Zhongren Chen
I am a Ph.D. student in Statistics and Data Science at Yale University. I am very fortunate to be advised by Professor Joshua Kalla, Professor Johan Ugander, and Professor Xiaohong Chen. My research lies at the intersection of political science, artificial intelligence, and statistics. I examine how AI shapes human behavior and how social science can benefit from the rise of AI. My recent work focuses on:
- How large language models influence political persuasion and polarization.
- How machine learning can enhance causal inference under real-world complications.
I received my M.S. in Statistics from Stanford University, where I was advised by Professor Wing Hung Wong and Professor Lu Tian. I received my B.A. in Mathematics and Statistics from the University of Oxford.
Publications and Preprints
(† = co-first author, * = alphabetical order)
Political Communication
A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies
Zhongren Chen*, Joshua Kalla, Quan Le, Shinpei Nakamura-Sakai, Jasjeet Sekhon, Ruixiao Wang
Journal of Experimental Political Science, 2026
Generalizing Causal Effects with Noncompliance: Application to Deep Canvassing Experiments
Zhongren Chen, Melody Huang
2026. Conditionally accepted at Political Analysis
Benchmarking Political Persuasion Risks Across Frontier Large Language Models
Zhongren Chen*, Joshua Kalla, Quan Le
2026. arXiv Preprint
AI and Causal Inference
An Encoding Generative Modeling Approach to Dimension Reduction and Covariate Adjustment in Causal Inference with Observational Studies
Qiao Liu, Zhongren Chen, and Wing Hung Wong
Proceedings of the National Academy of Sciences, 2024
Painless Activation Steering: An Automated, Lightweight Approach for Post-Training Large Language Models
Sasha Cui, Zhongren Chen
2026. arXiv Preprint
Quantile-Optimal Policy Learning under Unmeasured Confounding
Zhongren Chen, Siyu Chen, Zhuoran Yang, Zhengling Qi, and Xiaohong Chen
2024. arXiv Preprint
Miscellaneous in AI and Statistics
An Empirical Bayes Approach for Constructing the Confidence Intervals of Clonality and Entropy
Zhongren Chen, Lu Tian, and Richard Olshen
Journal of Applied Statistics, 2025
Subgraph Frequency Distribution Estimation using Graph Neural Networks
Zhongren Chen†, Xinyue Xu†, Shengyi Jiang, Hao Wang, and Lu Mi
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Workshop on Deep Learning on Graphs, 2022
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
Han Zhong, Zhongren Chen, Zhuoran Yang, Zhaoran Wang, Csaba Szepesvári
2021. Revise and Resubmit at the Journal of Machine Learning Research
Employment
Amazon, Customer Behavior Analytics
Applied Scientist Internship, June 2026 – August 2026
MIT, CSAIL Computational Connectomics Group
Research Internship, Oct. 2022 – Dec. 2022