Shun Zhang

• shunzh@umich.edu
• shunzh.github.io
• San Francisco Bay Area

Research interests: Reinforcement learning; large language models; automated theorem proving; automatic code generation; value alignment.

Experience

Founding Member of Technical Staff, Asari AI (San Francisco, CA) Jun. 2024 - Present

• Building AI that plans, abstracts, verifies, and discovers new skills and knowledge.

Research Scientist, MIT-IBM Watson AI Lab
Postdoctoral Researcher, MIT-IBM Watson AI Lab
Postdoctoral Researcher, IBM-NJIT Jun. 2022 - Jun. 2024
Oct. 2021 - Jun. 2022
Aug. 2020 - Oct. 2021

• Conducted research and published papers on reinforcement learning and large language models, with a focus on competitive-level code generation, reinforcement learning from human feedback, and AI for scientific discovery.

Graduate Research Assistant, University of Michigan (Ann Arbor, MI) Sep. 2015 - Apr. 2020

• Conducted research and published papers on preference elicitation and AI safety in reinforcement learning.
• Designed active learning algorithms to improve a learning agent’s performance and guarantee safety in domains with uncertain objectives.

Software Development Engineer Intern, Amazon (Seattle, WA) Jun. - Aug. 2014

• Created a WebRTC-related internal tool to resolve cross-departmental communication issues.

Software Development Engineer Intern, Semantic Designs (Austin, TX) Jun. - Aug. 2013

• Created a user interface for a programming language analysis tool for better visualization.

Education

Ph.D. in Computer Science and Engineering, University of Michigan Sep. 2015 - Apr. 2020

• Dissertation: Efficiently Finding Approximately-Optimal Queries for Improving Policies and Guaranteeing Safety
• Advisors: Satinder Singh, Edmund H. Durfee

M.S. in Computer Science, University of Texas at Austin Aug. 2015

• Master thesis: Parameterized Modular Inverse Reinforcement Learning
• Committee members: Dana Ballard, Peter Stone

B.S. in Computer Science, University of Texas at Austin May 2014

Publications and Preprints

• Graph-Transformer-based Surrogate Model for Accelerated Converter Circuit Topology Design
  Shaoze Fan, Haoshu Lu, Shun Zhang, Ningyuan Cao, Xin Zhang, and Jing Li
  Design Automation Conference (DAC), 2024
  paper

• Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble (Short Paper)
  Shun Zhang, Zhenfang Chen, Sunli Chen, Yikang Shen, Zhiqing Sun, and Chuang Gan
  arXiv, 2024
  paper

• Adaptive Online Replanning with Diffusion Models
  Siyuan Zhou, Yilun Du, Shun Zhang, Mengdi Xu, Yikang Shen, Wei Xiao, Dit-Yan Yeung, and Chuang Gan
  Conference on Neural Information Processing Systems (NeurIPS), 2023
  paper

• 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
  paper

• 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
  paper

• 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
  paper

• 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
  Ph.D. Dissertation, 2020
  paper

• 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

• 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

• 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

• 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

• 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

Academic Services

Conference Reviewer

IEEE ITSC 2014, AAAI 2019, AISTATS 2023-24, CVPR 2023, ICML 2023-24, NeurIPS 2023-24, ICLR 2024.

Skills

Research

Reinforcement learning, convex optimization, deep learning, large language models, active learning, planning under uncertainty.

Programming languages

Proficient in Python (NumPy, PyTorch). Experienced in Java, C++, C, Scheme, Matlab.

Miscellaneous

• Languages: Mandarin Chinese (native), English (professional proficiency).
• No sponsorship required to work in the United States.