Scholar
Chi Jin
Google Scholar ID: GINhGvwAAAAJ
Assistant Professor, Princeton University
Citations & Impact
Citations
11,770
H-index
47
i10-index
69
Publications
20
Co-authors
65
list available
Publications
Resume (English only)
Academic Achievements
- Princeton AI Lab Seed Grant, 2025
- Sloan Research Fellowship, 2024
- NSF CAREER Award, Division of Information and Intelligent Systems, 2023
- E. Lawrence Keyes, Jr./Emerson Electric Co. Faculty Advancement Award, 2023
- Princeton Commendation for Outstanding Teaching (ECE524), 2022 & 2024
- Princeton Commendation for Outstanding Teaching (ECE539), 2021 & 2023
- Princeton SEAS Innovation Award, 2022
- Best Paper Award, ICLR 2022 Workshop on Gamification and Multiagent Solutions
- Best Paper Award, ICML 2018 Workshop on Exploration in Reinforcement Learning
- Published Goedel-Prover (COLM 2025) and Goedel-Prover-V2 (arXiv), establishing the strongest open-source theorem prover to date
Research Experience
- Assistant Professor (now Associate Professor) at Princeton University, Department of Electrical and Computer Engineering
- Leads research on LLM reasoning & agents, game theory & MARL, statistical learning, and optimization
- Teaches courses including ECE524 (Foundations of Reinforcement Learning), ECE539/COS512, and COS511/ECE434/COS434
- Delivered a tutorial on multiagent reinforcement learning at the Simons Institute
- Principal investigator of the Goedel-Prover project on automated theorem proving with LLMs
Background
- Associate Professor of Electrical and Computer Engineering, Princeton University
- Associated Faculty Member of Computer Science
- Research focuses on decision-making aspects of machine learning
- Develops intelligent agents capable of advanced reasoning, strategic planning, and complex task execution
- Contributions span theoretical foundations of reinforcement learning, multi-agent learning, game theory, statistical learning theory, and optimization
- Currently extending work to AI for mathematics and games, with emphasis on grounded and verifiable AI systems
