Yue Wang (王越)
Assistant Professor
Department of Electrical and Computer Engineering
Office: UCF Global Building-317 (main office) / HEC-442
Email: yue.wang AT ucf.edu
Yue Wang (王越)
Assistant Professor
Department of Electrical and Computer Engineering
Office: UCF Global Building-317 (main office) / HEC-442
Email: yue.wang AT ucf.edu
About Me.
I am an Assistant Professor at the Department of Electrical and Computer Engineering (joint appointment with the Computer Science Department), University of Central Florida. I am also affiliated with the Artificial Intelligence Institute.
I received my Ph.D. degree in Electrical engineering from The State University of New York at Buffalo in 2023, under the advisory of Prof. Shaofeng Zou, and my B.S. degree in mathematics from Nanjing University in 2019.
Research Interests.
My research interests specialize in machine learning, with a primary focus on reinforcement learning (RL), optimization, and generative models. Below are some of the areas I am interested in:
Robust RL and reliable decision making system
Multi-Agent RL and Game Theory
Optimization and Learning Theory
Generative models
Applications of RL
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 file 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 response). Please ping the department.
News
Paper: Two papers are accepted by ICML 2025. Congratulations to Zachary and Chi, and many thanks to our collaborators!
Paper: Our paper "Model-Free Offline Reinforcement Learning with Enhanced Robustness" has been accepted by ICLR 2025. Congratulations to Chi and Zain!
Paper: Our paper "A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch" has been accepted by NeurIPS 2024.
Paper: Our paper "Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward" has been accepted by IEEE TIT.
Paper: Our paper "Achieving the Asymptotically Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach" has been accepted by TMLR.
Paper: Our paper "Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation" has been accepted by ICML 2024.
Paper: Our paper "Model-Free Robust Reinforcement Learning with Sample Complexity Analysis" has been accepted by UAI 2024.
Paper: Our paper "Finite-time error bounds for Greedy-GQ" has been accepted by Machine Learning.
Grant: 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!
Paper: Our paper "Robust average-reward reinforcement learning" has been accepted by the Journal of Artificial Intelligence Research (JAIR).