Reinventing Operations Management’s Research and Practice with Data Science (AI, Statistics & Data Science in Practice Series)

Overview

About the Speaker

David Simchi-Levi is the William Barton Rogers Professor in Energy, and Professor of Civil and Environmental Engineering and Engineering Systems at MIT. He also serves as the head of the MIT Data Science Lab. He is considered one of the premier thought leaders in supply chain management and business analytics. His Ph.D. students have accepted faculty positions in leading academic institutes including U. of California Berkeley, Carnegie Mellon U., Columbia U., Cornell U., Duke U., Georgia Tech, Harvard U., U. of Illinois Urbana-Champaign, U. of Michigan, Purdue U. and Virginia Tech. Professor Simchi-Levi is the current Editor-in-Chief of Management Science, one of the two flagship journals of INFORMS. He served as the Editor-in-Chief for Operations Research (2006-2012), the other flagship journal of INFORMS and for Naval Research Logistics (2003-2005). In 2023, he was elected a member of the National Academy of Engineering. In 2020, he was awarded the prestigious INFORMS Impact Prize for playing a leading role in developing and disseminating a new highly impactful paradigm for the identification and mitigation of risks in global supply chains. He is an INFORMS Fellow and MSOM Distinguished Fellow and the recipient of the 2020 INFORMS Koopman Award given to an outstanding publication in military operations research; Ford Motor Company 2015 Engineering Excellence Award; 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; and 2009 INFORMS Revenue Management and Pricing Section Prize. He was the founder of LogicTools which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016. In 2014, he co-founded Opalytics, a cloud analytics platform company focusing on operations and supply chain decisions. The company became part of the Accenture Applied Intelligence in 2018. See Profile

About the Moderator

Dr. Will Wei Sun is an Associate Professor of Management at Purdue University's Mitchell E. Daniels, Jr. School of Business, with a courtesy appointment in the Department of Statistics. He serves as the PhD Coordinator for Quantitative Methods and is recognized for his expertise in statistical foundations of large language models, trustworthy reinforcement learning, online decision-making in two-sided markets, and neuroimaging analysis. Dr. Sun's research has been supported by notable grants, including a \$450K award from the National Science Foundation for work on trustworthy reinforcement learning, and a \$150K grant from the Office of Naval Research focusing on statistical methods for large tensor data. Dr. Sun earned his Ph.D. in Statistics from Purdue University in 2015, following M.S. degrees in Statistics and Computer Science from Purdue and in Statistics from the University of Illinois at Chicago. He began his academic career at the University of Miami before returning to Purdue. His teaching excellence has been acknowledged with multiple awards, such as the Salgo-Noren Outstanding Master's Teaching Award (First runner-up) in 2023 and 2024, and the Distinguished Teacher award from the Krannert School of Management in 2019, 2020, 2021, and 2023. Dr. Sun is also a member of the editorial board for the Journal of Data and Dynamic Systems. See Profile

About AI, StAtIstics and Data Science in Practice

The NISS AI, Statistics and Data Science in Practice is a monthly event series will bring together leading experts from industry and academia to discuss the latest advances and practical applications in AI, data science, and statistics. Each session will feature a keynote presentation on cutting-edge topics, where attendees can engage with speakers on the challenges and opportunities in applying these technologies in real-world scenarios. This series is intended for professionals, researchers, and students interested in the intersection of AI, data science, and statistics, offering insights into how these fields are shaping various industries. The series is designed to provide participants with exposure to and understanding of how modern data analytic methods are being applied in real-world scenarios across various industries, offering both theoretical insights, practical examples, and discussion of issues.