Integrating Online and Offline Learning to Improve Decision Making – Prof. David Simchi-Levi
Join us for the Data Sciences Speaker Series with Prof. David Simchi-Levi, MIT Professor of Engineering Systems; Director, MIT Data Science Lab; Core Faculty, Institute for Data, Systems, and Society (IDSS); Principal Investigator, Laboratory for Information, and Decision Systems (LIDS) and Member National Academy of Engineering. This talk is co-sponsored by the Data Sciences Institute and the Rotman School of Management, University of Toronto.
Registration Required – REGISTER HERE.
- Date: September 18, 2023
- Time: 11:00 a.m. – 12:00 p.m.
- Format: In-person
- Location: Data Sciences Institute, 10th floor, 700 University Avenue, Toronto
Talk Title: Integrating Online and Offline Learning to Improve Decision Making
Machine learning is playing increasingly important roles in decision making, with key applications ranging from dynamic pricing and recommendation systems to personalized medicine and clinical trials. While supervised machine learning traditionally excels at making predictions based on i.i.d. offline data, many modern decision-making tasks require making sequential decisions based on data collected online. Such discrepancy gives rise to important challenges of bridging offline supervised learning and online interactive learning to unlock the full potential of data – driven decision making.
The presentation will focus on the integration of online and offline learning to improve decision making. We highlight three examples. In the first, we consider the challenges of reducing difficult online decision-making problems to well-understood offline supervised learning problems. In the second, we show the impact of offline data on online decision making. Finally, in clinical trials, we show how to convert offline randomized control trials into adaptive, online, experimental design.
David Simchi-Levi is a Professor of Engineering Systems at MIT and 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.