Guido Imbens is the Applied Econometrics Professor and Professor of Economics at the Stanford Graduate School of Business. After graduating from Brown University Guido taught at Harvard University, UCLA, and UC Berkeley. He joined the GSB in 2012. Imbens specializes in econometrics, and in particular methods for drawing causal inferences. Guido Imbens is a fellow of the Econometric Society and the American Academy of Arts and Sciences. In 2021, he was co-awarded The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, for "their methodological contributions to the analysis of causal relationships".

Lihong Li is a Senior Principal Scientist at Amazon. He obtained a PhD degree in Computer Science from Rutgers University. After that, he has held research positions in Yahoo!, Microsoft and Google. His main research interests are in reinforcement learning, including contextual bandits, and other related problems in AI. His work has found applications in recommendation, advertising, Web search and conversation systems, and has won best paper awards at ICML, AISTATS and WSDM. He regularly serves as area chair or senior program committee member at major AI/ML conferences such as AAAI, AISTATS, ICLR, ICML, IJCAI and NeurIPS.

Daniel Russo is Associate Professor at the Decision, Risk, and Operations division of Columbia Business School. His research lies at the intersection of statistical machine learning and online decision making, mostly falling under the broad umbrella of reinforcement learning. Outside academia, he works with Spotify to apply reinforcement learning style models to audio recommendations. Prior to joining Columbia, he was Assistant Professor in the MEDS department at Northwestern's Kellogg School of Management and a Postdoctoral Researcher at Microsoft Research in New England. He received his PhD from Stanford University in 2015, where he was advised by Benjamin Van Roy and his BS in Mathematics and Economics from the University of Michigan. He currently serves as an associate editor at Management Science and Stochastic Systems.

Ehtsham Elahi is an Applied Machine Learning Scientist at Netflix. He holds a Masters degree in Electrical Engineering from University of Michigan - Ann Arbor. His primary interest area is Reinforcement Learning in Recommender Systems. Over many years at Netflix, he has improved Netflix’s member experience through innovations in personalized video ranking and homepage construction models. His research papers on collaborative filtering, representation learning and simulations for recommender systems have been published in RecSys in recent years.

Yuyan Wang is currently a senior research engineer at Google Brain, working on machine learning research to understand long-term user journeys on personalized platforms, and optimize the long-term values for Google recommendation products. Prior to Google, she has worked as an applied scientist at Uber, building the first generations of recommender systems for Uber Eats. She received her PhD in Statistics from Princeton University, and her BSc in Statistics from Special Class for the Gifted Young at University of Science and Technology of China.