Please visit my new website: https://ywang294.github.io
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Yue Wang (王越)
Assistant Professor
Department of Electrical and Computer Engineering
Office: UCF Global-317
Email: yue.wang AT ucf.edu
About Me.
I am an Assistant Professor in the Department of Electrical and Computer Engineering (jointly appointed with the Computer Science Department) at the University of Central Florida. I am also affiliated with the Artificial Intelligence Institute.
I received my Ph.D. in Electrical Engineering from The State University of New York at Buffalo in 2023, under the advisement of Prof. Shaofeng Zou, and my B.S. in Mathematics from Nanjing University in 2019.
Research Interests.
My research interests specialize in machine learning, with a primary focus on reinforcement learning (RL). Below are some of the areas I am interested in:
Robust RL and reliable decision-making systems
Multi-Agent RL and Game Theory
Optimization and Learning Theory
Alignment and preference-based learning
Applications of RL in robotics
Prospective Students.
I am looking for highly motivated Ph.D. students with strong backgrounds in mathematics, statistics, computer science, or electrical engineering. If you have a solid foundation in mathematical concepts and are interested in working with me, please fill out this form and send me an email with your CV and transcripts.
Students at UCF are encouraged to contact me for research opportunities/(honor) thesis, if you are interested in my work.
Students whom I have never met before, DO NOT cold email me about getting you a grader/TA job ( I will not respond). Please ping the department.
News
Zain and Debamita's paper is accepted by ICLR 2026; Another two papers are accepted by ICASSP and AISTATS. Congratulations to the students and collaborators!
Our first paper on LLM fine-tuning is accepted by AAMAS 2026 as an Extended Abstract.
Two papers are accepted by AAAI 2026 (accepted rate 17.6%, and one is selected as Oral). Congratulations to Shihab and Debamita, and thanks to our collaborators!
Two papers are accepted by ICML 2025. Congratulations to Zachary and Chi, and many thanks to our collaborators!
Our paper "Model-Free Offline Reinforcement Learning with Enhanced Robustness" has been accepted by ICLR 2025. Congratulations to Chi and Zain!
Our paper "A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch" has been accepted by NeurIPS 2024.
Our paper "Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward" has been accepted by IEEE TIT.
Our paper "Achieving the Asymptotically Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach" has been accepted by TMLR.
Our paper "Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation" has been accepted by ICML 2024.
Our paper "Model-Free Robust Reinforcement Learning with Sample Complexity Analysis" has been accepted by UAI 2024.
Our paper "Finite-time error bounds for Greedy-GQ" has been accepted by Machine Learning.
Our "Distributionally Robust Approaches to Transfer Learning" (Co-PI) project has been awarded a prestigious DARPA Award totaling $1.23M, part of DARPA's Transfer from Imprecise and Abstract Models to Autonomous Technologies Program. Thanks, DARPA!
Our paper "Robust average-reward reinforcement learning" has been accepted by the Journal of Artificial Intelligence Research (JAIR).
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