York datetime 2021-09-16 03:00:00 2021-09-16 03:30:00 America/New_York America/New_York datetime <![CDATA[]]> Center for Health and Humanitarian Systems
chhs@gatech.edu

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658377 658377 image <![CDATA[Victoria Jordan]]> image/jpeg 1652986313 2022-05-19 18:51:53 1652986313 2022-05-19 18:51:53
<![CDATA[Webinar: An Unconventional Path, a Rewarding Career]]> 27233 In 2015, Jim McClelland concluded a 45-year career as an executive with Goodwill Industries. Forty-one of those years were as President and CEO of one of the largest and most diversified Goodwill organizations in the country, based in Indianapolis. Following his Goodwill career, he served three years as Executive Director for Drug Prevention,  Treatment, and Enforcement for the State of Indiana. Jim has served on the boards of numerous not-for-profit organizations at local, national, and international levels and chaired several of them. He was also heavily involved in helping develop Goodwill Industries in South Korea.

Among other honors, Jim has been inducted into the Central Indiana Business Hall of Fame, the Georgia Tech Engineering Hall of Fame, and the Goodwill Industries International Hall of Fame. He is also a recipient of the Distinguished Entrepreneur Award from the Kelley School of Business at Indiana University and the Lifetime Achievement in Innovation Award from the Venture Club of Indiana.

Jim earned a bachelor’s degree in Industrial & Systems Engineering at Georgia Tech and an MBA from the Kelley School of Business at Indiana University. A native of Florida, he and his wife Jane live in Indianapolis. They have two grown children and two  grandchildren.

]]> Andy Haleblian 1 1652984936 2022-05-19 18:28:56 1653158945 2022-05-21 18:49:05 0 0 event Jim McClelland discussed his career path in the nonprofit industry, as well as his work in the public health sector as part of the Health System Leadership Webinar series hosted by the Center for Health and Humanitarian Logistics.

]]>
2021-10-28T15:00:00-04:00 2021-10-28T15:30:00-04:00 2021-10-28T15:30:00-04:00 2021-10-28 19:00:00 2021-10-28 19:30:00 2021-10-28 19:30:00 2021-10-28T15:00:00-04:00 2021-10-28T15:30:00-04:00 America/New_York America/New_York datetime 2021-10-28 03:00:00 2021-10-28 03:30:00 America/New_York America/New_York datetime <![CDATA[]]> Center for Health and Humanitarian Systems
chhs@gatech.edu

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658368 658368 image <![CDATA[Jim McClelland]]> image/jpeg 1652984457 2022-05-19 18:20:57 1652984457 2022-05-19 18:20:57
<![CDATA[Webinar: Don't Get Too Comfortable - Learning to Take a Leap of Faith]]> 27233 During the webinar, Ann Dunkin discussed her career in both the private and public sector, as well as the transition points that brought her to where she is today.

Ann Dunkin is currently the Chief Information Officer of the United States Department of Energy. Before joining the Department of Energy, she was Chief Strategy and Innovation Officer for State and Local Government at Dell. Prior to joining Dell, Ms. Dunkin was the CIO for the County of Santa Clara. Ms. Dunkin also served in the Obama Administration as the Chief Information Officer of the United States Environmental Protection Agency and as Chief Technology Officer for the Palo Alto Unified School District. She also held a variety of leadership at Hewlett Packard.

Ms. Dunkin is a published author, most recently of the book Industrial Digital Transformation, and sought-after speaker on the topics of technology modernization, digital services and organizational transformation.

Ms. Dunkin holds an M.S. and a B.S. in Industrial and Systems Engineering, both from the Georgia Institute of Technology. She is a licensed professional engineer in California and Washington.

]]> Andy Haleblian 1 1652362199 2022-05-12 13:29:59 1653158930 2022-05-21 18:48:50 0 0 event Ann Dunkin discussed her career in both the private and public sector as part of the Health System Leadership Webinar series hosted by the Center for Health and Humanitarian Logistics.

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2021-11-11T15:00:00-05:00 2021-11-11T15:30:00-05:00 2021-11-11T15:30:00-05:00 2021-11-11 20:00:00 2021-11-11 20:30:00 2021-11-11 20:30:00 2021-11-11T15:00:00-05:00 2021-11-11T15:30:00-05:00 America/New_York America/New_York datetime 2021-11-11 03:00:00 2021-11-11 03:30:00 America/New_York America/New_York datetime <![CDATA[]]> Center for Health and Humanitarian Systems
chhs@gatech.edu

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650153 650153 image <![CDATA[Ann Dunkin]]> image/jpeg 1630025587 2021-08-27 00:53:07 1630025587 2021-08-27 00:53:07
<![CDATA[Bridging Health and Humanity - Documentary Screening Co-hosted by GT Center for Health and Humanitarian Systems and France Atlanta]]> 34586 Monday November 11th, 6pm France Atlanta will host a special Documentary Screening as a part of their 2019 Event Series: Triage: Dr Orbinski’s humanitarian dilemma which follows the powerful odyssey of James Orbinski, a humanitarian, Nobel Peace Prize-winning doctor, and former head of Doctors Without Borders, as he returns to Africa to ponder the meaning of his life’s work and the value of helping others. Drawing on a lifetime of experience deep in the trenches of genocide and famine, this extraordinary individual relives the triumphs and tragedies of relief work in Somalia, Rwanda, and the Democratic Republic of Congo. The film will unsettle and move as it pointedly asks disturbing questions at the heart of the humanitarian dilemma. What can any one individual really do to bring peace to those who suffer? Where does humanitarianism end and raw politics begin? How does the sight of unspeakable evil affect the soul? Smartly directed by Patrick Reed, this remarkable film provides no definitive answers, but celebrates the best in the human spirit while staring unblinkingly at the worst.

Opening remarks and discussion will be led by Georgia Institute of Technology, Associate Professor of Economics, Shatakshee Dhongde.

Co-organized by The Georgia Tech Center For Health and Humanitarian Systems (CHHS) and the Consulate General of France in Atlanta, this screening will serve to link Health and Humanity through the France-Atlanta Humanitarian Forum taking place Nov 8th and the CHHS Health Systems- Next Generation Forum Nov 12th”

Don’t Miss These Events!

This event is part of #FranceAtlanta2019!

Created in 2010 by the Consulate General of France in Atlanta and Georgia Tech, France-Atlanta is a series of events centered on innovation and designed to foster cooperation between France and the U.S. Southeast.

]]> jcooper90 1 1571248510 2019-10-16 17:55:10 1652902435 2022-05-18 19:33:55 0 0 event Monday November 11th, 6pm France Atlanta will host a special Documentary Screening as a part of their 2019 Event Series: Triage: Dr Orbinski’s humanitarian dilemma. Opening remarks and discussion will be led by Georgia Institute of Technology, Associate Professor of Economics, Shatakshee Dhongde.

Co-organized by The Georgia Tech Center For Health and Humanitarian Systems, the screening will serve to link Health and Humanity through the France-Atlanta Humanitarian Forum taking place Nov 8th and the CHHS Health Systems- Next Generation Forum Nov 12th

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2019-11-11T19:00:00-05:00 2019-11-12T20:59:00-05:00 2019-11-12T20:59:00-05:00 2019-11-12 00:00:00 2019-11-13 01:59:00 2019-11-13 01:59:00 2019-11-11T19:00:00-05:00 2019-11-12T20:59:00-05:00 America/New_York America/New_York datetime 2019-11-11 07:00:00 2019-11-12 08:59:00 America/New_York America/New_York datetime <![CDATA[]]> Questions? Email chhs@gatech.edu

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627742 622160 627742 image <![CDATA[Bridging Health and Humanity]]> image/jpeg 1571332650 2019-10-17 17:17:30 1571332650 2019-10-17 17:17:30 622160 image <![CDATA[Shatakshee Dhongde]]> image/png 1559571454 2019-06-03 14:17:34 1559571454 2019-06-03 14:17:34 <![CDATA[Register Now for Free]]>
<![CDATA[ Health Systems: The Next Generation Forum 2018]]> 34586 The Center for Health & Humanitarian Systems (CHHS) at Georgia Tech invites you to attend an event with professionals and scholars from across the fields of healthcare delivery, operations and education focused on improving local and global health systems. The goal of this event is to promote and maintain wellness by identifying important trends in healthcare system applications and designs, opportunities for collaboration, and the future of health systems. Focusing on the theme of moving from sick-care to healthcare, the event will include two plenary panel sessions, presentations and a poster fair showcasing new proactive methods and research in technological applications.

If you or someone you know is interested in showcasing their research during our poster session please visit our submission page - The deadline to submit is Friday10/26

]]> jcooper90 1 1539003685 2018-10-08 13:01:25 1652893661 2022-05-18 17:07:41 0 0 event Guest Speakers Include:

  • Bridget Hurley, VP of Clinical and Regulatory | Evidation Health
  • Jim McClelland, Executive Director for Drug Prevention, Treatment, and Enforcement | State of Indiana
  • Patrick O’Neal, M.D. Commissioner & Director of Health Protection |Georgia Department of Public Health (DPH)
  • Vivian Singletary, JM, MBA Director Public Health Informatics Institute (PHII)
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2018-11-09T13:30:00-05:00 2018-11-09T17:30:00-05:00 2018-11-09T17:30:00-05:00 2018-11-09 18:30:00 2018-11-09 22:30:00 2018-11-09 22:30:00 2018-11-09T13:30:00-05:00 2018-11-09T17:30:00-05:00 America/New_York America/New_York datetime 2018-11-09 01:30:00 2018-11-09 05:30:00 America/New_York America/New_York datetime <![CDATA[Register Now!]]> 612452 612452 image <![CDATA[Health Systems: The Next Generation 2018 Flyer]]> image/png 1539002328 2018-10-08 12:38:48 1540175859 2018-10-22 02:37:39 <![CDATA[Register Now To Attend!]]> <![CDATA[If you or someone you know wants to showcase research during our poster session please visit our submission page -Deadline 10/26]]>
<![CDATA[ Health Systems: The Next Generation Forum 2019]]> 34586 This Forum provides a platform for thought leaders and field experts with local and global perspectives and knowledge. The impact of the discussions and collaborations range from local initiatives to advance healthcare systems right here in Atlanta to broader impact across the country and beyond.

Our theme this year: Moving from Sick-care to Healthcare -Interdisciplinary collaboration between medical and STEM fields.

Agenda Will Include:

Featuring

]]> jcooper90 1 1571241766 2019-10-16 16:02:46 1652893629 2022-05-18 17:07:09 0 0 event Since its inauguration in 2016, the Forum has annually brought together 120+ professionals, students and scholars from across the fields of healthcare delivery, operations and education focused on promoting and maintaining wellness by identifying important trends in healthcare system applications and designs, opportunities for collaboration, and the future of health systems.

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2019-11-12T13:30:00-05:00 2019-11-12T18:30:00-05:00 2019-11-12T18:30:00-05:00 2019-11-12 18:30:00 2019-11-12 23:30:00 2019-11-12 23:30:00 2019-11-12T13:30:00-05:00 2019-11-12T18:30:00-05:00 America/New_York America/New_York datetime 2019-11-12 01:30:00 2019-11-12 06:30:00 America/New_York America/New_York datetime <![CDATA[]]> Questions? Email chhs@gatech.edu

]]>
629649 629649 image <![CDATA[Health Systems The Next Generation - 2019]]> image/jpeg 1575402201 2019-12-03 19:43:21 1575402201 2019-12-03 19:43:21 <![CDATA[Register Now for Free]]> <![CDATA[Submit your Poster abstract By Nov. 4th]]>
<![CDATA[2019 Health & Humanitarian Logistics Conference (Rwanda)]]> 34586 The  11th annual Health & Humanitarian Logistics (HHL) Conference will take place July 10-11, 2019 | Kigali, Rwanda to provide an open forum to discuss the challenges and new solutions in disaster preparedness and response, long-term development and humanitarian aid, and global health delivery. The conference platform encourages learning and collaboration within and across institutions; promotes system-wide improvements in organizations and the sector as a whole; identifies important research issues; and establishes priorities for nongovernmental organizations (NGOs), corporations, and the government in terms of strategies, policies and investments.

Representatives from the humanitarian sector, government, NGOs, foundations and private industry, and academia present diverse perspectives in health and humanitarian challenges through keynote addresses, panel discussions, focused workshops, lunchtime group discussions, and interactive poster sessions covering a broad set of research topics and applications.

The conference is sure to bring together an abundance of professionals active in the global health and humanitarian sectors from around the world. The event is chaired annually by the Georgia Tech Center for Health & Humanitarian Systems (CHHS), NCSU, INSEAD Humanitarian Research Group, MIT Humanitarian Response LabNortheastern University. This year’s conference is pleased to have as Co- organizers The International Association of Public Health Logisticians (IAPHL) and People that Deliver, and University of Rwanda Regional Center for Excellence as our Host.

We invite you to attend and particpate as a presenter in the following areas: collaborative workshopsoral presentations and poster sessions. To see our requirements and submit a proposal visit our Call For Presentations Page

]]> jcooper90 1 1550861453 2019-02-22 18:50:53 1652893063 2022-05-18 16:57:43 0 0 event The  11th annual Health & Humanitarian Logistics (HHL) Conference will take place July 10-11, 2019 | Kigali, Rwanda to provide an open forum to discuss the challenges and new solutions in disaster preparedness and response, long-term development and humanitarian aid, and global health delivery. We invite you to attend and particpate as a presenter in the following areas: collaborative workshopsoral presentations and poster sessions.

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2019-07-10T09:00:00-04:00 2019-07-12T17:59:00-04:00 2019-07-12T17:59:00-04:00 2019-07-10 13:00:00 2019-07-12 21:59:00 2019-07-12 21:59:00 2019-07-10T09:00:00-04:00 2019-07-12T17:59:00-04:00 America/New_York America/New_York datetime 2019-07-10 09:00:00 2019-07-12 05:59:00 America/New_York America/New_York datetime <![CDATA[Register Now for Early Bird Rates!]]> If you have a question or comments for the organizers, please submit them using our contact form.

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618310 618310 image <![CDATA[HHL conference 2019 Announcement]]> image/png 1550854994 2019-02-22 17:03:14 1550854994 2019-02-22 17:03:14 <![CDATA[Register Now for Early Bird Rates!]]> <![CDATA[Submit a Presentation Proposal]]>
<![CDATA[2020 Health & Humanitarian Logistics Conference (Virtual)]]> 34586 The COVID-19 Pandemic has caused uncertainty and disruption around the world, but the need to discuss challenges and new solutions in global health delivery, disaster preparedness and response, and long-term development still remains. 

Now more than ever the world calls for our leadership, our collaboration and our innovation. That is why we’ve made the decision to host the HHL 2020 Conference online.

As we approach the conference date, we encourage you to Register and stay up to date with new developments through our website and email communications.   

Learn More About HHL

]]> jcooper90 1 1589912480 2020-05-19 18:21:20 1652892934 2022-05-18 16:55:34 0 0 event The COVID-19 Pandemic has caused uncertainty and disruption around the world, but the need to discuss challenges and new solutions in global health delivery, disaster preparedness and response, and long-term development still remains.

Now more than ever the world calls for our leadership, our collaboration and our innovation. That is why we’ve made the decision to host the HHL2020 Conference online.   

 

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2020-09-29T11:00:00-04:00 2020-10-02T14:59:00-04:00 2020-10-02T14:59:00-04:00 2020-09-29 15:00:00 2020-10-02 18:59:00 2020-10-02 18:59:00 2020-09-29T11:00:00-04:00 2020-10-02T14:59:00-04:00 America/New_York America/New_York datetime 2020-09-29 11:00:00 2020-10-02 02:59:00 America/New_York America/New_York datetime <![CDATA[]]> humlogconf@gatech.edu

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638415 638415 image <![CDATA[12th Annual Conference on Health & Humanitarian Logistics]]> image/jpeg 1598407220 2020-08-26 02:00:20 1598407220 2020-08-26 02:00:20 <![CDATA[Visit the HHL2020 Conference Website]]> <![CDATA[Register Now]]>
<![CDATA[2018 Health & Humanitarian Logistics Conference (Dubai)]]> 34586 The aim of the 10th annual Health & Humanitarian Logistics (HHL) Conference is to provide an open forum to discuss the challenges and new solutions in disaster preparedness and response, long-term development and humanitarian aid, and global health delivery. The conference platform encourages learning and collaboration within and across institutions; promotes system-wide improvements in organizations and the sector as a whole; identifies important research issues; and establishes priorities for nongovernmental organizations (NGOs), corporations, and the government in terms of strategies, policies and investments.

Representatives from the humanitarian sector, government, NGOs, foundations and private industry, and academia present diverse perspectives in health and humanitarian challenges through keynote addresses, panel discussions, focused workshops, lunchtime group discussions, and interactive poster sessions covering a broad set of research topics and applications.

This is the first time, the conference will be hosted in Dubai, a staple humanitarian hub, and it is sure to bring together an abundance of professionals active in the global health and humanitarian sectors from around the world.

Visit the conference website at https://chhs.gatech.edu/conference/2018

]]> jcooper90 1 1520453278 2018-03-07 20:07:58 1652892827 2022-05-18 16:53:47 0 0 event 10th Health and Humanitarian Logistics Conference (logo attached)

 

Register Now for our discounted rate by: Wednesday, April 4 2018

We are pleased to announce registration is now open for The 10th annual Conference on Health and Humanitarian Logistics (HHL), an open forum that annually attracts 250+ professionals active in the global health and humanitarian sectors from around the world to discuss the challenges and new solutions in disaster preparedness and response, long-term development and humanitarian aid, and global health delivery.

Hosted in Dubai, United Arab Emirates July 18-19, the agenda will feature:

·          Keynote addresses

·          Panel discussions

·          Interactive workshops

·          Oral Presentations

·          Poster Sessions

·          Tour visits to local health and humanitarian logistics facilities

REGISTER ONLINE: https://chhs.gatech.edu/conference/2018/registration

We look forward to seeing you at #HHL2018!

2018 Conference Co-organizers:

  • Özlem Ergun, Northeastern University
  • Jarrod Goentzel, Humanitarian Response Lab, MIT
  • Pinar Keskinocak, Center for Health & Humanitarian Systems, Georgia Tech
  • Julie Swann, NC State
  • Luk Van Wassenhove, Humanitarian Research Group, INSEAD
  • Liz  Igharo, The International Association of Public Health Logisticians (IAPHL)
  • Dominique Zwinkels, People that Deliver
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2018-07-18T01:00:00-04:00 2018-07-20T00:59:00-04:00 2018-07-20T00:59:00-04:00 2018-07-18 05:00:00 2018-07-20 04:59:00 2018-07-20 04:59:00 2018-07-18T01:00:00-04:00 2018-07-20T00:59:00-04:00 America/New_York America/New_York datetime 2018-07-18 01:00:00 2018-07-20 12:59:00 America/New_York America/New_York datetime <![CDATA[HHL 2018 Webpage]]> Joscelyn D. Cooper | Program Manager |

Georgia Institute of Technology – Industrial & Systems Engineering (ISyE)

O: 404.385.1432 |  E: J.Cooper@ISYE.GaTech.edu

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603447 592779 603449 603447 image <![CDATA[10th Annual Health & Humanitarian Logistics Conference]]> image/png 1520452318 2018-03-07 19:51:58 1521644296 2018-03-21 14:58:16 592779 image <![CDATA[2017 Health and Humanitarian Logistics Conference Group Photo]]> image/jpeg 1497638592 2017-06-16 18:43:12 1497638592 2017-06-16 18:43:12 603449 image <![CDATA[Conference Sponsor and attendees]]> image/jpeg 1520453525 2018-03-07 20:12:05 1520539724 2018-03-08 20:08:44
<![CDATA[SCL Course: World Class Sales and Operations Planning (Virtual/Instructor-led)]]> 27233 COURSE DESCRIPTION

This course focuses on defining, executing, and improving the S&OP process. Participants will be introduced to the appropriate stakeholders of S&OP, the importance of S&OP to corporate performance, S&OP cadence, and the use of visionary technology to bring S&OP to the next level. Business cases will be used to show concrete examples of companies where S&OP is effectively applied.

WHO SHOULD ATTEND

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

LEARNING OBJECTIVES

WHAT IS COVERED

]]> Andy Haleblian 1 1652810284 2022-05-17 17:58:04 1652810295 2022-05-17 17:58:15 0 0 event This course focuses on defining, executing, and improving the S&OP process. Participants will be introduced to the appropriate stakeholders of S&OP, the importance of S&OP to corporate performance, S&OP cadence, and the use of visionary technology to bring S&OP to the next level. Business cases will be used to show concrete examples of companies where S&OP is effectively applied.

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2023-01-23T09:00:00-05:00 2023-01-24T13:00:00-05:00 2023-01-24T13:00:00-05:00 2023-01-23 14:00:00 2023-01-24 18:00:00 2023-01-24 18:00:00 2023-01-23T09:00:00-05:00 2023-01-24T13:00:00-05:00 America/New_York America/New_York datetime 2023-01-23 09:00:00 2023-01-24 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course registration page]]> <![CDATA[Course webpage within the SCL website]]> <![CDATA[Supply & Demand Planning Certificate Course Series Flyer]]>
<![CDATA[SCL Course: Financial Decision Making (Virtual/Instructor-led)]]> 27233 Course Description

The course is designed to help participants understand how decisions impact financial performance, identify initiatives to improve company performance, and build better business cases with the overall goal of improving financial acumen and decision-making. The course utilizes hands-on applications, group discussion, and exercises.

The course is comprised of (1) 60-minute pre-course introductory session (February 2 | 1:30-2:30pm ET) and (3) 90-minute instructor-led LIVE group webinars (February 9, 16, 23 | 1:30-3pm ET) with each group webinar requiring (1) 90-minute session of online pre-work to be completed before each webinar (total of 9 hours). Participants will be able to access the online "pre-work" material starting January 26.

Who Should Attend

Early or middle stage career professionals who are or will be responsible for executing organizational strategy tied to an integrated view of the organization (including professionals from distribution and logistics, production, and operations).

How You Will Benefit

What Is Covered

Webinar 1– Managing Financial Performance

Objectives
Pre-work
Topics

Webinar 2 – Improving Financial Performance

Objectives
Pre-work
Topics

Webinar 3 – Building the Better Business Case

Objectives
Pre-work
Topics
]]> Andy Haleblian 1 1652735615 2022-05-16 21:13:35 1652735628 2022-05-16 21:13:48 0 0 event The course is designed to help participants understand how decisions impact financial performance, identify initiatives to improve company performance, and build better business cases with the overall goal of improving financial acumen and decision-making. The course utilizes hands-on applications, group discussion, and exercises.

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2023-02-02T14:30:00-05:00 2023-02-23T16:00:00-05:00 2023-02-23T16:00:00-05:00 2023-02-02 19:30:00 2023-02-23 21:00:00 2023-02-23 21:00:00 2023-02-02T14:30:00-05:00 2023-02-23T16:00:00-05:00 America/New_York America/New_York datetime 2023-02-02 02:30:00 2023-02-23 04:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[ISyE / DCL Seminar - Adam Wierman]]> 34470 Also live streamed at https://gatech.zoom.us/j/96813456832

Zoom Meeting ID: 968 1345 6832

TITLE: Online Optimization and Control using Black-Box Predictions

ABSTRACT:

Making use of modern black-box AI tools is potentially transformational for online optimization and control. However, such machine-learned algorithms typically do not have formal guarantees on their worst-case performance, stability, or safety. So, while their performance may improve upon traditional approaches in “typical” cases, they may perform arbitrarily worse in scenarios where the training examples are not representative due to, e.g., distribution shift or unrepresentative training data. This represents a significant drawback when considering the use of AI tools for energy systems and autonomous cities, which are safety-critical. A challenging open question is thus: Is it possible to provide guarantees that allow black-box AI tools to be used in safety-critical applications? In this talk, I will introduce recent work that aims to develop algorithms that make use of black-box AI tools to provide good performance in the typical case while integrating the “untrusted advice” from these algorithms into traditional algorithms to ensure formal worst-case guarantees. Specifically, we will discuss the use of black-box untrusted advice in the context of online convex body chasing, online non-convex optimization, and linear quadratic control, identifying both novel algorithms and fundamental limits in each case.

BIO: Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at Caltech. He received his Ph.D., M.Sc., and B.Sc. in Computer Science from Carnegie Mellon University and has been a faculty at Caltech since 2007. Adam’s research strives to make the networked systems that govern our world sustainable and resilient. He is best known for his work spearheading the design of algorithms for sustainable data centers and his co-authored book on “The Fundamentals of Heavy-tails”. He is a recipient of multiple awards, including the ACM Sigmetrics Rising Star award, the ACM Sigmetrics Test of Time award, the IEEE Communications Society William R. Bennett Prize, multiple teaching awards, and is a co-author of papers that have received “best paper” awards at a wide variety of conferences across computer science, power engineering, and operations research.

]]> phand3 1 1649692003 2022-04-11 15:46:43 1649875152 2022-04-13 18:39:12 0 0 event 2022-04-19T12:00:00-04:00 2022-04-19T13:00:00-04:00 2022-04-19T13:00:00-04:00 2022-04-19 16:00:00 2022-04-19 17:00:00 2022-04-19 17:00:00 2022-04-19T12:00:00-04:00 2022-04-19T13:00:00-04:00 America/New_York America/New_York datetime 2022-04-19 12:00:00 2022-04-19 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[SCL Seminar: Innovation and Digital Supply Chain at AstraZeneca]]> 27233 Register to Attend the Session

WHEN
Monday, April 18, 12:30-1:45pm ET

WHERE
VIRTUAL via BlueJeans Events (after registration, you will receive a link to the session to the email address provided)

WHAT

Global Operations at AstraZeneca plays a critical ​role in the development, manufacturing, testing ​and delivery of our medicines around the world.​ With the world around us changing rapidly, ​we are evolving our network, our technology and our capabilities to deliver more medicines, to more patients, more quickly and to build a better, more sustainable future. ​In this session we will be exploring how we are embracing Digital Transformation and Pioneering High-Tech New Strategies Across the supply value chain including:

WHO

Gurinder Kaur is the vice president of Operations IT for AstraZeneca, with overall responsibility for the development and execution of end to end supply chain and manufacturing technology and digital strategies. In addition, she is responsible for ensuring the strategic development of digital, data and analytics platforms supporting the global AZ operations function. Reporting into the Chief Digital Officer, Gurinder is a member of the Global Operations and IT Senior Leadership Team. Gurinder has more than 25 years of diverse technology and digital experience in the consumer product goods, automotive and oil and gas industries. She brings a wealth of knowledge and international experience in leveraging technology and digital to drive business results. 

Prior to joining AstraZeneca, Gurinder held the role of Chief Information Officer, Coca Cola North America - driving the company’s digital transformation journey for key functions including Supply Chain, Operations, Marketing and Sales. Before Coca Cola, she served as Vice President - Global Commercial, Robotics and Analytics Solutions for Kellogg Company. Additionally, Gurinder held senior leadership roles with Diageo, Ford Motor Co and Shell Oil in Asia, Australia, UK, Germany and the US. She began her career as a Audit manager with Deloitte in Kuala Lumpur Malaysia. Gurinder earned her Bachelor of Business degree in Accounting from The Royal Melbourne Institute of Technology University (RMIT) in Australia and her MBA from the University of Michigan. She has completed executive leadership studies at Harvard and Stanford University. Ms. Kaur's LinkedIn Profile

Linzell Harris is senior vice president of Global Supply Chain and Strategy for AstraZeneca, with overall responsibility for the development and execution of the end to end supply chain platform and product supply strategies. In addition, he is responsible for ensuring the strategic development of network designs and supply chain capabilities supporting the global AZ operational network. Reporting into the EVP of Operations and Information Technology, Linzell is a member of the Global Operations Senior Leadership Team. Linzell has more than 30 years of diverse general management and global operations experience in the consumer product goods, pharmaceutical and luxury goods industries.

Prior to joining AstraZeneca, Linzell held the role of Senior Vice President, Global Supply Chain for TEVA Pharmaceuticals - supporting the company’s comprehensive global supply network for generic and specialty pharmaceuticals.  Before TEVA, he served as Head of Global Operations for Godiva Chocolatier and as a member of Godiva’s Executive Leadership team reporting to the CEO and President. Additionally, Linzell has worked in Senior Management roles at Johnson and Johnson, Pfizer, Warner Lambert and Baxter Healthcare. He began his working career as a Supply Corp officer in the US Navy. Linzell earned a BS in Mathematics from the US Naval Academy in Annapolis, MD with graduate studies at Villanova University. He has completed executive studies at Michigan State, Columbia and Harvard Universities. Mr. Harris' LinkedIn Profile

*Special Note
The session is being presented as part of ISyE6340: Global Supply Chain Seminar, but is open to the larger Georgia Tech community.

]]> Andy Haleblian 1 1649867848 2022-04-13 16:37:28 1649867963 2022-04-13 16:39:23 0 0 event Global Operations at AstraZeneca plays a critical ​role in the development, manufacturing, testing ​and delivery of our medicines around the world.​ With the world around us changing rapidly, ​we are evolving our network, our technology and our capabilities to deliver more medicines, to more patients, more quickly and to build a better, more sustainable future.

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2022-04-18T13:30:00-04:00 2022-04-18T14:45:00-04:00 2022-04-18T14:45:00-04:00 2022-04-18 17:30:00 2022-04-18 18:45:00 2022-04-18 18:45:00 2022-04-18T13:30:00-04:00 2022-04-18T14:45:00-04:00 America/New_York America/New_York datetime 2022-04-18 01:30:00 2022-04-18 02:45:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

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657249 657249 image <![CDATA[Gurinder Kaur and Linzell Harris, AstraZeneca]]> image/jpeg 1649867435 2022-04-13 16:30:35 1649867817 2022-04-13 16:36:57 <![CDATA[Register to Attend the Session]]>
<![CDATA[Systems Operations and Strategic Interactions in Supply Chains Course (Virtual/Instructor-led)]]> 27233 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm EDT each day. 

Course Description

Often the lack of cooperation and coordination between organizations or stakeholders lead to inefficiencies, despite having common goals. A systems view is needed to ensure appropriate use of scarce resources to meet the multiple, and often conflicting, short- and long-term goals from multiple constituents. This course will focus on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1645715385 2022-02-24 15:09:45 1647949093 2022-03-22 11:38:13 0 0 event This course will focus on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.

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2022-05-02T10:30:00-04:00 2022-05-05T14:00:00-04:00 2022-05-05T14:00:00-04:00 2022-05-02 14:30:00 2022-05-05 18:00:00 2022-05-05 18:00:00 2022-05-02T10:30:00-04:00 2022-05-05T14:00:00-04:00 America/New_York America/New_York datetime 2022-05-02 10:30:00 2022-05-05 02:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu

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<![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Course Details via Center for Health and Humanitarian Systems website]]>
<![CDATA[Inventory Management and Resource Allocation in Supply Chains Course (Virtual/Instructor-led)]]> 34586 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm EDT each day.

Course Description

Many Supply Chain decisions are concerned with the timely and efficient procurement, allocation, and distribution of resources (e.g. funds, supplies, volunteers, money, employees) through a supply chain network. This course will explore methodologies for “medium term” decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and supply chain design.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

]]> jcooper90 1 1566582715 2019-08-23 17:51:55 1647949083 2022-03-22 11:38:03 0 0 event This course will explore methodologies for tactical decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and transportation decisions.

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2022-04-25T10:30:00-04:00 2022-04-28T14:00:00-04:00 2022-04-28T14:00:00-04:00 2022-04-25 14:30:00 2022-04-28 18:00:00 2022-04-28 18:00:00 2022-04-25T10:30:00-04:00 2022-04-28T14:00:00-04:00 America/New_York America/New_York datetime 2022-04-25 10:30:00 2022-04-28 02:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu 

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<![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Course Details via Center for Health and Humanitarian Systems website]]>
<![CDATA[Responsive Supply Chain Design and Operations Course (Virtual/Instructor-led)]]> 27233 Classes will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components. Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm ET each day.

Course Description

Meeting demand in a timely and cost-effective manner is important both in public and private supply chains, and heavily depend on the design and operation of these supply chains. Demand is affected by ongoing factors such as local economy, infrastructure, and geographic location, as well as unexpected events such as natural or manmade disasters or other large-scale disruptions. Designing and operating responsive supply chains requires the consideration of uncertainty in timing, scope, scale, and understanding of various topics such as forecasting, distribution network design, and inventory management. This course will examine methods and models for making supply chain design and operational decisions and explore the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for meeting the need of customers and beneficiaries.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1645714897 2022-02-24 15:01:37 1647949077 2022-03-22 11:37:57 0 0 event This course will examine methods and models for making pre-planning decisions and explore the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for sustaining wellness.

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2022-04-11T10:30:00-04:00 2022-04-14T14:00:00-04:00 2022-04-14T14:00:00-04:00 2022-04-11 14:30:00 2022-04-14 18:00:00 2022-04-14 18:00:00 2022-04-11T10:30:00-04:00 2022-04-14T14:00:00-04:00 America/New_York America/New_York datetime 2022-04-11 10:30:00 2022-04-14 02:00:00 America/New_York America/New_York datetime <![CDATA[]]> chhs@gatech.edu

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<![CDATA[Registration link via Georgia Tech Professional Education]]> <![CDATA[Course Details via Center for Health and Humanitarian Systems website]]>
<![CDATA[ISyE Seminar Speaker- Alexandre Belloni]]> 36086 Abstract: The estimation of causal effects is increasingly relevant in different applied fields. In this work we consider a causal inference problem in the presence of interference. Our focus is on observational studies where interference across units is governed by a known network interference. However, the radius (and intensity) of interference is unknown and can be dependent on the observed treatment assignments in the relevant subnetwork.  We study causal estimators for average direct treatment effect given the network interference. The proposed estimators build upon a Lepski-like procedure that searches over the possible relevant radius/assignment patterns. In the process we also obtain estimators for the radius of the interference that can be dependent on the treatment assignment of neighbors. Thus it is creating an adaptive estimation of the network interference structure. We establish oracle inequalities and corresponding adaptive rates for the direct average treatment effect estimator. The adaptive network interference can be defined over the labelled subgraphs themselves or on features of these recovering many assumptions previously used in the literature.  We present theoretical examples and numerical simulation that illustrate the performance of the proposed estimators.

Bio: Alexandre Belloni is the John D. Forsyth Professor of Business Administration and Statistical Science at Duke University, and is an Amazon Scholar at SCOT. He received his Ph.D. in Operations Research at MIT and a M.Sc. in Mathematical Economics from IMPA. He was an IBM Herman Goldstein Postdoctoral Fellowship at the IBM Thomas J. Watson Research Center. Professor Belloni’s research interests are on machine learning and statistics, mechanism design (e.g. contracts/auctions), optimization and on their applications. His works appeared at top journals in Economics, Operations Research, and Statistics. He serves as Associate Editor to Annals of Statistics, Management Science and as the Area Editor to Operations Research (Machine Learning and Data Science).

 

]]> yrollins3 1 1647278868 2022-03-14 17:27:48 1647278868 2022-03-14 17:27:48 0 0 event Abstract: The estimation of causal effects is increasingly relevant in different applied fields. In this work we consider a causal inference problem in the presence of interference. Our focus is on observational studies where interference across units is governed by a known network interference. However, the radius (and intensity) of interference is unknown and can be dependent on the observed treatment assignments in the relevant subnetwork.  We study causal estimators for average direct treatment effect given the network interference. The proposed estimators build upon a Lepski-like procedure that searches over the possible relevant radius/assignment patterns. In the process we also obtain estimators for the radius of the interference that can be dependent on the treatment assignment of neighbors. Thus it is creating an adaptive estimation of the network interference structure. We establish oracle inequalities and corresponding adaptive rates for the direct average treatment effect estimator. The adaptive network interference can be defined over the labelled subgraphs themselves or on features of these recovering many assumptions previously used in the literature.  We present theoretical examples and numerical simulation that illustrate the performance of the proposed estimators.

Bio: Alexandre Belloni is the John D. Forsyth Professor of Business Administration and Statistical Science at Duke University, and is an Amazon Scholar at SCOT. He received his Ph.D. in Operations Research at MIT and a M.Sc. in Mathematical Economics from IMPA. He was an IBM Herman Goldstein Postdoctoral Fellowship at the IBM Thomas J. Watson Research Center. Professor Belloni’s research interests are on machine learning and statistics, mechanism design (e.g. contracts/auctions), optimization and on their applications. His works appeared at top journals in Economics, Operations Research, and Statistics. He serves as Associate Editor to Annals of Statistics, Management Science and as the Area Editor to Operations Research (Machine Learning and Data Science).

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2022-04-22T12:00:00-04:00 2022-04-22T13:00:00-04:00 2022-04-22T13:00:00-04:00 2022-04-22 16:00:00 2022-04-22 17:00:00 2022-04-22 17:00:00 2022-04-22T12:00:00-04:00 2022-04-22T13:00:00-04:00 America/New_York America/New_York datetime 2022-04-22 12:00:00 2022-04-22 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar Speaker -Sanjay Mehrotra ]]> 36086 Abstract: We study a service center location problem with ambiguous utility gains and uncertain demand. The model is motivated by the problem of deciding medical clinic/service centers, possibly in rural communities, where residents need to visit the clinics to receive health services. A resident gains his utility based on service features such as travel distance and waiting time at the facility that depend on the clinic location. The elicited location-dependent utilities are assumed to be ambiguously described by an expected value and variance constraint. We show that despite a non-convex nonlinearity, given by a constraint specified by a maximum of two second-order functions, the model admits a mixed 0-1 second-order cone (MISOCP) formulation, which leads to a two-stage-MISOCP under uncertain demand. We study the non-convex substructure of the problem, and present methods for developing its strengthened formulations by using valid tangent inequalities. We also develop a new branch-and-cut algorithm for the two-stage-MISOCP problem. Computational study shows the effectiveness of solving the strengthened formulations. Examples are used to illustrate the importance of including decision dependent ambiguity.

Biography: Sanjay Mehrotra is a Professor of Industrial Engineering and Management Sciences at Northwestern University. He is a Fellow of the Institute for Operations Research and Management Sciences (INFORMS), and the cohort leader of 2022 INFORMS Fellow Selection Committee. He is the founding director of the Center for Engineering and Health, which is a part of the Institute for Public Health and Medicine at Northwestern University. He is an expert in methodologies for decision making under uncertainty, and its applications to problems in Health Systems Engineering. He has made major contributions to the areas of Optimization Algorithms and Health Systems Engineering, for which he is known internationally.  Professor Mehrotra’s current methodology research is focused on robust decision making. His health systems engineering work encompasses a wide range of topics that include predictive modeling, hospital operations modeling, and policy modeling while using and developing modern operations research tools. Professor Mehrotra has made seminal contributions to the liver and kidney distribution modeling towards reducing geographic disparity. His current healthcare systems engineering research is focusing on reducing kidney discards, improving the understanding of liver cirrhosis, and developing scalable systems for infectious disease management, and patient centered care. He has been the department editor for the Optimization department and Health Systems Engineering department for the journal IIE-Transactions. He is also the founding co-Editor of Healthcare section for the journal Naval Research Logistics. In the Optimization area Mehrotra is widely known for his predictor-corrector method for solving continuous optimization problems. He has been INFORMS Optimization Society chair and has also served on INFORMS Board of Directors. His research has been funded by NIDDK, NIA, NIBIB, NSF, ONR and DOE. 

 

]]> yrollins3 1 1647261339 2022-03-14 12:35:39 1647261339 2022-03-14 12:35:39 0 0 event Abstract: We study a service center location problem with ambiguous utility gains and uncertain demand. The model is motivated by the problem of deciding medical clinic/service centers, possibly in rural communities, where residents need to visit the clinics to receive health services. A resident gains his utility based on service features such as travel distance and waiting time at the facility that depend on the clinic location. The elicited location-dependent utilities are assumed to be ambiguously described by an expected value and variance constraint. We show that despite a non-convex nonlinearity, given by a constraint specified by a maximum of two second-order functions, the model admits a mixed 0-1 second-order cone (MISOCP) formulation, which leads to a two-stage-MISOCP under uncertain demand. We study the non-convex substructure of the problem, and present methods for developing its strengthened formulations by using valid tangent inequalities. We also develop a new branch-and-cut algorithm for the two-stage-MISOCP problem. Computational study shows the effectiveness of solving the strengthened formulations. Examples are used to illustrate the importance of including decision dependent ambiguity.

Biography: Sanjay Mehrotra is a Professor of Industrial Engineering and Management Sciences at Northwestern University. He is a Fellow of the Institute for Operations Research and Management Sciences (INFORMS), and the cohort leader of 2022 INFORMS Fellow Selection Committee. He is the founding director of the Center for Engineering and Health, which is a part of the Institute for Public Health and Medicine at Northwestern University. He is an expert in methodologies for decision making under uncertainty, and its applications to problems in Health Systems Engineering. He has made major contributions to the areas of Optimization Algorithms and Health Systems Engineering, for which he is known internationally.  Professor Mehrotra’s current methodology research is focused on robust decision making. His health systems engineering work encompasses a wide range of topics that include predictive modeling, hospital operations modeling, and policy modeling while using and developing modern operations research tools. Professor Mehrotra has made seminal contributions to the liver and kidney distribution modeling towards reducing geographic disparity. His current healthcare systems engineering research is focusing on reducing kidney discards, improving the understanding of liver cirrhosis, and developing scalable systems for infectious disease management, and patient centered care. He has been the department editor for the Optimization department and Health Systems Engineering department for the journal IIE-Transactions. He is also the founding co-Editor of Healthcare section for the journal Naval Research Logistics. In the Optimization area Mehrotra is widely known for his predictor-corrector method for solving continuous optimization problems. He has been INFORMS Optimization Society chair and has also served on INFORMS Board of Directors. His research has been funded by NIDDK, NIA, NIBIB, NSF, ONR and DOE. 

 

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2022-03-18T12:00:00-04:00 2022-03-18T13:00:00-04:00 2022-03-18T13:00:00-04:00 2022-03-18 16:00:00 2022-03-18 17:00:00 2022-03-18 17:00:00 2022-03-18T12:00:00-04:00 2022-03-18T13:00:00-04:00 America/New_York America/New_York datetime 2022-03-18 12:00:00 2022-03-18 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar Speaker-Folashade B. Agusto]]> 36086 Abstract

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV2. The disease has led to over 455 million cases, with over 6 million deaths worldwide. In any pandemic, the risk of infection is driven by people's perception of risk of the infection. A number of factors drive public perception of disease risk, these include peoples’ beliefs, knowledge, and information about a disease. In this seminar, I will present two different models for COVID-19 that explore peoples' behavior and their sentiments about the disease in the early period of the pandemic.

In the first model I use game theory and appropriate payoff functions relating to the perception of risk measured using disease incidence and severity of infection to account for a series of human behaviors. Which leads to a complex interplay between the epidemiological model, that affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. In the second model I use tweets from Twitter to account for peoples' sentiments about the disease. It also takes into account negative sentiments driven by misinformation.

The results from these models shows that rational behavior of susceptible individuals can lead to multiple waves of infections; these multiple waves are possible if the rate of social learning of infected individuals is sufficiently high. To reduce the burden of the disease in the community, it is necessary to ensure positive sentiments and feelings, and to incentivize altruistic behavior by infected individuals such as voluntary self-isolation.

Bio:  I'm a trained applied mathematician based in the department of Ecology and Evolutionary Biology at the University of Kansas.

I received my PhD. in Mathematics from the University of Ilorin in Nigeria. 

My work focuses on designing novel models to gain insight on the emergence and re-emergence of infectious diseases of public health importance and how to mitigate the risks they pose to human health. 

I've designed and analyzed novel models for diseases like Ebola, avian influenza, bovine tuberculosis, Johnes disease, toxplasmagondii, Chikungunya, and malaria. My current works are on modeling tick-borne disease across the Great Plains and understanding the role of human behavior on the transmission of COVID-19. 

I'm also involved in capacity building across West Africa by organizing summer schools in mathematical epidemiology and ecology. I have organized summer schools in Benin, Senegal, and Nigeria, and currently seeking funds for a school in Ghana for 2022 Summer.

 

]]> yrollins3 1 1647260482 2022-03-14 12:21:22 1647260482 2022-03-14 12:21:22 0 0 event Abstract

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV2. The disease has led to over 455 million cases, with over 6 million deaths worldwide. In any pandemic, the risk of infection is driven by people's perception of risk of the infection. A number of factors drive public perception of disease risk, these include peoples’ beliefs, knowledge, and information about a disease. In this seminar, I will present two different models for COVID-19 that explore peoples' behavior and their sentiments about the disease in the early period of the pandemic.

In the first model I use game theory and appropriate payoff functions relating to the perception of risk measured using disease incidence and severity of infection to account for a series of human behaviors. Which leads to a complex interplay between the epidemiological model, that affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. In the second model I use tweets from Twitter to account for peoples' sentiments about the disease. It also takes into account negative sentiments driven by misinformation.

The results from these models shows that rational behavior of susceptible individuals can lead to multiple waves of infections; these multiple waves are possible if the rate of social learning of infected individuals is sufficiently high. To reduce the burden of the disease in the community, it is necessary to ensure positive sentiments and feelings, and to incentivize altruistic behavior by infected individuals such as voluntary self-isolation.

Bio:  I'm a trained applied mathematician based in the department of Ecology and Evolutionary Biology at the University of Kansas.

I received my PhD. in Mathematics from the University of Ilorin in Nigeria. 

My work focuses on designing novel models to gain insight on the emergence and re-emergence of infectious diseases of public health importance and how to mitigate the risks they pose to human health. 

I have designed and analyzed novel models for diseases like Ebola, avian influenza, bovine tuberculosis, Johnes disease, toxplasmagondii, Chikungunya, and malaria. My current works are on modeling tick-borne disease across the Great Plains and understanding the role of human behavior on the transmission of COVID-19. 

I'm also involved in capacity building across West Africa by organizing summer schools in mathematical epidemiology and ecology. I have organized summer schools in Benin, Senegal, and Nigeria, and currently seeking funds for a school in Ghana for 2022 Summer.

 

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2022-04-15T12:00:00-04:00 2022-04-15T13:00:00-04:00 2022-04-15T13:00:00-04:00 2022-04-15 16:00:00 2022-04-15 17:00:00 2022-04-15 17:00:00 2022-04-15T12:00:00-04:00 2022-04-15T13:00:00-04:00 America/New_York America/New_York datetime 2022-04-15 12:00:00 2022-04-15 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Department Seminar-Meisam Razaviyayn]]> 36086 Title: “Solving” a class of nonconvx min-max optimization problems" 

Abstract: Recent applications that arise in machine learning have surged significant interest in solving min-max optimization problems. This problem has been extensively studied in the convex-concave regime for which a globally optimal solution can be computed efficiently. In the nonconvex regime, on the other hand, most problems cannot be solved to any reasonable notion of stationarity. In this talk, we present different classes of smooth nonconvex min-max problems that can be solved efficiently up to first-order stationarity of its Moreau envelope. In particular, we propose efficient algorithms for finding (first-order) stationary solutions to nonconvex min-max problems classes when the inner maximization problem is concave or when the diameter of the constraint set for the inner maximization problem is "small". We also discuss the validity of our assumptions in various applications and evaluate the performance of our algorithms on different applications including training adversarial robust neural networks, fair machine learning, data imputation, and training generative adversarial networks.

Bio: Meisam Razaviyayn is an assistant professor of Industrial and Systems Engineering, Electrical Engineering, and Computer Science at the University of Southern California. His research interests include the design and analysis of optimization algorithms for modern problems arising in machine learning applications. His contributions to the field of optimization were recognized through awards such as Signal Processing Society Young Author Best PaperAward, ICCM Best Paper Award in Mathematics, IEEE Data Science Workshop Best Paper Award, and the 3M NTFA award, and AFOSR Young Investigator Prize.

]]> yrollins3 1 1646756857 2022-03-08 16:27:37 1647036902 2022-03-11 22:15:02 0 0 event Abstract:

Recent applications that arise in machine learning have surged significant interest in solving min-max optimization problems. This problem has been extensively studied in the convex-concave regime for which a globally optimal solution can be computed efficiently. In the nonconvex regime, on the other hand, most problems cannot be solved to any reasonable notion of stationarity. In this talk, we present different classes of smooth nonconvex min-max problems that can be solved efficiently up to first-order stationarity of its Moreau envelope. In particular, we propose efficient algorithms for finding (first-order) stationary solutions to nonconvex min-max problems classes when the inner maximization problem is concave or when the diameter of the constraint set for the inner maximization problem is "small". We also discuss the validity of our assumptions in various applications and evaluate the performance of our algorithms on different applications including training adversarial robust neural networks, fair machine learning, data imputation, and training generative adversarial networks.

Bio:

Meisam Razaviyayn is an assistant professor of Industrial and Systems Engineering, Electrical Engineering, and Computer Science at the University of Southern California. His research interests include the design and analysis of optimization algorithms for modern problems arising in machine learning applications. His contributions to the field of optimization were recognized through awards such as Signal Processing Society Young Author Best PaperAward, ICCM Best Paper Award in Mathematics, IEEE Data Science Workshop Best Paper Award, and the 3M NTFA award, and AFOSR Young Investigator Prize.

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2022-03-16T15:00:00-04:00 2022-03-16T16:00:00-04:00 2022-03-16T16:00:00-04:00 2022-03-16 19:00:00 2022-03-16 20:00:00 2022-03-16 20:00:00 2022-03-16T15:00:00-04:00 2022-03-16T16:00:00-04:00 America/New_York America/New_York datetime 2022-03-16 03:00:00 2022-03-16 04:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar Speaker-Oktay Gunluk ]]> 36086 Abstract. We consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints. We formulate the problem as an integer program that maximizes classification accuracy with explicit constraints on equality of opportunity and equalized odds metrics. A column generation framework is used to efficiently search over exponentially many possible rules, eliminating the need for heuristic rule mining. Compared to other interpretable machine learning algorithms, our method produces interpretable classifiers that have superior performance with respect to the fairness metric.

Joint work with Connor Lawless

 

Bio: Oktay Gunluk joined the School of Operations Research and Information Engineering faculty in January 2020. Before joining Cornell, he was the manager of the Mathematical Optimization and Algorithms group at IBM Research. He has also spent three years as a researcher in the Operations Research group in AT&T Labs. At both of these industrial labs, in addition to basic research in mathematical optimization, he has worked on various large-scale applied optimization projects for internal and external customers. His main research interests are related to theoretical and computational aspects of discrete optimization problems, mainly in the area of integer programing. In particular, his main body of work is in the area of cutting planes for mixed-integer sets. Some of his recent work focuses on developing integer programming-based approaches to classification problems in machine learning. He has B.S./M.S. degrees in Industrial Engineering from Boğaziçi University, and M.S./Ph.D. degrees in Operations Research) from Columbia University.

 

]]> yrollins3 1 1646938708 2022-03-10 18:58:28 1646938708 2022-03-10 18:58:28 0 0 event Abstract. We consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints. We formulate the problem as an integer program that maximizes classification accuracy with explicit constraints on equality of opportunity and equalized odds metrics. A column generation framework is used to efficiently search over exponentially many possible rules, eliminating the need for heuristic rule mining. Compared to other interpretable machine learning algorithms, our method produces interpretable classifiers that have superior performance with respect to the fairness metric.

Joint work with Connor Lawless.

 

Bio: Oktay Gunluk joined the School of Operations Research and Information Engineering faculty in January 2020. Before joining Cornell, he was the manager of the Mathematical Optimization and Algorithms group at IBM Research. He has also spent three years as a researcher in the Operations Research group in AT&T Labs. At both of these industrial labs, in addition to basic research in mathematical optimization, he has worked on various large-scale applied optimization projects for internal and external customers. His main research interests are related to theoretical and computational aspects of discrete optimization problems, mainly in the area of integer programing. In particular, his main body of work is in the area of cutting planes for mixed-integer sets. Some of his recent work focuses on developing integer programming-based approaches to classification problems in machine learning. He has B.S./M.S. degrees in Industrial Engineering from Boğaziçi University, and M.S./Ph.D. degrees in Operations Research) from Columbia University.

 

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2022-04-08T12:00:00-04:00 2022-04-08T13:00:00-04:00 2022-04-08T13:00:00-04:00 2022-04-08 16:00:00 2022-04-08 17:00:00 2022-04-08 17:00:00 2022-04-08T12:00:00-04:00 2022-04-08T13:00:00-04:00 America/New_York America/New_York datetime 2022-04-08 12:00:00 2022-04-08 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Department Seminar- Itai Gurvich]]> 36086 Abstract: We consider centralized dynamic matching markets with finitely many agent types and heterogeneous match values. A network topology determines the feasible matches in the market and the value generated from each match. An inherent trade-off arises between short- and long-term objectives. A social planner may delay match decisions to thicken the market and increase match opportunities to generate high value. This inevitably compromises short-term value, and the planner may match greedily to maximize short-term objectives.

A matching policy is hindsight optimal if the policy can (nearly) maximize the total value simultaneously at all times. We first establish that in multi-way networks, where a match can include more than two agent types, acting greedily is suboptimal, and a periodic clearing policy with a carefully chosen period length is hindsight optimal. Interestingly, in two-way networks, where any match includes two agent types, suitably designed greedy policies also achieve hindsight optimality. This implies that there is essentially no positive externality from having agents waiting to form future matches.

Central to our results is the general position gap, ε, which quantifies the stability or the imbalance in the network. No policy can achieve a regret that is lower than the order of 1/ε at all times. This lower bound is achieved by the proposed policies.

The talk is based on joint work with Suleyman Kerimov and Itai Ashlagi.

]]> yrollins3 1 1646919102 2022-03-10 13:31:42 1646929594 2022-03-10 16:26:34 0 0 event Abstract: We consider centralized dynamic matching markets with finitely many agent types and heterogeneous match values. A network topology determines the feasible matches in the market and the value generated from each match. An inherent trade-off arises between short- and long-term objectives. A social planner may delay match decisions to thicken the market and increase match opportunities to generate high value. This inevitably compromises short-term value, and the planner may match greedily to maximize short-term objectives.

A matching policy is hindsight optimal if the policy can (nearly) maximize the total value simultaneously at all times. We first establish that in multi-way networks, where a match can include more than two agent types, acting greedily is suboptimal, and a periodic clearing policy with a carefully chosen period length is hindsight optimal. Interestingly, in two-way networks, where any match includes two agent types, suitably designed greedy policies also achieve hindsight optimality. This implies that there is essentially no positive externality from having agents waiting to form future matches.

Central to our results is the general position gap, ε, which quantifies the stability or the imbalance in the network. No policy can achieve a regret that is lower than the order of 1/ε at all times. This lower bound is achieved by the proposed policies.

The talk is based on joint work with Suleyman Kerimov and Itai Ashlagi.

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2022-04-01T12:00:00-04:00 2022-04-01T13:00:00-04:00 2022-04-01T13:00:00-04:00 2022-04-01 16:00:00 2022-04-01 17:00:00 2022-04-01 17:00:00 2022-04-01T12:00:00-04:00 2022-04-01T13:00:00-04:00 America/New_York America/New_York datetime 2022-04-01 12:00:00 2022-04-01 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[SCL Course: Transforming Supply Chain Management and Performance Analysis (Virtual/Instructor-led)]]> 27233 Course Description

This course is the first in the four-course Supply Chain Analytics Professional certificate program. It prepares you to apply leading-edge analytical methods and technology enablers across the supply chain. You’ll learn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you’ll learn about data cleansing, exploratory data analysis, and visualization. You’ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.

The online version of the course is comprised of (4) half-day instructor-led LIVE group webinars (February 14, 15, 16, 17 | 1-5pm ET) and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1616183508 2021-03-19 19:51:48 1646228511 2022-03-02 13:41:51 0 0 event Learn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you’ll learn about data cleansing, exploratory data analysis, and visualization. You’ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.

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2022-04-18T14:00:00-04:00 2022-04-21T18:00:00-04:00 2022-04-21T18:00:00-04:00 2022-04-18 18:00:00 2022-04-21 22:00:00 2022-04-21 22:00:00 2022-04-18T14:00:00-04:00 2022-04-21T18:00:00-04:00 America/New_York America/New_York datetime 2022-04-18 02:00:00 2022-04-21 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Business Case Development for Operations and Supply Chain Management (Virtual/Instructor-led)]]> 27233 Course Description

The ability to write a compelling business case is a core competency for effective leaders. A business case is a critical requirement before committing to projects, new products or other investments. It is also an argument that needs to convince the recipient to invest in this undertaking rather than others. Rigorous business case preparation reduces the risk of poorly targeted or poorly executed projects, improves strategic alignment of investments and increases the probability of achieving expected returns

This course equips participants with the necessary skills and tools to develop structured business cases. Presented techniques are field-proven and derived from successful implementation. Case exercises are adapted from real situations and projects. 
 

Who Should Attend

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1643656427 2022-01-31 19:13:47 1645719803 2022-02-24 16:23:23 0 0 event The ability to write a compelling business case is a core competency for effective leaders. A business case is a critical requirement before committing to projects, new products or other investments. It is also an argument that needs to convince the recipient to invest in this undertaking rather than others. Rigorous business case preparation reduces the risk of poorly targeted or poorly executed projects, improves strategic alignment of investments and increases the probability of achieving expected returns

This course equips participants with the necessary skills and tools to develop structured business cases. Presented techniques are field-proven and derived from successful implementation. Case exercises are adapted from real situations and projects. 

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2022-10-26T09:00:00-04:00 2022-10-27T18:00:00-04:00 2022-10-27T18:00:00-04:00 2022-10-26 13:00:00 2022-10-27 22:00:00 2022-10-27 22:00:00 2022-10-26T09:00:00-04:00 2022-10-27T18:00:00-04:00 America/New_York America/New_York datetime 2022-10-26 09:00:00 2022-10-27 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Contracting and Legal Oversight (Virtual/Instructor-led)]]> 27233 Course Description

Contracting and Legal Oversight provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.

The online version of the course is comprised of (3) instructor-led LIVE group webinars, homework, and pre-work (e.g. installing and testing software on your computer, testing connectivity with Canvas LMS and BlueJeans meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

This course is ideal for contract managers, procurement professionals, sourcing initiative leaders, project managers and all procurement & supply management-related professionals involved with bid contract development, contract execution or supplier performance management.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1644876272 2022-02-14 22:04:32 1645719728 2022-02-24 16:22:08 0 0 event This course provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.

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2022-09-15T16:00:00-04:00 2022-09-21T18:00:00-04:00 2022-09-21T18:00:00-04:00 2022-09-15 20:00:00 2022-09-21 22:00:00 2022-09-21 22:00:00 2022-09-15T16:00:00-04:00 2022-09-21T18:00:00-04:00 America/New_York America/New_York datetime 2022-09-15 04:00:00 2022-09-21 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Essentials of Negotiations and Stakeholder Influence (Virtual/Instructor-led)]]> 27233 Course Description

Essentials of Negotiations and Stakeholder Influence level-sets the participants' understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style “DNA” to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a “no judgement zone” environment.

Who Should Attend

This course is ideal for sourcing initiative leaders, project leaders, business unit leaders, operations managers, sales leaders and procurement & supply management-related professionals who are involved with supplier selection, contract development and supplier performance management.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1644876660 2022-02-14 22:11:00 1645719711 2022-02-24 16:21:51 0 0 event This course level-sets the participants' understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style “DNA” to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a “no judgement zone” environment.

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2022-09-22T14:00:00-04:00 2022-09-29T17:59:00-04:00 2022-09-29T17:59:00-04:00 2022-09-22 18:00:00 2022-09-29 21:59:00 2022-09-29 21:59:00 2022-09-22T14:00:00-04:00 2022-09-29T17:59:00-04:00 America/New_York America/New_York datetime 2022-09-22 02:00:00 2022-09-29 05:59:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Category Management and Sourcing Leadership (Virtual/Instructor-led)]]> 27233 Course Description

Category Management and Sourcing Leadership is designed to deepen participants' knowledge base of core activities in the procurement & supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation & selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This "hands on" delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.

Who Should Attend

This course is ideal for sourcing initiative leaders, procurement professionals, project managers, finance analyst, contract managers and all procurement & supply management-related professionals involved with bid package development, bid package analysis, negotiations preparation, contracting and supplier selection activity.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1644875745 2022-02-14 21:55:45 1645719551 2022-02-24 16:19:11 0 0 event This course is designed to deepen participants' knowledge base of core activities in the procurement & supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation & selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This "hands on" delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.

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2022-09-12T14:00:00-04:00 2022-09-15T15:30:00-04:00 2022-09-15T15:30:00-04:00 2022-09-12 18:00:00 2022-09-15 19:30:00 2022-09-15 19:30:00 2022-09-12T14:00:00-04:00 2022-09-15T15:30:00-04:00 America/New_York America/New_York datetime 2022-09-12 02:00:00 2022-09-15 03:30:00 America/New_York America/New_York datetime <![CDATA[]]> EMAIL: info@scl.gatech.edu or CALL: (404) 385-3501 between 9:00a.m. and 4:00p.m., Eastern time.

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Principles of Transportation Management (Virtual/Instructor-led)]]> 27233 COURSE DESCRIPTION

This course prepares students in the basics of transportation operations and analysis.  The course includes review of the key elements of transportation such as: modes of transportation, transportation procurement, cost minimization techniques, the role of ports in global logistics, and international trade terms.  The course also will discuss emerging trends in North American transportation markets, emerging techniques, and greenhouse gas emissions reduction.

WHO SHOULD ATTEND

This course is designed for Supply Chain Managers, Distribution Managers, Transportation Planners, Transportation Clerks, Transportation Analysts, and Transportation Managers and learners seeking to enter these roles.  Supply chain professionals from other domains will also benefit through gaining insights into transportation operations.

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

WHAT IS COVERED

]]> Andy Haleblian 1 1632244509 2021-09-21 17:15:09 1645718912 2022-02-24 16:08:32 0 0 event This course prepares students in the basics of transportation operations and analysis.  The course includes review of the key elements of transportation such as: modes of transportation, transportation procurement, cost minimization techniques, the role of ports in global logistics, and international trade terms.  The course also will discuss emerging trends in North American transportation markets, emerging techniques, and greenhouse gas emissions reduction.

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2022-06-21T09:00:00-04:00 2022-06-23T17:59:00-04:00 2022-06-23T17:59:00-04:00 2022-06-21 13:00:00 2022-06-23 21:59:00 2022-06-23 21:59:00 2022-06-21T09:00:00-04:00 2022-06-23T17:59:00-04:00 America/New_York America/New_York datetime 2022-06-21 09:00:00 2022-06-23 05:59:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Supply Chain Project Management Vendor Selection & Management (Virtual/Instructor-led)]]> 27233 Course Description

To keep pace with the continuous moves toward outsourcing of operations and the advancement of technology, companies need to focus on selecting the right suppliers and partnerships to provide the most value to their customers and to remain profitable. This course provides a deeper understanding of the Project Management Body of Knowledge (PMBOK) areas of project integration and procurement, as applied to the supply-chain vendor-selection and management process. You will gain the knowledge, skills, and tools to ensure that you are selecting the right supply-chain partners based on your business goals. In addition, you will learn about alternative techniques for supplier selection, including applied quantitative decision-making techniques.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1630417223 2021-08-31 13:40:23 1645718873 2022-02-24 16:07:53 0 0 event This course provides a deeper understanding of the PMBOK knowledge areas of project integration and procurement applied in the supply chain vendor selection and management process. To keep pace with the continuous moves toward outsourcing of operations and the advancement of technology, companies need to focus on selecting the right suppliers and partnerships to provide the most value to their customers and to remain profitable. This course provides the knowledge, skills, and tools to ensure that you are selecting the right supply chain partners (including 3PL’s) based on your business goals. Emphasis is placed on understanding alternative techniques for supplier selection including applied quantitative decision making techniques.

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2022-06-14T09:00:00-04:00 2022-06-16T18:00:00-04:00 2022-06-16T18:00:00-04:00 2022-06-14 13:00:00 2022-06-16 22:00:00 2022-06-16 22:00:00 2022-06-14T09:00:00-04:00 2022-06-16T18:00:00-04:00 America/New_York America/New_York datetime 2022-06-14 09:00:00 2022-06-16 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage with the SCL website]]>
<![CDATA[SCL Course: Lean Warehousing (Virtual/Instructor-led)]]> 27233 COURSE DESCRIPTION

This course will demonstrate how warehouse operations are a key enabler to a successful supply chain implementation and the starting point for a transformation. It is critical to manage safety, quality and efficiency. Learn to leverage the lean supply chain modifications to improve customer responsiveness and reduce operating costs and in doing so contributing to a supply chain that creates a competitive advantage for a company. To accomplish this goal, we must bring lean principles into the warehouse and distribution center.

WHO SHOULD ATTEND

Supply chain professionals, logistics professionals, material managers, production control managers, transportation managers, warehousing managers and purchasing managers

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

Benefits:

WHAT IS COVERED

]]> Andy Haleblian 1 1632242478 2021-09-21 16:41:18 1645718820 2022-02-24 16:07:00 0 0 event This course will demonstrate how warehouse operations are a key enabler to a successful supply chain implementation and the starting point for a transformation. It is critical to manage safety, quality and efficiency. Learn to leverage the lean supply chain modifications to improve customer responsiveness and reduce operating costs and in doing so contributing to a supply chain that creates a competitive advantage for a company. To accomplish this goal, we must bring lean principles into the warehouse and distribution center.

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2022-06-07T09:00:00-04:00 2022-06-09T17:59:00-04:00 2022-06-09T17:59:00-04:00 2022-06-07 13:00:00 2022-06-09 21:59:00 2022-06-09 21:59:00 2022-06-07T09:00:00-04:00 2022-06-09T17:59:00-04:00 America/New_York America/New_York datetime 2022-06-07 09:00:00 2022-06-09 05:59:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Creating Business Value with Statistical Analysis (Virtual/Instructor-led)]]> 27233 Course Description

This course is the second in the four-course Supply Chain Analytics Professional certificate program. It emphasizes operational performance metrics to align supply chain management with strategic business goals. You’ll learn several statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) along with inventory management models. You’ll use diagnostic analytics with PowerBI and Python to conduct demand and service profiling, undertake root cause analysis, and use time series forecasting in inventory management.

The online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars (March 14, 15, 16, 17 | 1-5pm ET) and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

What Is Covered

]]> Andy Haleblian 1 1618924444 2021-04-20 13:14:04 1645718779 2022-02-24 16:06:19 0 0 event Learn statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) and inventory management models to improve operational performance metrics and align supply chain management with strategic business goals. 

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2022-05-16T14:00:00-04:00 2022-05-19T18:00:00-04:00 2022-05-19T18:00:00-04:00 2022-05-16 18:00:00 2022-05-19 22:00:00 2022-05-19 22:00:00 2022-05-16T14:00:00-04:00 2022-05-19T18:00:00-04:00 America/New_York America/New_York datetime 2022-05-16 02:00:00 2022-05-19 06:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[SCL Course: Supply Chain Risk Management (Virtual/Instructor-led)]]> 27233 COURSE DESCRIPTION

In today’s global economy, operating risks are increasingly on the minds of executives. The specific context of operating risk can range from general areas of business continuity to the effects of natural disasters. In this course participants will gain a solid understanding of Supply Chain Risk Management principals including effective ways to identify, mitigate and measure the impact of potential supply chain disruptions.

WHO SHOULD ATTEND

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

WHAT IS COVERED

]]> Andy Haleblian 1 1634134104 2021-10-13 14:08:24 1645718508 2022-02-24 16:01:48 0 0 event In today’s global economy, operating risks are increasingly on the minds of executives. The specific context of operating risk can range from general areas of business continuity to the effects of natural disasters. In this course participants will gain a solid understanding of Supply Chain Risk Management principals including effective ways to identify, mitigate and measure the impact of potential supply chain disruptions.

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2022-04-27T09:00:00-04:00 2022-04-28T13:00:00-04:00 2022-04-28T13:00:00-04:00 2022-04-27 13:00:00 2022-04-28 17:00:00 2022-04-28 17:00:00 2022-04-27T09:00:00-04:00 2022-04-28T13:00:00-04:00 America/New_York America/New_York datetime 2022-04-27 09:00:00 2022-04-28 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course registration page]]> <![CDATA[Course webpage within the SCL website]]> <![CDATA[Supply & Demand Planning Certificate Course Series Flyer]]>
<![CDATA[SCL Course: World Class Sales and Operations Planning (Virtual/Instructor-led)]]> 27233 COURSE DESCRIPTION

This course focuses on defining, executing, and improving the S&OP process. Participants will be introduced to the appropriate stakeholders of S&OP, the importance of S&OP to corporate performance, S&OP cadence, and the use of visionary technology to bring S&OP to the next level. Business cases will be used to show concrete examples of companies where S&OP is effectively applied.

WHO SHOULD ATTEND

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

LEARNING OBJECTIVES

WHAT IS COVERED

]]> Andy Haleblian 1 1634150369 2021-10-13 18:39:29 1645718457 2022-02-24 16:00:57 0 0 event This course focuses on defining, executing, and improving the S&OP process. Participants will be introduced to the appropriate stakeholders of S&OP, the importance of S&OP to corporate performance, S&OP cadence, and the use of visionary technology to bring S&OP to the next level. Business cases will be used to show concrete examples of companies where S&OP is effectively applied.

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2022-03-28T09:00:00-04:00 2022-03-29T13:00:00-04:00 2022-03-29T13:00:00-04:00 2022-03-28 13:00:00 2022-03-29 17:00:00 2022-03-29 17:00:00 2022-03-28T09:00:00-04:00 2022-03-29T13:00:00-04:00 America/New_York America/New_York datetime 2022-03-28 09:00:00 2022-03-29 01:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course registration page]]> <![CDATA[Course webpage within the SCL website]]> <![CDATA[Supply & Demand Planning Certificate Course Series Flyer]]>
<![CDATA[SCL Course: Financial Decision Making (Virtual/Instructor-led)]]> 27233 Course Description

The course is designed to help participants understand how decisions impact financial performance, identify initiatives to improve company performance, and build better business cases with the overall goal of improving financial acumen and decision-making. The course utilizes hands-on applications, group discussion, and exercises.

The course is comprised of (1) 60-minute pre-course introductory session (March 9 | 1:30-2:30pm ET) and (3) 90-minute instructor-led LIVE group webinars (March 16, 23, 30 | 1:30-3pm ET) with each group webinar requiring (1) 90-minute session of online pre-work to be completed before each webinar (total of 9 hours). Participants will be able to access the online "pre-work" material starting March 2.

Who Should Attend

Early or middle stage career professionals who are or will be responsible for executing organizational strategy tied to an integrated view of the organization (including professionals from distribution and logistics, production, and operations).

How You Will Benefit

What Is Covered

Webinar 1– Managing Financial Performance

Objectives
Pre-work
Topics

Webinar 2 – Improving Financial Performance

Objectives
Pre-work
Topics

Webinar 3 – Building the Better Business Case

Objectives
Pre-work
Topics
]]> Andy Haleblian 1 1618861525 2021-04-19 19:45:25 1645718449 2022-02-24 16:00:49 0 0 event The course is designed to help participants understand how decisions impact financial performance, identify initiatives to improve company performance, and build better business cases with the overall goal of improving financial acumen and decision-making. The course utilizes hands-on applications, group discussion, and exercises.

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2022-03-09T14:30:00-05:00 2022-03-30T16:00:00-04:00 2022-03-30T16:00:00-04:00 2022-03-09 19:30:00 2022-03-30 20:00:00 2022-03-30 20:00:00 2022-03-09T14:30:00-05:00 2022-03-30T16:00:00-04:00 America/New_York America/New_York datetime 2022-03-09 02:30:00 2022-03-30 04:00:00 America/New_York America/New_York datetime <![CDATA[]]> info@scl.gatech.edu

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<![CDATA[Course webpage within the SCL website]]>
<![CDATA[CANCELED - ISyE Seminar - Courtney Paquette ]]> 34977 Title:

Stochastic Algorithms in the Large: Exact Dynamics, Average-case Analysis, and Stepsize Criticality

 

Abstract: 

In this talk, I will present a framework, inspired by random matrix theory, for analyzing the dynamics of stochastic algorithms (e.g., stochastic gradient descent (SGD) and momentum) when both the number of samples and dimensions are large. Using this new framework, we show that the dynamics of stochastic algorithms on a least squares problem with random data become deterministic in the large sample and dimensional limit. Furthermore, the limiting dynamics are governed by a Volterra integral equation. This model predicts that SGD undergoes a phase transition at an explicitly given critical stepsize that ultimately affects its convergence rate, which we also verify experimentally. Finally, when input data is isotropic, we provide explicit expressions for the dynamics and average-case convergence rates. These rates show significant improvement over the worst-case complexities.

 

Bio: 

Courtney Paquette is an assistant professor at McGill University and a CIFAR Canada AI chair. Paquette’s research broadly focuses on designing and analyzing algorithms for large-scale optimization problems, motivated by applications in data science. She received her PhD from the mathematics department at the University of Washington (2017), held postdoctoral positions at Lehigh University (2017-2018) and University of Waterloo (NSF postdoctoral fellowship, 2018-2019), and was a research scientist at Google Research, Brain Montreal (2019-2020).

]]> Julie Smith 1 1641301521 2022-01-04 13:05:21 1645221329 2022-02-18 21:55:29 0 0 event Abstract: 

In this talk, I will present a framework, inspired by random matrix theory, for analyzing the dynamics of stochastic algorithms (e.g., stochastic gradient descent (SGD) and momentum) when both the number of samples and dimensions are large. Using this new framework, we show that the dynamics of stochastic algorithms on a least squares problem with random data become deterministic in the large sample and dimensional limit. Furthermore, the limiting dynamics are governed by a Volterra integral equation. This model predicts that SGD undergoes a phase transition at an explicitly given critical stepsize that ultimately affects its convergence rate, which we also verify experimentally. Finally, when input data is isotropic, we provide explicit expressions for the dynamics and average-case convergence rates. These rates show significant improvement over the worst-case complexities.

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2022-02-22T12:00:00-05:00 2022-02-22T13:00:00-05:00 2022-02-22T13:00:00-05:00 2022-02-22 17:00:00 2022-02-22 18:00:00 2022-02-22 18:00:00 2022-02-22T12:00:00-05:00 2022-02-22T13:00:00-05:00 America/New_York America/New_York datetime 2022-02-22 12:00:00 2022-02-22 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[Supply Chain and Logistics in 2022 and Beyond - Learners and Leaders Breakfast Series]]> 27233 Supply chain and logistics is now a common phrase used by everyone around the world. There are many questions about how to capitalize on the expediting of technology, business model, and innovation change that has been exhibited during the past two years.

Tim Brown, managing director of the Georgia Tech Supply Chain and Logistics Institute, will moderate a panel of experts with topics of discussion ranging from workforce development and future freight flows to the impact on the economic climate.

The event will be hosted in Georgia Tech Savannah, but note that this is a hybrid event (attendees can join in-person or virtually, but you must register to attend.)

Tuesday, April 19, 2022
Supply Chain and Logistics in 2022 and Beyond - Learners & Leaders Breakfast Series at Georgia Tech-Savannah

Cost: Free

Registration: 2022supplychain.eventbrite.com

Moderator:

Tim Brown, Managing Director, Georgia Tech
     Supply Chain and Logistics Institute

Panelists:

Mark Ferzacca, Vice President-Warehousing, Matson Logistics Warehousing, Inc.

Brian Greene, Chief Supply Chain Officer, HMTX Industries

Eric Howell, CEO, Port City Logistics

Sandy Lake, Director, Georgia Center of Innovation for Logistics

Cliff Pyron, Chief Commercial Officer, Georgia Ports Authority

]]> Andy Haleblian 1 1644859773 2022-02-14 17:29:33 1644862850 2022-02-14 18:20:50 0 0 event Tim Brown, managing director of the Georgia Tech Supply Chain and Logistics Institute, will moderate a panel of experts with topics of discussion ranging from workforce development and future freight flows to the impact on the economic climate. 

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2022-04-19T08:30:00-04:00 2022-04-19T10:30:00-04:00 2022-04-19T10:30:00-04:00 2022-04-19 12:30:00 2022-04-19 14:30:00 2022-04-19 14:30:00 2022-04-19T08:30:00-04:00 2022-04-19T10:30:00-04:00 America/New_York America/New_York datetime 2022-04-19 08:30:00 2022-04-19 10:30:00 America/New_York America/New_York datetime <![CDATA[]]> 912-966-7922

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655454 655454 image <![CDATA[Supply Chain and Logistics in 2022 and Beyond]]> image/jpeg 1644861371 2022-02-14 17:56:11 1644861371 2022-02-14 17:56:11 <![CDATA[Register to Attend]]>
<![CDATA[ ISyE Department Seminar – Frank E. Curtis]]> 36086 Title: Algorithms for Deterministically Constrained Stochastic Optimization

Abstract: I will present the recent work by my research group on the design, analysis, and implementation of algorithms for solving continuous nonlinear optimization problems that involve a stochastic objective function and deterministic constraints.  The talk will focus on our sequential quadratic optimization (commonly known as SQP) methods for cases when the constraints are defined by nonlinear systems of equations, which arise in various applications including optimal control, PDE-constrained optimization, and network optimization problems.  One might also consider our techniques for training machine learning (e.g., deep learning) models with constraints.  Much of our recent work focuses on the "fully stochastic" regime in which only stochastic gradient estimates are employed, for which we have derived convergence in expectation results and worst-case iteration complexity bounds that are on par with stochastic gradient methods for the unconstrained setting.  I will also discuss the various extensions that my group is exploring along with other related open questions.

Bio: Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University. He received his Bachelor degree from the College of William and Mary with a double major in Mathematics and Computer Science, received his Master degree and Ph.D. from the Department of Industrial Engineering and Management Science at Northwestern University, and spent two years as a Postdoctoral Researcher in the Courant Institute of Mathematical Sciences at New York University. His research focuses on the design, analysis, and implementation of numerical methods for solving large-scale nonlinear optimization problems. He received an Early Career Award from the Advanced Scientific Computing Research program of the U.S. Department of Energy, and has received funding from various programs of the U.S. National Science Foundation, including through a TRIPODS Institute grant awarded to him and his collaborators at Lehigh, Northwestern, and Boston University. He received, along with Leon Bottou (Facebook AI Research) and Jorge Nocedal (Northwestern), the 2021 SIAM/MOS Lagrange Prize in Continuous Optimization. He was awarded, with James V. Burke (U. of Washington), Adrian Lewis (Cornell), and Michael Overton (NYU), the 2018 INFORMS Computing Society Prize. He and team members Daniel Molzahn (Georgia Tech), Andreas Waechter (Northwestern), Ermin Wei (Northwestern), and Elizabeth Wong (UC San Diego) were awarded second place in the ARPA-E Grid Optimization Competition in 2020. He currently serves as an Associate Editor for Mathematical Programming, SIAM Journal on Optimization, Mathematics of Operations Research, IMA Journal of Numerical Analysis, and Mathematical Programming Computation.

 

]]> yrollins3 1 1644615901 2022-02-11 21:45:01 1644615901 2022-02-11 21:45:01 0 0 event Abstract: I will present the recent work by my research group on the design, analysis, and implementation of algorithms for solving continuous nonlinear optimization problems that involve a stochastic objective function and deterministic constraints.  The talk will focus on our sequential quadratic optimization (commonly known as SQP) methods for cases when the constraints are defined by nonlinear systems of equations, which arise in various applications including optimal control, PDE-constrained optimization, and network optimization problems.  One might also consider our techniques for training machine learning (e.g., deep learning) models with constraints.  Much of our recent work focuses on the "fully stochastic" regime in which only stochastic gradient estimates are employed, for which we have derived convergence in expectation results and worst-case iteration complexity bounds that are on par with stochastic gradient methods for the unconstrained setting.  I will also discuss the various extensions that my group is exploring along with other related open questions.

Bio: Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University. He received his Bachelor degree from the College of William and Mary with a double major in Mathematics and Computer Science, received his Master degree and Ph.D. from the Department of Industrial Engineering and Management Science at Northwestern University, and spent two years as a Postdoctoral Researcher in the Courant Institute of Mathematical Sciences at New York University. His research focuses on the design, analysis, and implementation of numerical methods for solving large-scale nonlinear optimization problems. He received an Early Career Award from the Advanced Scientific Computing Research program of the U.S. Department of Energy, and has received funding from various programs of the U.S. National Science Foundation, including through a TRIPODS Institute grant awarded to him and his collaborators at Lehigh, Northwestern, and Boston University. He received, along with Leon Bottou (Facebook AI Research) and Jorge Nocedal (Northwestern), the 2021 SIAM/MOS Lagrange Prize in Continuous Optimization. He was awarded, with James V. Burke (U. of Washington), Adrian Lewis (Cornell), and Michael Overton (NYU), the 2018 INFORMS Computing Society Prize. He and team members Daniel Molzahn (Georgia Tech), Andreas Waechter (Northwestern), Ermin Wei (Northwestern), and Elizabeth Wong (UC San Diego) were awarded second place in the ARPA-E Grid Optimization Competition in 2020. He currently serves as an Associate Editor for Mathematical Programming, SIAM Journal on Optimization, Mathematics of Operations Research, IMA Journal of Numerical Analysis, and Mathematical Programming Computation.

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2022-03-04T12:00:00-05:00 2022-03-04T13:00:00-05:00 2022-03-04T13:00:00-05:00 2022-03-04 17:00:00 2022-03-04 18:00:00 2022-03-04 18:00:00 2022-03-04T12:00:00-05:00 2022-03-04T13:00:00-05:00 America/New_York America/New_York datetime 2022-03-04 12:00:00 2022-03-04 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar - Manuel Aprile ]]> 34977 Title:

Extended formulations for combinatorial optimization problems

Abstract:

Mixed-integer linear programming is one of the most popular and effective tools to solve optimization problems. Representing our problem of interest via a linear formulation is a key step crucially affecting the performance of the solver. When the original formulation is too large to be processed, one can resort to an extended formulation: while extra variables are added, the number of constraints can be dramatically reduced, allowing for a compact representation.

In this talk I will introduce my research on extended formulations and give some examples of how to construct such formulations for some classical combinatorial optimization problems, using tools from communication complexity, matroid theory and combinatorics. I will then highlight how these theoretical constructions can be effectively used in computational applications.

Bio:

Manuel Aprile has studied mathematics and computer science in Catania (Italy) and Oxford (UK). He obtained his PhD in Discrete Optimization at EPFL (Switzerland) in 2018, under the supervision of Friedrich Eisenbrand and Yuri Faenza. He has been a post-doc in Bruxelles, and he is currently a post-doc at Padua University, Italy.

]]> Julie Smith 1 1644255040 2022-02-07 17:30:40 1644255040 2022-02-07 17:30:40 0 0 event Abstract:

Mixed-integer linear programming is one of the most popular and effective tools to solve optimization problems. Representing our problem of interest via a linear formulation is a key step crucially affecting the performance of the solver. When the original formulation is too large to be processed, one can resort to an extended formulation: while extra variables are added, the number of constraints can be dramatically reduced, allowing for a compact representation.

In this talk I will introduce my research on extended formulations and give some examples of how to construct such formulations for some classical combinatorial optimization problems, using tools from communication complexity, matroid theory and combinatorics. I will then highlight how these theoretical constructions can be effectively used in computational applications.

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2022-02-17T12:00:00-05:00 2022-02-17T13:00:00-05:00 2022-02-17T13:00:00-05:00 2022-02-17 17:00:00 2022-02-17 18:00:00 2022-02-17 18:00:00 2022-02-17T12:00:00-05:00 2022-02-17T13:00:00-05:00 America/New_York America/New_York datetime 2022-02-17 12:00:00 2022-02-17 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Xin Chen ]]> 34977 Title:

Inventory management: new models, challenges, and opportunities

Abstract:

Recent years witness novel business models and new challenges in inventory management. In this talk, I will present several research projects motivated by current practice and collaborations with industry partners: inventory models with large lead times, inventory allocation in omnichannel retailing, and operations in smart shelves. The theoretical and practical challenges and opportunities will be highlighted. I will also briefly introduce some other research projects on data-driven modeling in online platforms and retailing and the application of discrete convex analysis to operations models.

Bio:

Xin Chen is a professor at the University of Illinois at Urbana-Champaign. He obtained his PhD from MIT in 2003, MS from Chinese Academy of Sciences in 1998 and BS from Xiangtan University in 1995. His research interest lies in optimization, data analytics, revenue management and supply chain management. He received the Informs revenue management and pricing section prize in 2009. He is the coauthor of the book “The Logic of Logistics: Theory, Algorithms, and Applications for Logistics and Supply Chain Management (Second Edition, 2005, & Third Edition, 2014)”, and serving as the department editor of logistics and supply chain management of Naval Research Logistics and an associate editor of several journals including Operations Research, Management Science, and Production and Operations Management.

]]> Julie Smith 1 1642694031 2022-01-20 15:53:51 1642694031 2022-01-20 15:53:51 0 0 event Abstract:

Recent years witness novel business models and new challenges in inventory management. In this talk, I will present several research projects motivated by current practice and collaborations with industry partners: inventory models with large lead times, inventory allocation in omnichannel retailing, and operations in smart shelves. The theoretical and practical challenges and opportunities will be highlighted. I will also briefly introduce some other research projects on data-driven modeling in online platforms and retailing and the application of discrete convex analysis to operations models.

]]>
2022-01-31T12:00:00-05:00 2022-01-31T13:00:00-05:00 2022-01-31T13:00:00-05:00 2022-01-31 17:00:00 2022-01-31 18:00:00 2022-01-31 18:00:00 2022-01-31T12:00:00-05:00 2022-01-31T13:00:00-05:00 America/New_York America/New_York datetime 2022-01-31 12:00:00 2022-01-31 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Eunhye Song ]]> 34977 Title:

“Selection of the most probable best”

 

Abstract:

In many business applications, simulation is the primary decision-making tool for a complex stochastic system, where an analytical expression of the problem is unavailable. Often, parameters of these simulators are unknown and must be estimated from data. When plug-in estimates of the parameters are adopted, there is a risk of making a suboptimal decision due to the estimation error in the parameter values. Under this type of model risk, this talk discusses a new decision-making framework in the context of simulation optimization, the most probable best (MPB), is introduced in this talk. The MPB is defined as the solution whose posterior probability of being optimal is the largest given the data when the parameters’ estimation error is modeled with a posterior distribution. Some saliant theoretical properties of the MPB will be discussed including its strong consistency to the optimum under the true parameter as the data size increases. In the second half of the talk, efficient sequential sampling algorithms to find the MPB will be introduced and their asymptotic optimality (in efficiency) will be discussed. To demonstrate business insights the MPB formulation provides, a product portfolio optimization problem, where consumer utility parameters are estimated from conjoint survey data will be presented.

 

Bio:

Eunhye Song is Harold and Inge Marcus Early Career Assistant Professor in Industrial and Manufacturing Engineering at the Penn State University and an Associate of the Institute for Computational and Data Sciences. She earned her PhD in Industrial Engineering and Management Sciences at Northwestern University in 2017 and BS and MS degrees in Industrial and Systems Engineering at KAIST in 2010 and 2012, respectively. Her research interests include simulation design of experiments, uncertainty and risk quantification, and simulation optimization. She received the National Science Foundation CAREER award in 2021 and won an honorable mention at the 2020 INFORMS Junior Faculty Interest Group paper competition. She is an active member of the INFORMS Simulation Society and had served on the society's Underrepresented Minorities & Women committee from 2018 to 2020 and organized the 2021 I-Sim Research Workshop.

]]> Julie Smith 1 1641998348 2022-01-12 14:39:08 1642185413 2022-01-14 18:36:53 0 0 event Abstract:

In many business applications, simulation is the primary decision-making tool for a complex stochastic system, where an analytical expression of the problem is unavailable. Often, parameters of these simulators are unknown and must be estimated from data. When plug-in estimates of the parameters are adopted, there is a risk of making a suboptimal decision due to the estimation error in the parameter values. Under this type of model risk, this talk discusses a new decision-making framework in the context of simulation optimization, the most probable best (MPB), is introduced in this talk. The MPB is defined as the solution whose posterior probability of being optimal is the largest given the data when the parameters’ estimation error is modeled with a posterior distribution. Some saliant theoretical properties of the MPB will be discussed including its strong consistency to the optimum under the true parameter as the data size increases. In the second half of the talk, efficient sequential sampling algorithms to find the MPB will be introduced and their asymptotic optimality (in efficiency) will be discussed. To demonstrate business insights the MPB formulation provides, a product portfolio optimization problem, where consumer utility parameters are estimated from conjoint survey data will be presented.

]]>
2022-02-03T12:00:00-05:00 2022-02-03T13:00:00-05:00 2022-02-03T13:00:00-05:00 2022-02-03 17:00:00 2022-02-03 18:00:00 2022-02-03 18:00:00 2022-02-03T12:00:00-05:00 2022-02-03T13:00:00-05:00 America/New_York America/New_York datetime 2022-02-03 12:00:00 2022-02-03 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Johannes Milz ]]> 34977 Title:

Properties of Monte Carlo Estimators for Risk-Neutral PDE-Constrained Optimization Problems

Abstract:

Complex systems in science and engineering can often be modeled with partial differential equations (PDEs) with uncertain parameters. In order to improve the design of such systems, we formulate a risk-neutral optimization problem with PDE constraints, an infinite-dimensional stochastic program. We apply the sample average approximation (SAA) to the risk-neutral PDE-constrained optimization problem and analyze the consistency of the SAA optimal value and solutions. Our analysis exploits hidden compactness in PDE-constrained optimization problems, allowing us to construct deterministic, compact sets containing the solutions to the risk-neutral problem and those to the SAA problems. Exploiting further problem structure, we establish nonasymptotic sample size estimates using the covering number approach, thereby we shed light on the computational resources needed to obtain accurate solutions.

Bio:

Johannes Milz is research associate at the Technical University of Munich. Johannes' research broadly lies in optimization under uncertainty with a current focus on complexity analysis of and algorithmic design for PDE-constrained optimization problems under uncertainty. He received his doctorate in applied mathematics from the Technical University of Munich in 2021.

]]> Julie Smith 1 1642172561 2022-01-14 15:02:41 1642172561 2022-01-14 15:02:41 0 0 event Abstract:

Complex systems in science and engineering can often be modeled with partial differential equations (PDEs) with uncertain parameters. In order to improve the design of such systems, we formulate a risk-neutral optimization problem with PDE constraints, an infinite-dimensional stochastic program. We apply the sample average approximation (SAA) to the risk-neutral PDE-constrained optimization problem and analyze the consistency of the SAA optimal value and solutions. Our analysis exploits hidden compactness in PDE-constrained optimization problems, allowing us to construct deterministic, compact sets containing the solutions to the risk-neutral problem and those to the SAA problems. Exploiting further problem structure, we establish nonasymptotic sample size estimates using the covering number approach, thereby we shed light on the computational resources needed to obtain accurate solutions.

]]>
2022-01-27T12:00:00-05:00 2022-01-27T13:00:00-05:00 2022-01-27T13:00:00-05:00 2022-01-27 17:00:00 2022-01-27 18:00:00 2022-01-27 18:00:00 2022-01-27T12:00:00-05:00 2022-01-27T13:00:00-05:00 America/New_York America/New_York datetime 2022-01-27 12:00:00 2022-01-27 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Gideon Weiss]]> 36086 Title: Design for parallel skill based service systems.

 

Abstract: 

Service systems with several types of customers and servers subject to a bipartite compatibility graph are in general quite intractable.  I will discuss tractable examples of such systems, and their relation to the simpler tractable model of FCFS bipartite matching, and present a conjecture, and a heuristic based on it, to answer problems of design for large scale general systems

 

Bio-sketch: 

Gideon Weiss is professor emeritus at the Department of Statistics in the University of Haifa, Israel. He has previously been on the faculty of ISYE GA Tech 1983--1994. His research interests are scheduling and control of queueing networks (recent book, Cambridge University Press), and simplex methods for continuous linear programming.

]]> yrollins3 1 1642105537 2022-01-13 20:25:37 1642105537 2022-01-13 20:25:37 0 0 event 2022-02-18T12:00:00-05:00 2022-02-18T13:00:00-05:00 2022-02-18T13:00:00-05:00 2022-02-18 17:00:00 2022-02-18 18:00:00 2022-02-18 18:00:00 2022-02-18T12:00:00-05:00 2022-02-18T13:00:00-05:00 America/New_York America/New_York datetime 2022-02-18 12:00:00 2022-02-18 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Seminar - Ryan Cory-Wright ]]> 34977 Title: 

Mixed-projection conic optimization: A new paradigm for modeling rank constraints

 

Abstract: 

Many central problems in optimization, machine learning, and control theory are equivalent to optimizing a low-rank matrix over a convex set. However, while rank constraints offer unparalleled modeling flexibility, no generic code currently solves these problems to certifiable optimality at even moderate sizes. In this talk, we propose such an approach. To model rank constraints, we introduce symmetric projection matrices that satisfy Y^2 = Y and model the row space of a matrix, the matrix analog of binary variables that satisfy z^2 = z, and model the sparsity of a vector. 

We demonstrate that this modeling paradigm yields tractable convex problems over the non-convex set of projection matrices. Further, we design outer-approximation algorithms to solve low-rank problems to certifiable optimality and demonstrate their efficacy on matrix completion problems. We also study the convex relaxations of low-rank problems, and propose a new preprocessing technique for obtaining strong yet computationally affordable relaxations. The technique leads to a class of new relaxations for several widely-used low-rank models, including matrix completion problems among others. 

Finally, we discuss an ongoing collaboration with OCP-a large Moroccan fertilizer manufacturer-to optimally decarbonize their production process by investing in an appropriate mixture of batteries, solar panels, and transmission lines. All papers mentioned in the talk are available at ryancorywright.github.io

Bio: 

Ryan Cory-Wright is a fifth-year Ph.D. candidate at MIT’s Operations Research Center, advised by Dimitris Bertsimas. His research interests lie at the intersection of optimization, machine learning and statistics, with a focus on their application in energy systems. His current research follows two different threads. First, developing a suite of algorithms that efficiently address interpretable (e.g., sparse or low-rank) optimization problems. Second, integrating renewables within energy markets to combat climate change. He is a recipient of the INFORMS Nicholson Prize (2020), the INFORMS Pierskalla Award (2020), the INFORMS Computing Society Student Paper Award (2019), and the INFORMS Data Mining Section Student Paper Award (2021). 

]]> Julie Smith 1 1641312449 2022-01-04 16:07:29 1641312449 2022-01-04 16:07:29 0 0 event Abstract: 

Many central problems in optimization, machine learning, and control theory are equivalent to optimizing a low-rank matrix over a convex set. However, while rank constraints offer unparalleled modeling flexibility, no generic code currently solves these problems to certifiable optimality at even moderate sizes. In this talk, we propose such an approach. To model rank constraints, we introduce symmetric projection matrices that satisfy Y^2 = Y and model the row space of a matrix, the matrix analog of binary variables that satisfy z^2 = z, and model the sparsity of a vector. 

We demonstrate that this modeling paradigm yields tractable convex problems over the non-convex set of projection matrices. Further, we design outer-approximation algorithms to solve low-rank problems to certifiable optimality and demonstrate their efficacy on matrix completion problems. We also study the convex relaxations of low-rank problems, and propose a new preprocessing technique for obtaining strong yet computationally affordable relaxations. The technique leads to a class of new relaxations for several widely-used low-rank models, including matrix completion problems among others. 

Finally, we discuss an ongoing collaboration with OCP-a large Moroccan fertilizer manufacturer-to optimally decarbonize their production process by investing in an appropriate mixture of batteries, solar panels, and transmission lines. All papers mentioned in the talk are available at ryancorywright.github.io

]]>
2022-01-18T12:00:00-05:00 2022-01-18T13:00:00-05:00 2022-01-18T13:00:00-05:00 2022-01-18 17:00:00 2022-01-18 18:00:00 2022-01-18 18:00:00 2022-01-18T12:00:00-05:00 2022-01-18T13:00:00-05:00 America/New_York America/New_York datetime 2022-01-18 12:00:00 2022-01-18 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Grani A. Hanasusanto ]]> 34977 Title: 

Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach

 

Abstract: 

We consider generic stochastic optimization problems in the presence of side information that enables a more insightful decision. The side information constitutes observable exogenous covariates that alter the conditional probability distribution of the random problem parameters. Decision-makers who adapt their decisions according to the observed side information solve a stochastic optimization problem where the objective function is specified by the conditional expectation of the random cost. If the joint probability distribution is unknown, then the conditional expectation can be approximated in a data-driven manner using the Nadaraya-Watson kernel regression. While the emerging approximation scheme has found successful applications in diverse decision problems under uncertainty, it is largely unknown whether the scheme can provide any reasonable out-of-sample performance guarantees. In this talk, we establish guarantees for the generic problems by leveraging techniques from moderate deviations theory. Our analysis motivates the use of a variance-based regularization scheme which, in general, leads to a non-convex optimization problem. We adopt ideas from distributionally robust optimization to obtain tractable formulations. We present numerical experiments for inventory management and wind energy commitment problems to highlight the effectiveness of our regularization scheme.

 

Bio:

Grani A. Hanasusanto is an Assistant Professor of Operations Research and Industrial Engineering at The University of Texas at Austin (UT). Before joining UT, he was a postdoctoral researcher at the College of Management of Technology at École Polytechnique Fédérale de Lausanne. He holds a PhD degree in Operations Research from Imperial College London and an MSc degree in Financial Engineering from the National University of Singapore. He is the recipient of the 2018 NSF CAREER Award. His research focuses on the design and analysis of tractable solution schemes for decision-making problems under uncertainty, with applications in operations management, energy systems, finance, machine learning and data analytics.

 

]]> Julie Smith 1 1640008110 2021-12-20 13:48:30 1641312063 2022-01-04 16:01:03 0 0 event Abstract: 

We consider generic stochastic optimization problems in the presence of side information that enables a more insightful decision. The side information constitutes observable exogenous covariates that alter the conditional probability distribution of the random problem parameters. Decision-makers who adapt their decisions according to the observed side information solve a stochastic optimization problem where the objective function is specified by the conditional expectation of the random cost. If the joint probability distribution is unknown, then the conditional expectation can be approximated in a data-driven manner using the Nadaraya-Watson kernel regression. While the emerging approximation scheme has found successful applications in diverse decision problems under uncertainty, it is largely unknown whether the scheme can provide any reasonable out-of-sample performance guarantees. In this talk, we establish guarantees for the generic problems by leveraging techniques from moderate deviations theory. Our analysis motivates the use of a variance-based regularization scheme which, in general, leads to a non-convex optimization problem. We adopt ideas from distributionally robust optimization to obtain tractable formulations. We present numerical experiments for inventory management and wind energy commitment problems to highlight the effectiveness of our regularization scheme.

]]>
2022-01-10T12:00:00-05:00 2022-01-10T13:00:00-05:00 2022-01-10T13:00:00-05:00 2022-01-10 17:00:00 2022-01-10 18:00:00 2022-01-10 18:00:00 2022-01-10T12:00:00-05:00 2022-01-10T13:00:00-05:00 America/New_York America/New_York datetime 2022-01-10 12:00:00 2022-01-10 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Weijun Xie ]]> 34977 Title: 

ALSO-X and ALSO-X+: Better Convex Approximations for Chance Constrained Programs


Abstract: 

Chance constrained programs (CCPs) are generic frameworks for decision-making under uncertain constraints. The objective of a CCP is to find the best decision that violates the uncertainty constraints within the prespecified risk level. A CCP is often nonconvex and is difficult to solve to optimality. This paper studies and generalizes the ALSO-X, originally proposed by Ahmed, Luedtke, SOng, and Xie (2017), for solving a CCP. We first show that the ALSO-X resembles a bilevel optimization, where the upper-level problem is to find the best objective function value and enforce the feasibility of a CCP for a given decision from the lower-level problem, and the lower-level problem is to minimize the expectation of constraint violations subject to the upper bound of the objective function value provided by the upper-level problem. This interpretation motivates us to prove that when uncertain constraints are convex in the decision variables, ALSO-X always outperforms the state-of-art conditional-value-ta-risk (CVaR) approximation. We further show (i) sufficient conditions under which ALSO-X can recover an optimal solution to a CCP; (ii) an equivalent bilinear programming formulation of a CCP, inspiring us to enhance ALSO-X with a convergent alternating minimization method (ALSO-X+); (iii) extensions of ALSO-X and ALSO-X+ to solve distributionally robust chance constrained programs (DRCCPs) under Wasserstein ambiguity set. Our numerical study demonstrates the effectiveness of the proposed methods.

 

Bio:

Dr. Weijun Xie is an Assistant Professor of Industrial and Systems Engineering, Virginia Tech. He obtained his Ph.D. from Georgia Tech in 2017. His research interests lie in theory and applications of stochastic, discrete, and convex optimization. Dr. Xie has won multiple awards including NSF Career Award, INFORMS Optimization Prize for Young Researchers, INFORMS Junior Faculty Interest Group Paper Competition (Third Place), INFORMS George Nicholson Student Paper Competition (Honorable Mention). He currently serves as the Vice Chair of Optimization under Uncertainty at INFORMS Optimization Society and is an associate editor of the Journal of Global Optimization.

]]> Julie Smith 1 1637703284 2021-11-23 21:34:44 1641300869 2022-01-04 12:54:29 0 0 event Abstract: 

Chance constrained programs (CCPs) are generic frameworks for decision-making under uncertain constraints. The objective of a CCP is to find the best decision that violates the uncertainty constraints within the prespecified risk level. A CCP is often nonconvex and is difficult to solve to optimality. This paper studies and generalizes the ALSO-X, originally proposed by Ahmed, Luedtke, SOng, and Xie (2017), for solving a CCP. We first show that the ALSO-X resembles a bilevel optimization, where the upper-level problem is to find the best objective function value and enforce the feasibility of a CCP for a given decision from the lower-level problem, and the lower-level problem is to minimize the expectation of constraint violations subject to the upper bound of the objective function value provided by the upper-level problem. This interpretation motivates us to prove that when uncertain constraints are convex in the decision variables, ALSO-X always outperforms the state-of-art conditional-value-ta-risk (CVaR) approximation. We further show (i) sufficient conditions under which ALSO-X can recover an optimal solution to a CCP; (ii) an equivalent bilinear programming formulation of a CCP, inspiring us to enhance ALSO-X with a convergent alternating minimization method (ALSO-X+); (iii) extensions of ALSO-X and ALSO-X+ to solve distributionally robust chance constrained programs (DRCCPs) under Wasserstein ambiguity set. Our numerical study demonstrates the effectiveness of the proposed methods.

]]>
2022-01-13T12:00:00-05:00 2022-01-13T13:00:00-05:00 2022-01-13T13:00:00-05:00 2022-01-13 17:00:00 2022-01-13 18:00:00 2022-01-13 18:00:00 2022-01-13T12:00:00-05:00 2022-01-13T13:00:00-05:00 America/New_York America/New_York datetime 2022-01-13 12:00:00 2022-01-13 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[SCL February 2022 Supply Chain Days]]> 27233 Georgia Tech Supply Chain students, please join us for our second fall Supply Chain Days! We will be hosting both an On Campus (Feb 3) and a Virtual session (Feb 4). Please note that you need to register separately for each event to attend.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).
 

EVENT DETAILS

On Campus/In-Person (ISyE Main Building Atrium)

Thursday, February 3 | 11am-2pm ET

Virtual/Online (Career Fair Plus)

Friday, February 4 | 9am - 3pm ET

 

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday for a list of attending organizations and links to register.

 

]]> Andy Haleblian 1 1638911876 2021-12-07 21:17:56 1639150350 2021-12-10 15:32:30 0 0 event Georgia Tech Supply Chain students, please join us for our spring Supply Chain Days! We will be hosting both an On Campus (Feb 3) and a Virtual session (Feb 4). Please note that you need to register separately for each event to attend.

]]>
2022-02-03T12:00:00-05:00 2022-02-04T16:00:00-05:00 2022-02-04T16:00:00-05:00 2022-02-03 17:00:00 2022-02-04 21:00:00 2022-02-04 21:00:00 2022-02-03T12:00:00-05:00 2022-02-04T16:00:00-05:00 America/New_York America/New_York datetime 2022-02-03 12:00:00 2022-02-04 04:00:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

]]>
653473 653473 image <![CDATA[SCL February 2022 Supply Chain Days]]> image/jpeg 1638911868 2021-12-07 21:17:48 1638911868 2021-12-07 21:17:48 <![CDATA[Register online to attend (for Georgia Tech students)]]> <![CDATA[Supply Chain and Logistics Institute website]]>
<![CDATA[ISyE Seminar - Meng Qi ]]> 34977 Title:

Smarter data-driven decision-making by integrating prediction and optimization

 

Abstract:

Big data provides new opportunities to tackle one of the main difficulties in decision-making systems – uncertain behavior driven by the unknown probability distribution. Instead of the classical two-step predict-then-optimize (PTO) procedure, we provide smarter data-driven solutions by integrating these two steps. In the first half of this talk, we focus on a multi-period inventory replenishment problem with uncertain demand and vendor lead time (VLT), with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep-learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations, without any prior assumptions on the distributions of the demand and the VLT. This algorithm is currently implemented in production at JD.com to replenish thousands of products. In the second half of this talk, I will move to a more general setting of the contextual stochastic optimization problem. We propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution using data while considering the structure of the downstream optimization problem. We show that our ICEO approach is asymptotically consistent and further provide finite performance guarantees in the form of generalization bounds. We also discuss the computational difficulties of performing the ICEO approach and propose a general methodology by approximating the potential non-differentiable oracle. We also provide a polynomial optimization solution approach in the semi-algebraic case. The concept of E2E, which uses the input information directly for the ultimate goal, shortens the decision process and can also be useful in practice for a wide range of circumstances beyond supply chain management.

 

Bio:

Meng Qi is a Ph.D. Candidate in the Department of Industrial Engineering and Operations Research at University of California, Berkeley, where she is advised by Prof. Zuo-Jun (Max) Shen. Previously, she graduated from Tsinghua University with a B.S. in Physics. Her research focuses on developing more automatic and robust data-driven solutions for decision-making with uncertainty, combining tools and concepts from optimization, machine learning, and statistics. From an applications perspective, her research focuses on supply chain management and retail operations. As a part of it, she actively collaborates with industrial partners in e-commerce.

]]> Julie Smith 1 1637587398 2021-11-22 13:23:18 1637587398 2021-11-22 13:23:18 0 0 event Abstract:

Big data provides new opportunities to tackle one of the main difficulties in decision-making systems – uncertain behavior driven by the unknown probability distribution. Instead of the classical two-step predict-then-optimize (PTO) procedure, we provide smarter data-driven solutions by integrating these two steps. In the first half of this talk, we focus on a multi-period inventory replenishment problem with uncertain demand and vendor lead time (VLT), with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep-learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations, without any prior assumptions on the distributions of the demand and the VLT. This algorithm is currently implemented in production at JD.com to replenish thousands of products. In the second half of this talk, I will move to a more general setting of the contextual stochastic optimization problem. We propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution using data while considering the structure of the downstream optimization problem. We show that our ICEO approach is asymptotically consistent and further provide finite performance guarantees in the form of generalization bounds. We also discuss the computational difficulties of performing the ICEO approach and propose a general methodology by approximating the potential non-differentiable oracle. We also provide a polynomial optimization solution approach in the semi-algebraic case. The concept of E2E, which uses the input information directly for the ultimate goal, shortens the decision process and can also be useful in practice for a wide range of circumstances beyond supply chain management.

]]>
2021-12-02T12:00:00-05:00 2021-12-02T13:00:00-05:00 2021-12-02T13:00:00-05:00 2021-12-02 17:00:00 2021-12-02 18:00:00 2021-12-02 18:00:00 2021-12-02T12:00:00-05:00 2021-12-02T13:00:00-05:00 America/New_York America/New_York datetime 2021-12-02 12:00:00 2021-12-02 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Seminar - Chamsi Hssaine ]]> 34977 Title:

Pseudo-Competitive Games and Algorithmic Pricing

 

Abstract:

Algorithmic pricing is increasingly a staple of e-commerce platform operations; however, while such data-driven pricing techniques are known to work well in non-strategic environments, their performance in competitive settings remains poorly understood. To this end, we investigate market outcomes that may arise when multiple competing firms deploy local price experimentation algorithms while treating their market environment as a black-box. For price-competition games induced by a broad class of well-validated customer behavior models, we demonstrate that price trajectories resulting from natural local learning dynamics may converge to outcomes in which firms can experience unbounded losses in revenue compared to the best price equilibrium. We moreover design a novel learning algorithm to address this concern. 

This work falls under a broader range of questions in people-centric operations, wherein new markets and platforms fail to fully harness advances in optimization and AI due to inadequately accounting for the utilities of agents, firms, and society as a whole. Such questions arise both in competitive settings, as discussed above, but also in collaborative settings; I will highlight this in the latter part of my talk by briefly discussing my work on the design of multi-modal transportation systems.

 

Bio: 

Chamsi Hssaine is a final-year Ph.D. candidate in the School of Operations Research and Information Engineering at Cornell University, where she is advised by Professor Sid Banerjee. She graduated magna cum laude from Princeton University in 2016, with a B.S. in Operations Research and Financial Engineering. Her research centers around algorithm and incentive design for smart societal systems, with a focus on incorporating more realistic models of behavior under incentives, and better understanding the effect of policy decisions on stakeholders. Chamsi was selected for the 2020 Rising Stars in EECS workshop at UC Berkeley, as well as the 2020 Rising Scholars conference at the Stanford Graduate School of Business. In 2019, she was a visitor at the Simons Institute for the program on Online and Matching-Based Market Design. Her paper "Real-Time Approximate Routing for Smart Transit Systems" (joint with Sid Banerjee, Noémie Périvier, and Samitha Samaranayake) was a finalist for the 2021 INFORMS Minority Issues Forum Paper Competition.

]]> Julie Smith 1 1637249170 2021-11-18 15:26:10 1637249170 2021-11-18 15:26:10 0 0 event Abstract:

Algorithmic pricing is increasingly a staple of e-commerce platform operations; however, while such data-driven pricing techniques are known to work well in non-strategic environments, their performance in competitive settings remains poorly understood. To this end, we investigate market outcomes that may arise when multiple competing firms deploy local price experimentation algorithms while treating their market environment as a black-box. For price-competition games induced by a broad class of well-validated customer behavior models, we demonstrate that price trajectories resulting from natural local learning dynamics may converge to outcomes in which firms can experience unbounded losses in revenue compared to the best price equilibrium. We moreover design a novel learning algorithm to address this concern. 

This work falls under a broader range of questions in people-centric operations, wherein new markets and platforms fail to fully harness advances in optimization and AI due to inadequately accounting for the utilities of agents, firms, and society as a whole. Such questions arise both in competitive settings, as discussed above, but also in collaborative settings; I will highlight this in the latter part of my talk by briefly discussing my work on the design of multi-modal transportation systems.

]]>
2021-11-30T12:00:00-05:00 2021-11-30T13:00:00-05:00 2021-11-30T13:00:00-05:00 2021-11-30 17:00:00 2021-11-30 18:00:00 2021-11-30 18:00:00 2021-11-30T12:00:00-05:00 2021-11-30T13:00:00-05:00 America/New_York America/New_York datetime 2021-11-30 12:00:00 2021-11-30 01:00:00 America/New_York America/New_York datetime <![CDATA[ISyE Building ]]>
<![CDATA[ISyE Department Seminar - Dmitriy Drusvyatskiy]]> 34868 Title:

Stochastic optimization under distributional shifts

Abstract:

Learning problems commonly exhibit an interesting feedback
mechanism wherein the population data reacts to decision makers'
actions. This is the case for example when members of the population
respond to a deployed classifier by manipulating their features so as
to improve the likelihood of being positively labeled. In this way,
the population is manipulating the learning process by distorting the
data distribution that is accessible to the learner. In this talk, I will present some recent modelling frameworks and algorithms for dynamic problems of this type, rooted in stochastic optimization and game theory.

Joint work with Evan Faulkner (UW), Maryam Fazel (UW), Adhyyan Narang
(UW), Lillian J. Ratliff (UW), Lin Xiao (Facebook AI)

Bio:

Dmitriy Drusvyatskiy received his PhD from the Operations
Research and Information Engineering department at Cornell University
in 2013, followed by a post doctoral appointment in the Combinatorics
and Optimization department at Waterloo, 2013-2014. He joined the
Mathematics department at University of Washington as an Assistant
Professor in 2014, and was promoted to an Associate Professor in 2019.
Dmitriy's research broadly focuses on designing and analyzing
algorithms for large-scale optimization problems, primarily motivated
by applications in data science. Dmitriy has received a number of
awards, including the Air Force Office of Scientific Research (AFOSR)
Young Investigator Program (YIP) Award, NSF CAREER, INFORMS
Optimization Society Young Researcher Prize 2019, and finalist
citations for the Tucker Prize 2015 and the Young Researcher Best
Paper Prize at ICCOPT 2019. Dmitriy is currently a co-PI of the NSF
funded Transdisciplinary Research in Principles of Data Science
(TRIPODS) institute at University of Washington.

Research currently supported by NSF CAREER DMS 1651851 and NSF CCF 1740551.

]]> sbryantturner3 1 1630353322 2021-08-30 19:55:22 1637084587 2021-11-16 17:43:07 0 0 event Abstract: 

Learning problems commonly exhibit an interesting feedback
mechanism wherein the population data reacts to decision makers'
actions. This is the case for example when members of the population
respond to a deployed classifier by manipulating their features so as
to improve the likelihood of being positively labeled. In this way,
the population is manipulating the learning process by distorting the
data distribution that is accessible to the learner. In this talk, I will

present some recent modelling frameworks and algorithms for dynamic
problems of this type, rooted in stochastic optimization and game
theory.

Joint work with Evan Faulkner (UW), Maryam Fazel (UW), Adhyyan Narang
(UW), Lillian J. Ratliff (UW), Lin Xiao (Facebook AI)
 

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<![CDATA[ISyE Seminar - Nils Boysen]]> 34977 Title:

E-commerce warehousing and some new results on picker routing

Abstract:

In the wake of ever-increasing e-commerce sales, warehouses have evolved to technology-enriched, mission-critical fulfillment factories. This talk reviews suitable e-commerce warehouse structures such as scattered storage and robot-assisted order picking and investigates the routing problems that are to be solved within these novel warehouses. In the very core of traditional picker-to-parts warehouses is the classical picker routing problem, which equals the traveling salesman problem (TSP) but is well-known to be efficiently solvable in the parallel-aisle structure of warehouses. New warehouses require the solution of other well-known extended routing problems, such as the clustered TSP, the generalized TSP, and the prize collecting TSP. All these routing problems are well-known to be strongly NP-hard for general graphs. This talk shows how the warehouse structure impacts this complexity status and how the parallel-aisle structure of warehouses can be exploited to improve the efficiency of routing algorithms.

Bio:

After some industry practice at IBM Global Services, Nils joined the Friedrich Schiller University in Jena (Germany), where he became a full professor for operations management. His main research interests are in the fields of facility logistics, warehousing, transportation, and automobile production. To solve industry problems in these areas, Nils applies mathematical modelling and combinatorial optimization techniques, always based on a thorough analysis of computational complexity. He has published over 150 research papers in many of the top optimization and logistics journals. Among others he is a member of the editorial boards of Transportation Science and EJOR.

]]> Julie Smith 1 1636638282 2021-11-11 13:44:42 1636638282 2021-11-11 13:44:42 0 0 event Abstract:

In the wake of ever-increasing e-commerce sales, warehouses have evolved to technology-enriched, mission-critical fulfillment factories. This talk reviews suitable e-commerce warehouse structures such as scattered storage and robot-assisted order picking and investigates the routing problems that are to be solved within these novel warehouses. In the very core of traditional picker-to-parts warehouses is the classical picker routing problem, which equals the traveling salesman problem (TSP) but is well-known to be efficiently solvable in the parallel-aisle structure of warehouses. New warehouses require the solution of other well-known extended routing problems, such as the clustered TSP, the generalized TSP, and the prize collecting TSP. All these routing problems are well-known to be strongly NP-hard for general graphs. This talk shows how the warehouse structure impacts this complexity status and how the parallel-aisle structure of warehouses can be exploited to improve the efficiency of routing algorithms.

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<![CDATA[DCL Seminar Speaker - Abhishek Gupta]]> 34470 TITLE: Cyberattack Detection through Dynamic Watermarking

ABSTRACT:

Dynamic watermarking, as an active intrusion detection technique, can potentially detect replay attacks, spoofing attacks, and deception attacks in the feedback channel for control systems. In this talk, we will discuss our recent work on a novel dynamic watermarking algorithm for finite-state finite-action Markov decision processes and present bounds on the mean time between false alarms, and the mean delay between the time an attack occurs and when it is detected. We further compute the sensitivity of the performance of the control system as a function of the watermark. We demonstrate the effectiveness of the proposed dynamic watermarking algorithm by detecting a spoofing attack in a sensor network system.

Bio: Abhishek Gupta is an assistant professor at Electrical and Computer Engineering at The Ohio State University. He completed his Ph.D. in Aerospace Engineering (2014), MS in Applied Mathematics (2012), and MS in Aerospace Engineering (2011), all from University of Illinois at Urbana-Champaign (UIUC). He completed his undergraduate in Aerospace Engineering from Indian Institute of Technology, Bombay, India (2005-09). His research develops new theory and algorithms for stochastic control problems, games, and optimization problems, with applications to secure cyberphysical systems and develop market mechanisms for deep renewable integration. He is a recipient of Kenneth Lee Herrick Memorial Award at UIUC and Lumley Research Award at OSU.

]]> phand3 1 1636402343 2021-11-08 20:12:23 1636402343 2021-11-08 20:12:23 0 0 event 2021-11-16T12:00:00-05:00 2021-11-16T13:00:00-05:00 2021-11-16T13:00:00-05:00 2021-11-16 17:00:00 2021-11-16 18:00:00 2021-11-16 18:00:00 2021-11-16T12:00:00-05:00 2021-11-16T13:00:00-05:00 America/New_York America/New_York datetime 2021-11-16 12:00:00 2021-11-16 01:00:00 America/New_York America/New_York datetime <![CDATA[]]>
<![CDATA[ISyE Department Seminar- Karen Smilowitz]]> 34868 Title:

Integrating dual scheduling modes in workforce management

Abstract:

Motivated by the emergence of self-scheduling platforms for volunteers and the continued need to meet time-specific needs of nonprofit organizations, this research explores modeling approaches and scheduling policies to effectively manage workforce scheduling for organizations with dual scheduling modes.  As an illustrative example, we consider a nonprofit organization that provides relief to those impacted by disasters using both volunteers who schedule themselves and staff members who are assigned to shifts.  We explore the advantages of scheduling policies that explicitly account for the two groups, balancing the need to cover time slots to meet demand with the desire to offer meaningful and convenient opportunities to volunteers such that they maintain their engagement with the organization.  We present a case study based on operational data from our collaborators and more general insights based on synthesized data.  This is joint work with Mariana Escallon-Barrios and Reut Noham.

Bio:

Dr. Karen Smilowitz is the James N. and Margie M. Krebs Professor in Industrial Engineering and Management Science at Northwestern University, with a joint appointment in the Operations group at the Kellogg School of Business.  Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications.  Dr.  Smilowitz is the founder of the Northwestern Initiative on Humanitarian and Nonprofit Logistics.  She has been instrumental in promoting the use of operations research within the humanitarian and nonprofit sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets.  Dr. Smilowitz is the Editor-in-Chief of Transportation Science.  

]]> sbryantturner3 1 1630353231 2021-08-30 19:53:51 1636401185 2021-11-08 19:53:05 0 0 event Abstract:

Motivated by the emergence of self-scheduling platforms for volunteers and the continued need to meet time-specific needs of nonprofit organizations, this research explores modeling approaches and scheduling policies to effectively manage workforce scheduling for organizations with dual scheduling modes.  As an illustrative example, we consider a nonprofit organization that provides relief to those impacted by disasters using both volunteers who schedule themselves and staff members who are assigned to shifts.  We explore the advantages of scheduling policies that explicitly account for the two groups, balancing the need to cover time slots to meet demand with the desire to offer meaningful and convenient opportunities to volunteers such that they maintain their engagement with the organization.  We present a case study based on operational data from our collaborators and more general insights based on synthesized data.  This is joint work with Mariana Escallon-Barrios and Reut Noham.

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<![CDATA[SCL November 2021 Supply Chain Days]]> 27233 Georgia Tech Supply Chain students, please join us for our second fall Supply Chain Days! We will be hosting both an On Campus (Nov 11) and a Virtual session (Nov 12). Please note that you need to register separately for each event to attend.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).
 

EVENT DETAILS

On Campus/In-Person (ISyE Main Building Atrium)

Thursday, November 11 | 11am-2pm ET

Virtual/Online (Career Fair Plus)

Friday, November 12 | 9am - 3pm ET

 

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday for a list of attending organizations and links to register.


EVENT SPONSOR

The event is sponsored through the generosity and support of the CSCMP Atlanta Roundtable. Students, young professionals, academics and military personnel are eligible for a discounted membership. Make sure to stop by the Atlanta CSCMP table at our on campus event and visit https://www.atlantacscmp.org/join.

]]> Andy Haleblian 1 1633373481 2021-10-04 18:51:21 1635951371 2021-11-03 14:56:11 0 0 event Georgia Tech Supply Chain students, please join us for our second fall Supply Chain Days! We will be hosting both an On Campus (Nov 11) and a Virtual session (Nov 12). Please note that you need to register separately for each event to attend.

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2021-11-11T12:00:00-05:00 2021-11-12T15:00:00-05:00 2021-11-12T15:00:00-05:00 2021-11-11 17:00:00 2021-11-12 20:00:00 2021-11-12 20:00:00 2021-11-11T12:00:00-05:00 2021-11-12T15:00:00-05:00 America/New_York America/New_York datetime 2021-11-11 12:00:00 2021-11-12 03:00:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

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651357 651357 image <![CDATA[SCL November 2021 Supply Chain Days]]> image/jpeg 1633373432 2021-10-04 18:50:32 1635951539 2021-11-03 14:58:59 <![CDATA[Register online to attend (for Georgia Tech students)]]> <![CDATA[Supply Chain and Logistics Institute website]]>
<![CDATA[2020 Monie A. Ferst Award Symposium]]> 34760 To view the program and event details, visit https://sites.gatech.edu/2020-monie-a-ferst-award-symposium.

]]> Laurie Haigh 1 1635250996 2021-10-26 12:23:16 1635258590 2021-10-26 14:29:50 0 0 event 2021-11-11T09:00:00-05:00 2021-11-11T18:15:00-05:00 2021-11-11T18:15:00-05:00 2021-11-11 14:00:00 2021-11-11 23:15:00 2021-11-11 23:15:00 2021-11-11T09:00:00-05:00 2021-11-11T18:15:00-05:00 America/New_York America/New_York datetime 2021-11-11 09:00:00 2021-11-11 06:15:00 America/New_York America/New_York datetime <![CDATA[Marcus Nanotechnology Research Center]]> Sigma Xi

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<![CDATA[Jeff Wu Receives Sigma Xi’s Monie A. Ferst Award]]>
<![CDATA[ RELEX Solutions Information Session at ISyE]]> 27233 Join us to learn about RELEX Solutions and opportunities relating to technical and business consulting positions!

Please RSVP to attend, so we have enough representatives and food for the attendees.

]]> Andy Haleblian 1 1634840141 2021-10-21 18:15:41 1634840366 2021-10-21 18:19:26 0 0 event Join us to learn about RELEX Solutions and opportunities relating to technical and business consulting positions!

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2021-11-10T12:00:00-05:00 2021-11-10T13:30:00-05:00 2021-11-10T13:30:00-05:00 2021-11-10 17:00:00 2021-11-10 18:30:00 2021-11-10 18:30:00 2021-11-10T12:00:00-05:00 2021-11-10T13:30:00-05:00 America/New_York America/New_York datetime 2021-11-10 12:00:00 2021-11-10 01:30:00 America/New_York America/New_York datetime <![CDATA[]]> event@scl.gatech.edu

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651934 651934 image <![CDATA[RELEX Solutions Information Session]]> image/jpeg 1634840317 2021-10-21 18:18:37 1634840317 2021-10-21 18:18:37 <![CDATA[Register Online to Attend]]> <![CDATA[Event flyer]]>
<![CDATA[ISyE Department Seminar- Jianqing Fan]]> 34868 Title: Understanding Deep Q-learning

Abstract: Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep Q-network (DQN) algorithm from both algorithmic and statistical perspectives. Specifically, we focus on a slight simplification of DQN that fully captures its key features. Under mild assumptions, we establish the algorithmic and statistical rates of convergence for the action-value functions of the iterative policy sequence obtained by DQN. In particular, the statistical error characterizes the bias and variance that arise from approximating the action-value function using a deep neural network, while the algorithmic error converges to zero at a geometric rate. As a byproduct, our analysis provides justifications for the techniques of experience replay and target network, which are crucial to the empirical success of DQN. Furthermore, as a simple extension of DQN, we propose the Minimax-DQN algorithm for zero-sum Markov game with two players. Borrowing the analysis of DQN, we also quantify the difference between the policies obtained by Minimax-DQN and the Nash equilibrium of the Markov game in terms of both the algorithmic and statistical rates of convergence.

 

Bio: Jianqing Fan is a statistician, financial econometrician, and data scientist. He is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at the Princeton University where he chaired the department from 2012 to 2015. He is the winner of The 2000 COPSS Presidents' Award, Morningside Gold Medal for Applied Mathematics (2007), Guggenheim Fellow (2009), Pao-Lu Hsu Prize (2013) and Guy Medal in Silver (2014). He got elected to Academician from Academia Sinica in 2012.

]]> sbryantturner3 1 1630353006 2021-08-30 19:50:06 1634567217 2021-10-18 14:26:57 0 0 event Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep Q-network (DQN) algorithm from both algorithmic and statistical perspectives. Specifically, we focus on a  slight simplification of DQN that fully captures its key features. Under mild assumptions, we establish the algorithmic and statistical rates of convergence for the action-value functions of the iterative policy sequence  obtained by DQN. In particular, the statistical error characterizes the bias and variance that arise from approximating the action-value function using deep neural network, while the algorithmic error converges to zero at a geometric rate. As a byproduct, our analysis provides justifications for the techniques of experience replay and target network, which are crucial to the empirical success of DQN. Furthermore, as a simple extension of  DQN, we   propose the Minimax-DQN algorithm for zero-sum Markov game with two players.  Borrowing the analysis of DQN, we also quantify the difference between  the   policies   obtained by Minimax-DQN  and  the