Abstract: Two-sided matching platforms, including labor markets, dating apps, accommodation services, and ridesharing systems, must make matching decisions in the presence of choice congestion and strategic platform design challenges. When agents have correlated preferences, popular options can attract too much demand, reduce overall efficiency, and lead to poor market outcomes. In this talk, I will present a framework for two-sided assortment optimization that studies how a platform should decide which options to display to agents and in what order, with the goal of improving matching performance. The main focus will be on maximizing the expected number of matches under general choice models. I will describe several natural classes of platform policies, ranging from static simultaneous displays to fully adaptive sequential policies, and compare their power through adaptivity gap results. I will also discuss polynomial-time approximation algorithms for computing near-optimal policies, and then briefly discuss the revenue-maximization version of the problem, where matches generate pair-dependent rewards. This talk is based on joint works with Alfredo Torrico, Ulysse Hennebelle, and Mohammadreza Ahmadnejadsaein.
 

Bio: Omar El Housni is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell Tech and Cornell University. He is a Field Member of the Center of Applied Mathematics at Cornell. He is also an Amazon Scholar. His research focuses on decision-making under uncertainty where he aims to develop optimization models and design robust and efficient algorithms to address a wide range of operational problems, including revenue management problems such as assortment optimization and online matchings. Omar has spent time as a research scientist at Amazon and Uber where he contributed to the design and implementation of data-driven optimization models for matching and retailing platforms. Omar holds a PhD in Operations Research from Columbia University and an MS and BS in Applied Mathematics from Ecole Polytechnique (Paris). His work has been recognized by INFORMS George Nicholson award and his current research is supported by NSF. 

Abstract: Modern manufacturing generates increasingly rich in-situ sensing data, creating new opportunities for AI to enable automated and intelligent quality control decisions. However, conventional quality control using supervised learning relies on abundant labeled data and assumes that future product defects or process faults resemble those known during training. In practice, new defect types emerge, while abnormal conditions may be rarely observed or completely unknown. These challenges are particularly important for in-situ quality control, where defects must be detected or correctly classified for real-time decision-making, including newly emerging defects with limited or unavailable labels. Moreover, for latent defects that cannot be directly inspected online, defects must instead be predicted from indirect process-sensing signals. This requires mapping process-signal changes to possible defects despite scarce or unavailable defect training samples. This talk explores how advances in AI can address these challenges and enable more adaptive and intelligent in-situ quality control and decision-making for smart manufacturing.
 

Bio: Dr. Judy Jin is the A. Galip Ulsoy Collegiate Professor of Engineering and Professor of Industrial and Operations Engineering at the University of Michigan. Her research lies at the intersection of data science and quality engineering, with a focus on synergistically integrating engineering models, AI, and advanced quality control methods to improve system design and operational performance. She has served as PI/Co-PI on more than $20 million in federally and industry-funded research. Her work has received numerous honors, including 18 Best Paper Awards, the S.M. Wu Research Implementation Award from SME, the Forging Achievement Award from FIERF, the NSF CAREER Award, and the NSF PECASE Award.

Dr. Jin currently serves as Editor-in-Chief of IISE Transactions. She has also served as Vice President of INFORMS, Chair of the INFORMS Quality, Statistics and Reliability Section, and President of the IISE Quality Control and Reliability Engineering Division. She is a Fellow of ASME, IISE, and INFORMS.

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

• Time: 10:00AM - 3:00PM
• Dates: 
  • 10/6: Staff
  • 10/7: Faculty
  • 10/8: Ph.D. Students

Recommendations for attire: 

• Wear blue, or dark-colored clothing (will be on a white backdrop)
• Do NOT wear red
• Avoid wearing large jewelry and patterns

Abstract: This talk describes the progress in analysis and optimization of Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs) with infinite state spaces and possibly noncompact action sets. We shall also discuss applications to inventory control and to controlled linear Gaussian systems. 
 

Bio: Eugene A. Feinberg received MS in Applied Mathematics and Computer Engineering from Moscow University of Transportation, Russia, in 1976 and Ph.D. in Probability and Statistics from Vilnius University, Lithuania, in 1979. Currently he is Distinguished Professor at the Department of Applied Mathematics and Statistics of Stony Brook University.

His research interests include stochastic models of operations research, probability theory, real analysis, Markov Decision Processes, and applications of operations research and statistics to engineering, biology, and medicine. He has published more than 100 papers and edited the Handbook on Markov Decision Processes. His research has been partially supported by the National Science Foundation, Office of Naval Research, National Institute of Health, New York Office of Science, Technology and Academic Research, and private industry. He has served as a Council Member of the INFORMS Applied Probability Society and on several editorial boards. He is a fellow of INFORMS.

Abstract: Recent advances in neuroimaging have produced massive and heterogeneous datasets, ranging from fMRI with high spatial resolution to EEG with high temporal resolution, characterized by complex spatiotemporal correlations and substantial inter-subject variability. Traditional regression models and Gaussian process (GP) approaches with fixed parametric kernels often fail to model such complex data effectively while maintaining scalability and interpretability. This talk introduces a family of modern Bayesian GP frameworks that integrate deep kernel learning, neural network priors, and geometric modeling for large-scale neuroimaging analysis. An example is the Deep Kernel Learning Process (DKLP), which embeds deep neural networks within GP priors to learn data-adaptive covariance structures directly from imaging data. DKLP provides a unified modeling foundation for image-on-scalar, scalar-on-image, and image-on-image regression, supported by theoretical guarantees and efficient posterior computation. Applications to fMRI data from the Adolescent Brain Cognitive Development (ABCD) study reveal reproducible cortical activation patterns associated with cognitive ability, while analyses of EEG-based brain–computer interface data demonstrate robust neural decoding under high noise. I will also discuss scalable heat-kernel GPs on manifolds and thresholded GP–based spatially varying neural network priors, which together expand the scope of Bayesian inference for complex neuroimaging data.
 

Bio: Dr. Jian Kang is Professor and Associate Chair for Research in the Department of Biostatistics at the University of Michigan. His research lies at the intersection of Bayesian statistics, machine learning, and artificial intelligence, with applications in neuroimaging, brain–computer interfaces, omics, and precision medicine. He has published more than 175 papers in leading statistics, machine learning, and biomedical journals. Dr. Kang has served as an Associate Editor for several premier statistical journals, including the Journal of the American Statistical Association (JASA), The Annals of Applied Statistics (AOAS) and Biometrics. He is a Fellow of both the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). He currently serves as Chair of the ASA Section on Statistics in Imaging.

The H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology is pleased to welcome you to Atlanta, Georgia for Georgia Statistics Day 2026.

Georgia Statistics Day is an annual event that promotes interdisciplinary statistics research across the University of Georgia, the Georgia Institute of Technology, and Emory University, with the venue rotating among the participating institutions. The 2026 event will feature a keynote lecture, two semi-plenary lectures, parallel research sessions, a student poster session, and ample opportunity for exchange between academia and industry.

We are honored to have Prof. Jianqing Fan, Frederick L. Moore ’18 Professor of Finance and Professor of Statistics, Machine Learning, and Operations Research and Financial Engineering at Princeton University, as our keynote speaker. The semi-plenary speakers are Prof. Sivaraman Balakrishnan from Carnegie Mellon University and Prof. Mladen Kolar from the University of Southern California.

This one-day workshop brings together faculty, students, and industry researchers from across Georgia and the Southeast for invited talks, interdisciplinary exchange, mentoring, and networking. Registration and poster submissions are open through September 28, 2026.
 

Georgia Statistics Day 2026 website

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

• Understand the most relevant planning challenges across the strategic, tactical, and operational levels of supply chains
• Learn the difference between analytics types, the links between them, and how to best use them to improve supply chain management (SCM) processes
• Use Key Performance Indicators (KPIs) to find causes of underperformance in supply chains and to plan for analytics projects that will address strategic SCM goals
• Utilize Python and PowerBI to understand, visualize, and analyze data in order to prepare for deeper analytics

What Is Covered

• The role of analytics in SCM
• Types of analytics (descriptive, diagnostic, predictive, and prescriptive) and the relationships between them
• Preprocessing (cleaning and integrating) data as it relates to SCM
• Conducting exploratory data analysis on supply chain data
• Best practices for visualizing data and building dashboards
• Identifying and analyzing KPIs of SCM
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

This course provides a deep dive into the ways in which artificial intelligence (AI) optimizes supply chain efficiency. Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization. The course also covers ethical AI use, good and bad use of generative AI (GenAI), and rapidly emerging use cases. By the end, professionals will be skilled in applying AI to enhance supply chain processes and drive success in their organizations.

Who Should Attend

This course targets supply chain managers, data analysts, logistics professionals, procurement specialists, and business leaders aiming to harness GenAI for enhanced supply chain operations. It is ideal for those interested in GenAI-driven efficiency, strategic insights, and navigation of GenAI's role in transforming supply chain processes.

How You Will Benefit

• Enhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.
• Acquire practical skills in prompt engineering and the use of generative AI models.
• Explore practical use cases that can be reapplied.
• Learn about good and bad use of GenAI for individuals, teams, and organizations.
• Become better equipped to effectively harness GenAI capabilities in supply chain activities and planning.

What Is Covered

• Foundational understanding of using GenAI in supply chain management
• Basics of GenAI
• Crafting effective AI prompts and their applications in optimizing warehouse layouts
• Predictive maintenance and supplier selection
• Elimination of redundant tasks through AI
• Ethical considerations, risk assessments, and strategy for AI adoption
• Practical strategies and real-world examples for implementing AI solutions effectively and making informed decisions

This course provides a deep dive into the ways in which artificial intelligence (AI) optimizes supply chain efficiency. Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization. The course also covers ethical AI use, good and bad use of generative AI (GenAI), and rapidly emerging use cases. By the end, professionals will be skilled in applying AI to enhance supply chain processes and drive success in their organizations.

Who Should Attend

This course targets supply chain managers, data analysts, logistics professionals, procurement specialists, and business leaders aiming to harness GenAI for enhanced supply chain operations. It is ideal for those interested in GenAI-driven efficiency, strategic insights, and navigation of GenAI's role in transforming supply chain processes.

How You Will Benefit

• Enhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.
• Acquire practical skills in prompt engineering and the use of generative AI models.
• Explore practical use cases that can be reapplied.
• Learn about good and bad use of GenAI for individuals, teams, and organizations.
• Become better equipped to effectively harness GenAI capabilities in supply chain activities and planning.

What Is Covered

• Foundational understanding of using GenAI in supply chain management
• Basics of GenAI
• Crafting effective AI prompts and their applications in optimizing warehouse layouts
• Predictive maintenance and supplier selection
• Elimination of redundant tasks through AI
• Ethical considerations, risk assessments, and strategy for AI adoption
• Practical strategies and real-world examples for implementing AI solutions effectively and making informed decisions

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

• Use mathematical optimization to transform Supply Chain Management (SCM) processes.
• Apply LP, MIP, and heuristics to SCM, particularly in production planning, routing, and network design.
• Utilize PowerBI and Python in optimization projects.
• Participate in a hackathon that pulls together everything learned throughout the certificate program.

What Is Covered

• Role of mathematical optimization in addressing complex SCM challenges  
• Appropriate application of linear programming (LP), mixed integer programming (MIP), and heuristics
• Evaluation of production processes, distribution networks, and routes using optimization
• Ability to pull together all content of the certificate program into a prescriptive analytics project
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

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

• Understand why and how to align Supply Chain Management (SCM) strategy with business strategy
• Learn statistics techniques as they relate to SCM
• Understand inventory management models and how to apply statistics techniques to them
• Create time series forecasts based on SCM data
• Utilize Python and PowerBI to perform statistical analyses, create time series forecasts and visualize results

What Is Covered

• The importance of aligning SCM and business strategy
• How to ask the right business questions as they relate to SCM
• How to use statistics to identify issues, compare data, and forecast decision outcomes
• Statistical concepts including variance analysis and hypothesis testing
• Inventory management models
• Applying statistics to inventory management models
• Forecasting techniques including time series forecasting
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

This course provides a deep dive into the ways in which artificial intelligence (AI) optimizes supply chain efficiency. Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization. The course also covers ethical AI use, good and bad use of generative AI (GenAI), and rapidly emerging use cases. By the end, professionals will be skilled in applying AI to enhance supply chain processes and drive success in their organizations.

Who Should Attend

This course targets supply chain managers, data analysts, logistics professionals, procurement specialists, and business leaders aiming to harness GenAI for enhanced supply chain operations. It is ideal for those interested in GenAI-driven efficiency, strategic insights, and navigation of GenAI's role in transforming supply chain processes.

How You Will Benefit

• Enhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.
• Acquire practical skills in prompt engineering and the use of generative AI models.
• Explore practical use cases that can be reapplied.
• Learn about good and bad use of GenAI for individuals, teams, and organizations.
• Become better equipped to effectively harness GenAI capabilities in supply chain activities and planning.

What Is Covered

• Foundational understanding of using GenAI in supply chain management
• Basics of GenAI
• Crafting effective AI prompts and their applications in optimizing warehouse layouts
• Predictive maintenance and supplier selection
• Elimination of redundant tasks through AI
• Ethical considerations, risk assessments, and strategy for AI adoption
• Practical strategies and real-world examples for implementing AI solutions effectively and making informed decisions

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

• Use mathematical optimization to transform Supply Chain Management (SCM) processes.
• Apply LP, MIP, and heuristics to SCM, particularly in production planning, routing, and network design.
• Utilize PowerBI and Python in optimization projects.
• Participate in a hackathon that pulls together everything learned throughout the certificate program.

What Is Covered

• Role of mathematical optimization in addressing complex SCM challenges  
• Appropriate application of linear programming (LP), mixed integer programming (MIP), and heuristics
• Evaluation of production processes, distribution networks, and routes using optimization
• Ability to pull together all content of the certificate program into a prescriptive analytics project
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

This course focuses on defining, executing, and improving the sales and operations planning (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 decision support tools 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

This course is designed for chief operating officers; supply chain, sales, marketing and finance management executives (directors, vice presidents, executive vice presidents); supply chain and logistics managers, consultants, supervisors, planners, and engineers; supply chain education and human resource management personnel, inventory and demand planners, and procurement and sourcing analysts and managers; and manufacturing planners, analysts, and managers.

How You Will Benefit

• Understand the need for an S&OP process in a company.
• Apply the principles that are the key to success of an S&OP process.

What You Will Learn

• S&OP process and technology
• S&OP implementation planning and execution
• S&OP stakeholder and communications planning
• S&OP business case and best practices
• S&OP process management