Huazheng Wang

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Assistant Professor,
School of Electrical Engineering and Computer Science,
Oregon State University
Email: huazheng.wang [at] oregonstate.edu

About me

I am an Assistant Professor in the School of Electrical Engineering and Computer Science (EECS) at Oregon State University. I was a Postdoctoral Research Associate at the Department of Electrical and Computer Engineering at Princeton University from 2021 to 2022, hosted by Dr. Mengdi Wang. I received my Ph.D. in Computer Science at University of Virginia in 2021, supervised by Dr. Hongning Wang. I received my B.Eng. in Computer Science at University of Science and Technology of China in 2015. My research interests include reinforcement learning, information retrieval and machine learning in general. I recently focused on developing provably efficient and trustworthy reinforcement learning and multi-armed bandit algorithms with applications to information retrieval tasks such as recommendation, ranking, LLM agents, and scientific discovery problems in biology and chemistry.

I am looking for one self-motivated PhD students with solid math and coding backgrounds starting Fall 2026. If you are interested, please apply to the CS or AI program and mention my name in the application. If you are an undergraduate or graduate student at OSU and want to join my lab, please directly send me an email with your CV and transcripts.

News and Updates

  • [06/2025] Received EECS Fabulous Teacher Recognition. I appreciate the recognition from the students and committee.

  • [05/2025] Two papers accepted by ICML 2025: one spotlight paper on failure attribution of multi-agent LLMs and one on principal-agent bandits.

  • [02/2025] Talk at AAAI 2025 New Faculty Highlight: “Efficient and Robust Reinforcement Learning from Human Feedback”.

  • [01/2025] One paper on analyzing gradient entanglement of DPO and its variants is accepted by ICLR 2025.

  • [12/2024] Talk at CS colloquium series, University of Rochester: “Robust Reinforcement Learning from Biased Human Feedback and Corruption: Theory and Algorithms”.

  • [09/2024] One paper on risk-aware preference-based RL is accepted by NeurIPS 2024.

  • [08/2024] We received a new NSF award (IIS-2403401) on Neural Bandits. Thank you NSF!

  • [05/2024] One paper on conversational dueling bandits is accepted by KDD 2024.

  • [05/2024] One paper on adversarial attack on combinatorial bandits is accepted by ICML 2024.

  • [04/2024] One paper on fedrated pure exploration is accepted by UAI 2024.

  • [01/2024] One paper on policy alignment is accepted by ICLR 2024.

  • [12/2023] Two papers accepted by AAAI 2024: one on tree search bandits for protein optimization and one on stealthy attack against MAB.

  • [09/2023] One paper on offline RL for learning to rank is accepted by NeurIPS 2023.

  • [04/2023] One paper on representation learning in POMDP is accepted by ICML 2023. See you in Hawaii.

  • [01/2023] Our asynchronous kernel bandits paper is accepted by ICLR 2023.

  • [09/2022] Two papers accepted by NeurIPS 2022: one on distributed kernel bandits and the other on Thompson Sampling for Directed Evolution.

  • [09/2022] Joined EECS at Oregon State University as an Assistant Professor.

Honors and Awards

  • [06/2025], EECS Fabulous Teacher Recognition.

  • [02/2025], AAAI 2025 New Faculty Highlights.

  • [08/2021], ICML 2021 Best Reviewers (Top 10%).

  • [08/2019], SIGIR 2019 Best Paper Award.

  • [2018 - 2021], Bloomberg Data Science Ph.D. Fellowship.

Publications

Preprints

Tutorials

Service

  • Area Chair: ICLR 2023, 2024; NeurIPS 2023; KDD 2024