Bin Hu

Bin Hu  Assistant Professor
Department of Electrical and Computer Engineering
Coordinated Science Laboratory
University of Illinois at Urbana-Champaign
145 CSL
1308 W Main St
Urbana, IL 61801

About Me

I am an assistant professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign and affiliated with the Coordinated Science Laboratory. My research focuses on building fundamental connections between control and machine learning. Currently I am most interested in

  • System/control tools for understanding generalization in overparameterized deep learning models

  • Connections between robust control and deep reinforcement learning

  • Connections between control and stochastic optimization

I received the B.Sc. in Theoretical and Applied Mechanics from the University of Science and Technology of China in 2008, and received the M.S. in Computational Mechanics from Carnegie Mellon University in 2010. I received the Ph.D. in Aerospace Engineering and Mechanics at the University of Minnesota in 2016, under the supervision of Peter Seiler. Between July 2016 and July 2018, I was a postdoctoral researcher in the Wisconsin Institute for Discovery at the University of Wisconsin-Madison. At Madison, I was working with Laurent Lessard and closely collaborating with Stephen Wright.


  • 03/2021: Delighted to receive the 2020 Amazon Research Award. Big thanks to Amazon!

  • 02/2021: Thrilled to receive the NSF CAREER award on "Interplay between Control Theory and Machine Learning." Big thanks to NSF!

  • 09/2020: Our paper "On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems" has been accepted to NeurIPS 2020.

  • 03/2020: Our paper "Analysis of Biased Stochastic Gradient Descent Using Sequential Semidefinite Programs" has been accepted to Mathematical Programming. A full-text view-only version of the final paper can be found here.

  • 10/2019: Our paper "Policy Optimization for H2 Linear Control with H-infinity Robustness Guarantee: Implicit Regularization and Global Convergence" has been posted on arxiv. This paper studies the implicit regularization mechanism in policy-based reinforcement learning for robust control design.

    Update: A conference version of the above paper has been accepted to L4DC 2020. (one of 14/131 papers selected for oral presentation)

  • 06/2019: Our paper "Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory" has been posted on arxiv. This is my first paper on analyzing reinforcement learning algorithms using control theory!

    Update: The above paper has been accepted to NeurIPS 2019. The arxiv version of the paper has been revised.

  • 08/2018: I started as an Assistant Professor in the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign.