Prof Xiaole Wu（吴肖乐）

Fudan University

Bio: Xiaole Wu is a Professor at the School of Management, Fudan University, and Director of Fudan Center for Global Supply Chains. She received her PhD in Management from Olin Business School, Washington University in St. Louis in 2011, and bachelor's degree in Industrial Engineering from Tsinghua University in 2006. Her research interests include global supply chain management, risk management, and the interfaces between operations and other disciplines. She has published in Management Science, Manufacturing and Service Operations Management, Production and Operations Management, Decision Sciences, Energy Economics, Naval Research Logistics, European Journal of Operational Research, etc. She serves as an Associate Editor at Management Science and Naval Research Logistics, a Senior Editor at Production and Operations Management, and a Department Editor at Journal of Management Sciences in China. She is a principal investigator of the National Science Fund for Distinguished Young Scholars and the Major Program of National Natural Science Foundation of China. She consults for State Grid, Huawei, ZTE, SAIC MAXUS, etc.

Prof Nan Liu（刘楠）

Boston College

Bio: Nan Liu is a Professor of Business Analytics and the William S. McKiernan '78 Family Faculty Fellow at the Carroll School of Management, Boston College. His research interests span operations management, consumer behavior, and health policy. His recent work focuses on developing innovative strategies and leveraging emerging technologies (e.g., telemedicine) to improve access, efficiency, equity, and quality in care delivery. His research appears in leading journals such as Management Science, Manufacturing & Service Operations Management, Operations Research, Health Services Research, and Medical Care Research and Review.

Professor Liu serves as Associate Editor for Manufacturing & Service Operations Management and Operations Research, Senior Editor for Production and Operations Management, and Department Editor for Health Care Management Science.

At Boston College, he teaches undergraduate and MBA courses in operations management. He received the 2023 Coughlin Distinguished Teaching Award.

Before joining Boston College, he was on the faculty of the Department of Health Policy and Management at Columbia University. He holds a BEng in Civil Engineering from Tsinghua University in China, and an MS in Statistics and a PhD in Operations Research from the University of North Carolina at Chapel Hill.

Prof Jun Luo（罗俊）

Shanghai Jiao Tong University

Bio: Jun LUO is a professor of Antai College of Economics and Management at Shanghai Jiao Tong University. He received his PhD degree in Industrial Engineering and Logistics Management at HKUST and a B.S. degree in Statistics at Nanjing University. His research interests include stochastic modeling, simulation optimization, and statistical learning, with their applications in service operations management, supply chain management and financial risk management. His work has been published in journals such as Operations Research, INFORMS Journal on Computing, IISE Transactions, Naval Research Logistics and so on. He currently serves as an Associate Editor for Naval Research Logistics and Journal of Systems Science and Systems Engineering. He is the principle investigator for several research projects, including NSFC for Excellent Young Scientists and Key Program of NSFC, and Alibaba Innovation Research Project.

Presenters:

Prof Chengzhang Li

Shanghai Jiao Tong University

Topic: Data aggregation for prediction and decision making: Theory and Practice

Abstract: In this talk, I will present recent works on data aggregation strategies that enable firms to make effective predictions and decisions under limited data availability. In the first work, I will introduce a collaborative project with Meituan Youxuan, a leading community group buying platform. We develop a flexible data aggregation framework to support operational decision-making in their large-scale inventory system. The proposed framework was validated using publicly available real-world data and through an internal study with Meituan in a major province. In the second work, I will discuss how clustering analysis can be integrated with data aggregation and theoretically investigate the potential benefits of cluster-based aggregation in small-data environments.

Bio: Chengzhang Li is an Associate Professor (untenured) in Management Science at Antai College of Economics and Management (ACEM), Shanghai Jiao Tong University (SJTU). Prior to joining SJTU, he received his M.S. in Statistics and Computer Science and his Ph.D. in Operations Management from Purdue University, and his B.S. in Mechanical Engineering and Automation from SJTU. His research interests lie in supply chain management, data-driven decision-making, and socially responsible operations. His works have been published in Management Science, Manufacturing and Service Operations Management, and Production and Operations Management, and have received awards, including first prize in the MSOM Practice-based Research Competition and an honorable mention in the Best Student Paper Competition at the POM College of Sustainable Operations. He has two NSFC grants as Principal Investigator and was selected for the Shanghai Pujiang Talent Program. His teaching has also been recognized with several awards, including the Most Popular Undergraduate Instructor at SJTU and the Krannert Distinguished Teaching Award at Purdue.

Prof Sheng Liu

University of Toronto

Topic: From Bike Lanes to Last-Mile Delivery and Beyond

Abstract: Data-driven decision-making has been an emerging topic in management science and engineering for some time, and it has significantly influenced my research journey since my undergraduate studies. I will discuss how data and machine learning impact my thinking around transportation and logistics problems that are often motivated by industry collaborations. There are also many challenges involved in integrating artificial intelligence and operational knowledge, which I aspire to explore in the future.

Bio: Sheng Liu is an Assistant Professor of Operations Management and Statistics at the Rotman School of Management, University of Toronto. He joined Rotman after completing his PhD in Operations Research from UC Berkeley in 2019. Sheng's research focuses on solving operations problems in supply chains, transportation, and logistics systems through optimization and data analytics. His industry experience includes consulting or working for organizations such as JD.com, Sport Chek, Ninja Van, Hungerhub, Amazon, and Lyft. His work has been recognized by several awards and paper competitions, including the INFORMS Public Sector Operations Research Best Paper Award, INFORMS TSL Outstanding Paper Award (Freight Transportation and Logistics), and M&SOM Data-Driven Research Competition. He currently serves as an associate editor of Transportation Science and an Editorial Review Board member of Service Science.

Prof Xiaojie Mao

Tsinghua University

Topic: A Personal Journey in Data Science and Management Science: Research, Education, and Future Pathways

Abstract: In this talk, I will share my personal journey in data science and management science. I will summarize my previous research and share my experience in student teaching and mentorship. I will also discuss some of my ongoing research and future aspirations.

Bio: Xiaojie Mao is an associate professor in Management Science and Engineering at Tsinghua University. He did his undergraduate in Mathematical Economics at Wuhan University and Ph.D. in Statistics and Data Science at Cornell University. His research interest is in causal inference, data-driven decision-making, and statistical machine learning. His research has appeared in top journals and conferences across multiple disciplines, such as Operations Research, Management Science, Journal of Machine Learning Research, Journal of the Royal Statistical Society Series B, NeurIPS, ICML, AISTATS, COLT, etc.

Prof Zhongbin Wang

Tianjin University

Topic: Innovating Service Operations: Cross-Disciplinary Research and Teaching

Abstract: This presentation introduces my research interests and highlights core contributions to the field of service operations. Core methodological approaches are presented, particularly the integration of stochastic modeling and game theory to address fundamental challenges in service systems. The presentation also outlines a future vision centered on cross-disciplinary research, aiming to explore theoretical advancements and address key challenges across multiple domains. Finally, emphasis is placed on an inquiry-driven, student-centered teaching philosophy, with a focus on creating transformative and inspirational impact.

Bio: Dr. Zhongbin Wang is a Professor and PhD advisor at the College of Management and Economics, Tianjin University, and a recipient of the National Science Fund for Outstanding Young Scholars. He has published some high-quality papers, including several in top international journals in operations management such as Management Science, Operations Research, Manufacturing & Service Operations Management, and Production and Operations Management. His main research interests include operations management, consumer-driven service operations, queueing economics, and supply chain management in platform economies. Dr. Wang and his collaborators have published a book on queueing economics with Springer, titled Innovative Priority Mechanisms in Service Operations: Theory and Applications. His doctoral dissertation received the Excellent Doctoral Dissertation Award from the Systems Engineering Society of China, as well as the 2021 Excellent Doctoral Dissertation Award from the Management Science and Engineering Society. His research has also been recognized with the First Prize for Best Conference Paper at the 2022 National Conference on Supply Chain and Operations Management, and the First Prize for Best Service Science Paper at INFORMS in 2021.

Prof Minglong Zhou

Fudan University

Topic: Towards AI-driven Robust Optimization for Resilient Supply Chain Management

Abstract: In the big data era, the wide availability of contextual information has rendered contextual stochastic optimization an essential tool for decision-making under uncertainty. The big data also brings unprecedented volatility in estimating the conditional distribution of the uncertainty, especially in complex systems, revealing the need for incorporating the idea of distributionally robust optimization into contextual optimization. Many pioneer works have explored along this direction. In this presentation, I will briefly discuss my research exploration in bridging AI and distributionally robust optimization. By synergizing the predictive power of AI with the prescriptive capabilities of data-driven robust optimization, I hope to contribute towards smarter, more resilient supply chain management. Partnering with major companies, I test how an estimate-then-robust-optimize framework can improve the supply chain efficiency. The potential benefits are substantial, including a significant reduction in operational costs and risks. From a methodological perspective, I delve into two modeling paradigms. The first integrates the prediction model into data-driven robust optimization. The second integrates policy optimization with data-driven robust optimization. These methods open up new opportunities beyond estimate-then-robust-optimize. In this talk, I wish also to share my inspirations in teaching, professional endeavors, and future research and career aspirations.

Bio: Minglong Zhou is an Associate Professor at the Department of Management Science at the School of Management, Fudan University. Minglong Zhou Received his PhD degree from the Business School at the National University of Singapore. Prior to his appointment at Fudan University, he worked as a research fellow at the Institute of Operations Research and Analytics at the National University of Singapore.  Minglong’s research mainly lies in the area of data-driven robust optimization and its applications in healthcare, logistics, and supply chain management. His research has been published in Operations Research, Manufacturing & Service Operations Management, and Production and Operations Management. He actively engages in industrial collaborations, addressing real-world management science problems partnering with MAXUS, ZTE, and several major hospitals in both China and Singapore.