Simon Du

• Year

  2024

• Program

  AI2050

• Institution

  University of Washington

• Location

  USA

Simon S. Du is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Prior to starting as faculty, he was a postdoc at the Institute for Advanced Study of Princeton, under the mentorship of Sanjeev Arora. He completed his Ph.D. in Machine Learning at Carnegie Mellon University, where he was advised by Aarti Singh and Barnabás Póczos. Simon’s research has been recognized by a Sloan Research Fellowship, a Samsung AI Researcher of the Year Award, an Intel Rising Star Faculty Award, an NSF CAREER award, an Nvidia Pioneer Award, a Distinguished Dissertation Award honorable mention from CMU, among others. His notable contributions include proving the first global convergence result of gradient descent for optimizing deep neural networks, settling the sample complexity in reinforcement learning, and establishing the necessary and sufficient conditions for reinforcement learning in large state spaces. His current research focuses multi-agent reinforcement learning and data selection algorithms for foundation models.