Assaf Zeevi is Professor and holder of the Kravis chair at the Graduate School of Business and the Data Science Institute (DSI), Columbia University. His research and teaching interests lie at the intersection of Data Science, Operations and Machine Learning, and AI technologies more broadly. In particular, he has been developing theory and algorithms for reinforcement learning, Bandit problems, stochastic optimization, statistical learning and stochastic networks, and successfully implementing these in a variety of application domains such as online retail, healthcare analytics, dynamic pricing, recommender systems, and online marketplaces. Assaf is a founding member of the Columbia Center for Artificial Intelligence Technologies (CAIT), and serves on several scientific advisory boards for startup companies in the high technology sector. He received his B.Sc. and M.Sc. (Cum Laude) from the Technion – Israel Institute of Technology, and subsequently his Ph.D. from Stanford University in 2001. Assaf has held several visiting positions, including Stanford University, the Technion, and Tel Aviv University. He was awarded the INFORMS 2019 Lanchester Prize, and is past recipient of the NSF CAREER Award, IBM Faculty Award, Google Faculty Award, and several best paper awards.