Zhuoran Yang

About

Zhuoran Yang is an Assistant Professor of Statistics and Data Science and Computer Science at Yale University. He is affiliated with the Yale Institute for Foundations of Data Science and the Center for Algorithms, Data, and Market Design at Yale (CADMY). His research interests lie at the intersection of machine learning, statistics, game theory, and optimization. Recently, his work has focused on the foundations of reinforcement learning, particularly in multi-agent systems where agents interact strategically, as well as the foundations of artificial intelligence, with an emphasis on understanding the emergent behaviors of large language models during pre-training and post-training and their relationship with model architecture. His research is supported by NSF DMS 2413243. Before joining Yale, Zhuoran Yang was a postdoctoral researcher at the University of California, Berkeley, working under the supervision of Michael I. Jordan. He earned his Ph.D. from the Department of Operations Research and Financial Engineering at Princeton University, where he was co-advised by Jianqing Fan and Han Liu. He completed his bachelor's degree in Mathematics at Tsinghua University in 2015.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Mathematics
• Mathematical optimization
• Engineering
• Combinatorics
• Discrete mathematics
• Management science

Selected publications

• Efficient and assured reinforcement learning-based building HVAC control with heterogeneous expert-guided training

  Scientific Reports · 2025-03-05 · 14 citations

  articleOpen access

  Building heating, ventilation, and air conditioning (HVAC) systems account for nearly half of building energy consumption and [Formula: see text] of total energy consumption in the US. Their operation is also crucial for ensuring the physical and mental health of building occupants. Compared with traditional model-based HVAC control methods, the recent model-free deep reinforcement learning (DRL) based methods have shown good performance while do not require the development of detailed and costl…

  PublisherOA PDFDOI

• Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information

  Management Science · 2025-08-06 · 2 citations

  article

  Motivated by the human-machine interaction such as recommending videos for improving customer engagement, we study human-guided human-machine interaction for decision making with private information. We model this interaction as a two-player turn-based game, where one player (Bob, a human) guides the other player (Alice, a machine) toward a common goal. Specifically, we focus on offline reinforcement learning (RL) in this game, where the goal is to find a policy pair for Alice and Bob that maxim…

  PublisherDOI

• Incremental birth-death element method for thermo-mechanical coupling simulation of fused deposition modeling process

  Journal of Manufacturing Processes · 2026-04-06

  article1st authorCorresponding
  PublisherDOI

• Training Language Models for Bilateral Trade with Private Information

  arXiv (Cornell University) · 2026-04-10

  preprintOpen accessSenior author

  Bilateral bargaining under incomplete information provides a controlled testbed for evaluating large language model (LLM) agent capabilities. Bilateral trade demands individual rationality, strategic surplus maximization, and cooperation to realize gains from trade. We develop a structured bargaining environment where LLMs negotiate via tool calls within an event-driven simulator, separating binding offers from natural-language messages to enable automated evaluation. The environment serves two…

  PublisherDOI

• Training Language Models for Bilateral Trade with Private Information

  ArXiv.org · 2026-04-10

  articleOpen accessSenior author

  Bilateral bargaining under incomplete information provides a controlled testbed for evaluating large language model (LLM) agent capabilities. Bilateral trade demands individual rationality, strategic surplus maximization, and cooperation to realize gains from trade. We develop a structured bargaining environment where LLMs negotiate via tool calls within an event-driven simulator, separating binding offers from natural-language messages to enable automated evaluation. The environment serves two…

  PublisherOA PDF

Frequent coauthors

• Zhaoran Wang

  125 shared

• Zhaoran Wang

  Shanghai University

  72 shared

• Kaiqing Zhang

  24 shared

• Michael I. Jordan

  20 shared

• Qi Cai

  Civil Aviation Administration of China

  20 shared

• Tamer Başar

  19 shared

• Xiaohan Wei

  University of Edinburgh

  17 shared

• Mingyi Hong

  17 shared

Similar researchers at Yale University

• Mark Gersteinh-217
• Andrew Barronh-88
• Huibin Zhouh-55