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About the Talk:<\/strong><\/p>\n Large models fuel impressive capabilities. \u00a0However, they currently do not know what they do not know, which is critical to the important challenges of exploration and alignment. \u00a0Knowing what is not known is essential, for example, to gathering informative data. \u00a0In this talk, I will discuss the need for scalable uncertainty and the design of architectures and training algorithms for epistemic neural networks, which serve that need.<\/p>\n About the Speaker: <\/strong><\/p>\n Benjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His research interests center on the design and analysis of reinforcement learning agents. Beyond academia, he founded and leads the Efficient Agent Team at Google DeepMind, and has also led research programs at Morgan Stanley, Unica (acquired by IBM), and Enuvis (acquired by SiRF), which he co-founded. He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tsitsiklis.<\/p>\n He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, the INFORMS Journal on Optimization, and Foundations and Trends in Machine Learning. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master’s Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department’s Graduate Teaching Award, and the Frederick W. Lanchester Prize.<\/p>\n He has graduated dozens of doctoral students, who have gone on to careers in academia (Carnegie Mellon, Columbia, Cornell, MIT, Northwestern, Rice, Stanford, USC), technology (Adobe, Amazon, DeepMind, Meta, Microsoft, Netflix, OpenAI, Spotify), and finance (Citadel, DE Shaw, Goldman Sachs, Jane Street, Morgan Stanley, Two Sigma).<\/p>\n