Yuejie Chi
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Since July 2025, I am the Charles C. and Dorothea S. Dilley Professor of Statistics and Data Science at Yale University, with a secondary appointment in Computer Science. I also spend some time as a visiting researcher at FAIR. Before joining Yale, I was the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Melon University, with affiliation in MLD and CyLab.
My research interests lie in the theoretical and algorithmic foundations of data science, generative AI, reinforcement learning, and signal processing, motivated by applications in scientific and engineering domains. The problems my group studies are often interdisciplinary in nature, lying at the intersection of statistics, learning, optimization, and sensing. My current focus is on improving the performance, efficiency and reliability of generative AI and decision making, driven by data-intensive but resource-constrained scenarios. Specific lines of research topics can be found here.
I have been lucky to receive a couple of awards for my work, including Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House. I am the inaugural recipient of the IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing. In addition, I received SIAM Activity Group on Imaging Science Best Paper Prize, IEEE Signal Processing Society Young Author Best Paper Award, and young investigator awards from several agencies including NSF, ONR and AFOSR.
I am an IEEE Fellow for contributions to statistical signal processing with low-dimensional structures. I also had the privilege to deliver plenary, keynote and tutorial talks at several conferences and workshops, with more materials shared here. I was named the Goldsmith Lecturer by IEEE Information Theory Society in 2021, a Distinguished Lecturer by IEEE Signal Processing Society for 2022-2023, and a Distinguished Speaker by ACM for 2023-2026.
Upcoming talks/travels
Sep. 2025: invited plenary talk at Swiss CLOCK Summit.
Oct. 2025: tutorial on reinforcement learning at INFORMS Annual Meeting.
Dec. 2025: plenary talk at IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).
Selected Recent Papers
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF, ICLR 2025. [Arxiv]
Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction, NeurIPS 2024. [Arxiv] [Code]
Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models, ICLR 2024. [Arxiv]
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model, short version at NeurIPS 2023. [Arxiv]
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond, JMLR 2025. [Arxiv]
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Settling the Sample Complexity of Model-Based Offline Reinforcement Learning, Annals of Statistics, 2024. [Arxiv]
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Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis, Operations Research, 2024. [Arxiv]
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization, Operations Research, 2022. [Arxiv]
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing, short version at ICML 2023. [Arxiv]
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements, JMLR 2022. [Arxiv]
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent, JMLR 2020. [Arxiv]
Contact
Office: Kline Tower 1249, Yale University
Mailing address: 219 Prospect Street, New Haven, CT 06511
Email: first.last@yale.edu