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. My undergraduate/master research is advised by Prof. Peter Stone and Prof. Dana Ballard.

My CV.

Publications

Planning with Large Language Models for Code Generation
Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum, and Chuang Gan
International Conference on Learning Representations (ICLR), 2023.
Also presented at the Foundation Models for Decision Making Workshop at NeurIPS, 2022.

Hyper-Decision Transformer for Efficient Online Policy Adaptation
Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, and Chuang Gan
International Conference on Learning Representations (ICLR), 2023.
Also presented at the Foundation Models for Decision Making Workshop at NeurIPS, 2022.

Prompting Decision Transformer for Few-shot Policy Generalization
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, and Chuang Gan
International Conference on Machine Learning (ICML), 2022. paper

Power Converter Circuit Design Automation using Parallel Monte Carlo Tree Search
Shaoze Fan, Shun Zhang, Jianbo Liu, Ningyuan Cao, Xiaoxiao Guo, Jing Li, and Xin Zhang
ACM Transactions on Design Automation of Electronic Systems (TODAES), 2022.

From Specification to Topology: Automatic Power Converter Design via Reinforcement Learning
Shaoze Fan, Ningyuan Cao, Shun Zhang, Jing Li, Xiaoxiao Guo, and Xin Zhang
International Conference on Computer Aided Design (ICCAD), 2021. paper

Efficiently Finding Approximately-Optimal Queries for Improving Policies and Guaranteeing Safety
Shun Zhang
PhD Dissertation. slides, dissertation

Querying to Find a Safe Policy Under Uncertain Safety Constraints in Markov Decision Processes
Shun Zhang, Edmund H. Durfee, and Satinder Singh
AAAI Conference on Artificial Intelligence (AAAI), 2020. paper
Also presented at the Safety and Robustness in Decision-making Workshop at NeurIPS, 2019.

Minimax-Regret Querying on Side Effects for Safe Optimality in Factored Markov Decision Processes
Shun Zhang, Edmund H. Durfee, and Satinder Singh
International Joint Conference on Artificial Intelligence (IJCAI), 2018. paper
Also presented as an extended abstract at Autonomous Agents and Multiagent Systems (AAMAS), 2018.

Approximately-Optimal Queries for Planning in Reward-Uncertain Markov Decision Processes
Shun Zhang, Edmund H. Durfee, and Satinder Singh
International Conference on Automated Planning and Scheduling (ICAPS), 2017. paper
Also presented at Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2017.

Modeling Sensory-Motor Decisions in Natural Behavior
Ruohan Zhang, Shun Zhang, Matthew H. Tong, Yuchen Cui, Constatin A. Rothkopf, Dana H. Ballard, and Mary M. Hayhoe
PLoS Computational Biology, 2018. paper
A preliminary version is presented at Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2015.

Determining Placements of Influencing Agents in a Flock
Katie Genter, Shun Zhang, and Peter Stone
Autonomous Agents and Multiagent Systems (AAMAS), 2015. paper

Autonomous Intersection Management for Semi-Autonomous Vehicles
Tsz-Chiu Au, Shun Zhang, and Peter Stone
Handbook of Transportation, 2015. paper
Also presented at Autonomous Agents and Multiagent Systems (AAMAS), 2014.

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