Xun Huan

Assistant Professor, University of Michigan

Xun is speaking at

Contributed Session
June 28, 2021
6:45 pm - 8:00 pm


  • Tahir Ekin (Speaker) Associate Professor, Texas State University
  • Lin Qiu (Speaker) Statistical Scientist, Genentech
  • Mingzhang Yin (Speaker) Postdoc, Columbia University
  • Yuexi Wang (Speaker) PhD Student, University of Chicago
  • Duke Chowdhury (Speaker) Johns Hopkins
  • Kush Bhatia (Speaker) PhD Student, UC Berkeley
  • Raaz Dwivedi (Speaker) Graduate Student, UC Berkeley
  • Luca Maestrini (Speaker) Postdoctoral Research Fellow, University of Technology Sydney
  • Woojung Kim (Speaker) PhD Student, University of Warwick
  • Wenzhe Xu (Speaker) University of Exeter
  • Xun Huan (Speaker) Assistant Professor, University of Michigan
  • Mingyuan Zhou (Chair) Associate Professor, University of Texas at Austin



June 28, 2021
7:25 pm - 7:30 pm


  • Xun Huan (Speaker) Assistant Professor, University of Michigan


We focus on designing a finite sequence of experiments, seeking fully optimal design policies (strategies) that can (a) adapt to newly collected data during the sequence (i.e. feedback) and (b) anticipate future changes (i.e. lookahead). We approach this sequential decision-making problem in a Bayesian setting with information-based utilities, and solve it numerically via policy gradient methods from reinforcement learning. In particular, we directly parameterize the policy and value functions by neural networks—thus adopting an actor-critic approach—and improve them using gradient estimates produced from simulated design and observation sequences. The overall method is demonstrated on an algebraic benchmark and a sensor movement application for source inversion. The results provide intuitive insights on the benefits of feedback and lookahead, and indicate computational advantages compared to previous approaches based on approximate dynamical programming.

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