Yuling Yan

I am a quantitative analyst at the D. E. Shaw Group in New York. Before moving to quantitative finance, I was an assistant professor in the Department of Statistics at University of Wisconsin-Madison from 2024 to 2025. Prior to that, I spent one year as a Norbert Wiener Postdoctoral Associate at MIT, working with Professor Philippe Rigollet and Professor Martin Wainwright. I obtained my Ph.D. degree at Princeton University in 2023, advised by Professor Yuxin Chen and Professor Jianqing Fan. I received my bachelor's degree from Peking University in 2018. Research interests: statistics, optimization, reinforcement learning, diffusion models. E-mail: yulingy at alumni dot princeton dot edu [Google Scholar] [LinkedIn]

Featured projects

(*=equal contribution, -=alphabetical order)

• Learning in the space of probability measures (with applications to generative AI)

  • Y. Yan*, K. Wang*, P. Rigollet, “Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow,” Annals of Statistics, vol. 52, no. 4, pp. 1774-1795, 2024

  • G. Li*, Y. Yan*, “Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models,” NeurIPS 2024

  • G. Li*, Y. Yan*, “O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions,” ICLR 2025

• Reinforcement learning (offline RL and RLHF)

  • Y. Yan, G. Li, Y. Chen, J. Fan, “The Efficacy of Pessimism in Asynchronous Q-learning,” IEEE Transactions on Information Theory, vol. 69, no. 11, pp. 7185-7219, 2023

  • Y. Yan, G. Li, Y. Chen, J. Fan, “Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games,” Operations Research, vol. 72, no. 6, pp. 2430-2445, 2024

  • G. Li, Y. Yan, Y. Chen, J. Fan, “Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning,”, under major revision at Mathematics of Operations Research, 2025 (accepted in part to COLT 2024)

  • G. Li*, Y. Yan*, “Towards Efficient Online Exploration for Reinforcement Learning with Human Feedback,” 2025

• Inference and uncertainty quantification for low-rank models

  • Y. Chen, J. Fan, C. Ma, Y. Yan (-), “Inference and Uncertainty Quantification for Noisy Matrix Completion,” Proceedings of the National Academy of Sciences, vol. 116, no. 46, pp. 22931-22937, 2019

  • Y. Yan, Y. Chen, J. Fan, “Inference for Heteroskedastic PCA with Missing Data,” Annals of Statistics, vol. 52, no. 2, pp. 729–756, 2024

  • Y. Yan, M. J. Wainwright, “Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach,” 2024

  • E. Xia*, Y. Yan*, M. J. Wainwright, “Inference under Staggered Adoption: Case Study of the Affordable Care Act,” 2024

• Bridging convex and nonconvex optimization in low-rank estimation

  • Y. Chen, C. Chi, J. Fan, C. Ma, Y. Yan (-), “Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization,” SIAM Journal on Optimization, vol. 30, no. 4, pp. 3098-3121, 2020

  • Y. Chen, J. Fan, C. Ma, Y. Yan (-), “Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data,” Annals of Statistics, vol. 49, no. 5, pp. 2948-2971, 2021

  • Y. Chen, J. Fan, B. Wang, Y. Yan (-), “Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution Under Random Designs,” Journal of the American Statistical Association, vol. 118, no. 542, pp. 858-868, 2023

• Mechanism design for improving the peer review

  • Y. Yan, W. J. Su, J. Fan, “Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review,” accepted to Journal of the Royal Statistical Society: Series B, 2025+

  • B. Su, J. Zhang, N. Collina, Y. Yan, D. Li, K. Cho, J. Fan, A. Roth, W. J. Su, “The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review” accepted to Journal of the American Statistical Association (as a discussion paper), 2025+

Selected awards

• IMS Lawrence D. Brown Award, 2024

• ICCM Best Thesis Award (Silver Medal, declined due to inability to attend the award ceremony), 2024

• Norbert Wiener Postdoctoral Fellowship, MIT, 2023

• Charlotte Elizabeth Procter Honorific Fellowship, Princeton University, 2022

• ASA Statistical Learning and Data Science Best Student Paper Award, 2022