My research is about the foundations of data-driven sequential decision making and its practical applications in bio-informatics, life and health sciences, computer gaming, robotics, and computer networks. A defining feature of sequential decision making is learning and data collection are intertwined: data collected in the past directly influences the data yet to be collected in the future. My research aims to understand the role of data randomization in designing sequential learning algorithms that are both statistically efficient and computationally scalable. Reinforcement learning (RL) and privacy-preserving sequential learning constitute my primary research interests. In addition to foundational machine learning, I am also interested in applying sequential decision making algorithms to solve challenging wireless networking problems.

I defended my thesis in Sep 2021 advised by Nishant Mehta at the University of Victoria. Before coming to Canada for my PhD study in 2016, I was a 3GPP delegate of SONY.  I have around 27 patents granted about how to use unlicensed spectrum from cellular operator's perspective and Device-to-Device communications. 


My full patent list can be found in Bingshan's Patents. 

Google Scholar page: Bingshan's Google Scholar

Email:  bingshanhu3@gmail.com


News

Education

Sep 2016 - Sep 2021: University of Victoria (UVic), Victoria, Canada

Teaching

Publications and preprints