Abstract: Time series learning has become a central problem in modern machine learning, with applications spanning science, engineering, healthcare, and finance. While much of the existing research has focused on improving forecasting accuracy, accurate prediction is only the starting point. This talk explores a broader view of time series learning, moving from forecasting toward reasoning and scientific discovery. I will first discuss our work on efficient time series forecasting, introducing cross-architecture knowledge distillation to develop lightweight models that achieve strong predictive performance with reduced computational costs. I will then explore spatio-temporal reasoning, presenting methods that integrate time series, spatial structures, and large language models to understand how events evolve and propagate across connected systems. Finally, I will discuss whether AI can move beyond prediction to discover the underlying mathematical and causal relationships governing temporal dynamics. These directions highlight the potential of time series learning to advance from accurate prediction toward deeper understanding and scientific discovery.

Bio: Wei Jin is an Assistant Professor in the Department of Computer Science and the Department of Biostatistics and Bioinformatics at Emory University. His research focuses on time series analysis and AI for health. His honors and achievements include two NIH R01 awards as principal investigator, IJCAI-ECAI Early Career Spotlights, AAAI New Faculty Highlights, KAUST Rising Stars, the INNS Doctoral Dissertation Runner-up Award, recognition for Most Influential Papers in KDD and WWW by Paper Digest, and top finishes in three NeurIPS competitions. His research has been published in Nature Portfolio journals, PNAS, and leading conferences including ICLR, KDD, ICML, and NeurIPS. He has also organized multiple tutorials and workshops at major AI conferences.

Abstract: We prove an Q(L/T) lower bound for first-order optimization of convex functions smooth relative to negative entropy on the simplex, showing mirror descent is nearly optimal and acceleration is generally impossible.
Unlike prior results, our prox-function is standard rather than pathological.
We extend the same non-acceleration result to quantum optimization over spectrahedra.

Abstract: Abstract: Decision-making under uncertainties has been a subject of study for many years. In particular, the decision making (optimization) for complex systems such as engineering and economic systems involves human-decision phase. This triggers the occurrences of uncertainties in the decision making phase in addition to the widely-existed inherent uncertainties in the actual systems. In this talk, such challenges will be addressed by summarizing the year’s efforts of the presenter when he was with the University of Manchester before 2016. At first, a generic framework on the decision making for uncertainty systems has been formulated into a “closed loop control design problem”, where the uncertainties in both decision making phase and the actual systems have been represented by the uncertainty injections to the “controller” and to the “plant to be controlled”. The uncertainties in human decision making phase is characterized by their probability density functions (PDF) of the brain-cell’s signal collected via brain-computer-interface (BCI). Using such a generic framework, decision making under uncertainties are formulated as a PDF control problem of objective functions and constraints- leading to a total solution for the stochastic optimization for complex systems. Two case studies will be presented together with the rationale and claims that most of the existing stochastic optimizations are special cases of the presented theory. Note that the presentation is based upon the published materials by the author.
 

Bio: Professor Hong Wang (Member of European Academy of Sciences and Arts, FIEEE, FIET, FInstMC, SMIEEE) received the BSc form Anhui University of Science and Technology in 1982, the master’s and Ph.D. degrees from the Huazhong University of Science and Technology, Wuhan, China, in 1984 and 1987, respectively. He was a Research Fellow with Salford University, Salford, U.K., Brunel University, Uxbridge, U.K., and Southampton University, Southampton, U.K., before joining the University of Manchester Institute of Science and Technology (UMIST), Manchester, U.K., in 1992. He was a Chair Professor in process control of complex industrial systems with the University of Manchester (UoM), U.K., from 2002 to 2016, where he originated probability density function control theory in 1996 and was the Deputy Head of the Paper Science Department, the Director of the UMIST Control Systems Centre from 2004 to 2007, which is the birthplace of Modern Control Theory established in 1966. He was a University Senate member and a member of general assembly during his time in Manchester. Whilst he has been an Emeritus Professor of UoM, from 2016 to 2018, he was with the Pacific Northwest National Laboratory (PNNL), Richland, WA, USA, as a Laboratory Fellow and Chief Scientist,
and was the Co-Leader and the Chief Scientist for the Control of Complex Systems Initiative. He joined the Oak Ridge National Laboratory in January 2019.

His research focuses on stochastic distribution control, fault diagnosis and tolerant control, and intelligent controls with applications to complex system area. He has published 6 books and 200+ journal papers in these areas together with 10+ awards/prizes at international conferences.

He is an associate editor for IEEE Transactions on Neural Networks and Learning Systems and was an Associate Editor of IEEE Transactions on Automatic Control (2002 – 2004), IEEE Transactions on Control Systems Technology (2013 – 2018), and the IEEE Transactions on Automation Science and Engineering. He is also a member for three IFAC Committees for the three areas of his research.

Abstract: We study a class of nonconvex–nonconcave minimax problems in which the inner maximization problem satisfies a local Kurdyka–Łojasiewicz (KL) condition that may vary with the outer minimization variable. In contrast to the global KL or PL conditions commonly assumed in the literature—which are significantly stronger and often too restrictive in practice—this local KL condition accommodates a broader range of practical scenarios. However, it also introduces new analytical challenges. In particular, as an optimization algorithm approaches a stationary point, the region over which the KL condition holds may shrink, leading to a more intricate and potentially ill-conditioned landscape. To address this challenge, we show that the associated maximal function is locally generalized Hölder smooth. Leveraging this key property, we develop an inexact proximal gradient method for solving the minimax problem, where the inexact gradient of the maximal function is computed by applying a proximal gradient or sequential convex programming method to a KL-structured subproblem. Under mild assumptions, we establish complexity guarantees for computing an approximate stationary point of the minimax problem.

This is joint work with Xiangyuan Wang (University of Minnesota).
 

Bio: Zhaosong Lu is a Full Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. He received his Ph.D. in Operations Research from Georgia Tech. His research focuses on the theory and algorithms of continuous optimization, with applications in data science and machine learning. Dr. Lu has published extensively in leading journals, and his research has been supported by major funding agencies, including AFOSR, NSF, and ONR. He has served on several prize committees, such as the INFORMS George Nicholson Prize Committee and the ICCOPT Best Paper Award Committee. He has also served as an Associate Editor for leading journals, including Mathematics of Operations Research, SIAM Journal on Optimization, Computational Optimization and Applications, and Journal of Global Optimization.

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.

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

• Increase emphasis negotiation conditioning and philosophy setting before and throughout the entire sourcing engagement process
• Enhance your toolbox of industry standard negotiation prep tools like the SWOT and BATNA
• Better prepare for negotiations by leveraging knowledge of key negotiation terms and counter-offer tactics
• Improve negotiation table techniques and soft skills to direct and redirect negotiation momentum
• Heighten ability to successfully utilize your traditional "comfort zone" approach in combination with your negotiation team’s strengths by leveraging Personal Negotiation Styles

What Is Covered

• Negotiation Conditioning Overview
• Negotiation Preparation Tools
• Negotiation Execution Techniques
• Stakeholder Engagement & Team Leadership
• Live Negotiations Simulation & Feedback

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

• Increase knowledge base in law of agency concepts to gain more confidence with contracting.
• Enhance understanding of industry standard contract types and contract terms for more robust contract agreements.
• Better leverage sourcing category knowledge to modify existing contract elements for more holistic contract agreements.
• Improve internal contract execution communication for better results.
• Heighten sense of executive financial impact and risk needs to gain leadership early support.

What Is Covered

• Law of Agency Overview
• Contract Agreement Types
• Defining Key Terms
• Contract Structure & Drafting
• Risk Mitigation & Communication

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

• Increase knowledge base of solicitation options (i.e. RFI, RFP, RFQ) and which solicitation approach to apply based on your organization's needs
• Enhance understanding of sourcing process and critical steps in the Bid Package development and delivery activities
• Better leverage and utilization of solicitation tools to drive successful development of bid packages
• Improve set up and execution of supplier selection scorecards to aid in identifying best Total Cost of Ownership alternatives
• Heighten understanding of executive communication to leverage leadership support throughout the organization

What Is Covered

• Bid Package Development Overview
• Sourcing Initiative Process Roadmap
• Marketplace Analysis Tools
• Bid Package Alternatives & Design
• Supplier Selection & Communication

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.

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)