Welcome to the homepage of Shun Zhang (in Simplified Chinese: 张舜)!
I am a research scientist at the MIT-IBM Watson AI Lab. My research interests lie in reinforcement learning (RL) and artificial general intelligence (AGI), with a focus on the applications of RL in program synthesis and AI for design.
I received my Ph.D. at the University of Michigan, advised by Prof. Satinder Singh and Prof. Ed Durfee. My dissertation is on Efficiently Finding Approximately-Optimal Queries for Improving Policies and Guaranteeing Safety (defense slides). Before that, I received BS and MS in computer science at the University of Texas at Austin.
Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum, and Chuang Gan. “Planning with Large Language Models for Code Generation”. In submission.
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, and Chuang Gan. “Prompting Decision Transformer for Few-shot Policy Generalization”. In International Conference on Machine Learning (ICML), 2022. paper
Shaoze Fan, Shun Zhang, Jianbo Liu, Ningyuan Cao, Xiaoxiao Guo, Jing Li, and Xin Zhang. “Power Converter Circuit Design Automation using Parallel Monte Carlo Tree Search”. In ACM Transactions on Design Automation of Electronic Systems (TODAES), 2022.
Shaoze Fan, Ningyuan Cao, Shun Zhang, Jing Li, Xiaoxiao Guo, and Xin Zhang. “From Specification to Topology: Automatic Power Converter Design via Reinforcement Learning”. In International Conference on Computer Aided Design (ICCAD), 2021. paper
Shun Zhang, Edmund H. Durfee, and Satinder Singh. “Querying to Find a Safe Policy Under Uncertain Safety Constraints in Markov Decision Processes”. In AAAI Conference on Artificial Intelligence (AAAI), 2020. paper
Shun Zhang, Edmund H. Durfee, and Satinder Singh. “Minimax-Regret Querying on Side Effects for Safe Optimality in Factored Markov Decision Processes”. In International Joint Conference on Artificial Intelligence (IJCAI), 2018. paper
Shun Zhang, Edmund H. Durfee, and Satinder Singh. “Approximately-Optimal Queries for Planning in Reward-Uncertain Markov Decision Processes”. In International Conference on Automated Planning and Scheduling (ICAPS), 2017. paper
Ruohan Zhang, Shun Zhang, Matthew H. Tong, Yuchen Cui, Constatin A. Rothkopf, Dana H. Ballard, and Mary M. Hayhoe. “Modeling Sensory-Motor Decisions in Natural Behavior”. In PLoS Computational Biology, 2018. paper
Katie Genter, Shun Zhang, and Peter Stone. “Determining Placements of Influencing Agents in a Flock”. In Autonomous Agents and Multiagent Systems (AAMAS), 2015. paper
Tsz-Chiu Au, Shun Zhang, and Peter Stone. “Autonomous Intersection Management for Semi-Autonomous Vehicles”. In Handbook of Transportation, 2015. paper