Welcome to Ke Sun's Homepage.

My name is Ke Sun (孙科). I am a Postdoctoral Fellow working with Prof. Susan A. Murphy in the Department of Statistics and Computer Science at Harvard University.

I received my Ph.D. degree in Statistical Machine Learning from University of Alberta in 2024, advised by Prof. Linglong Kong, and was also an affiliated student at Alberta Machine Intelligence Institute (Amii). During my Ph.D., I was fortunate to visit the Data Science Group at the London School of Economics and Political Science (LSE) in 2023, advised by Prof. Chengchun Shi. I obtained my MPhil in Data Science at Peking University (PKU) in 2020 under the supervision of Prof. Zhouchen Lin and Prof. Zhanxing Zhu. I earned my Bachelor's degree in Financial Statistics and Risk Management from SouthWestern University of Finance and Economics (SWUFE) in 2017.

I am looking for a new postdoctoral position starting in 2026!

Contact: kesun[AT] fas.harvard.edu Links: Google Scholar / CV / Github / LinkedIn

Research Interests

My research interest is reinforcement learning. I am passionate about analyzing and developing practically efficient, robust, and adaptive decision-making algorithms guided by mathematical and statistical principles to achieve artificial general intelligence. Currently, I am working on the following topics:

Uncertainty: Distributional RL/Risk Control, Entropy Regularization/Exploration, Predictive Uncertainty (Bayesian/Conformal)
Robustness/Trustworthiness: Distribution Shift and Environmental Nonstationarity, Interpretability, Safe RL, Meta/Continual RL
Offline/Causality: Adaptive Experiment Design for Causal Inference, Offline RL, Causality for RL
Probabilistic Modeling: Inverse RL, Generative and Self-supervised RL, Model-based RL/World Model
Applications: Games, Robotics, Transportation, Healthcare, Economics and Finance, Language Intelligence

Selected Preprints / Publications [Full Publications]

[7] Ke Sun, Linglong Kong, Hongtu Zhu, Chengchun Shi. ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Experiments. (submitted)

[6] Ke Sun, Jun Jin, Xi Chen, Wulong Liu, Linglong Kong. Reweighted Bellman Targets for Continual Reinforcement Learning. (submitted) Preliminary Version Accepted in ICML Workshop: Aligning Reinforcement Learning Experimentalists and Theorists, 2024

[5] Ke Sun, Yingnan Zhao, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, Linglong Kong. The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning. Advances in Neural Information Processing Systems (NeurIPS), 2025

[4] Ke Sun, Yingnan Zhao, Wulong Liu, Bei Jiang, Linglong Kong. Distributional Reinforcement Learning with Regularized Wasserstein Loss [code]. Advances in Neural Information Processing Systems (NeurIPS), 2024

[3] Ke Sun*, Yafei Wang*, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong. (*equal contribution in alphabetical order). Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization [code]. Advances in Neural Information Processing Systems (NeurIPS), 2021.

[2] Ke Sun, Zhanxing Zhu, Zhouchen Lin. AdaGCN: AdaBoosting Graph Convolutional Networks into Deep Models [code]. International Conference on Learning Representations (ICLR), 2021.

[1] Ke Sun, Zhouchen Lin, Zhanxing Zhu. Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels [code]. Association for the Advancement of Artificial Intelligence (AAAI), 2020.

Favorite Quotes

Practice without theory is blind, but equally, theory without practice is mere intellectual play. ——Immanuel Kant

The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. …… The danger is that if we define the boundaries of our field in terms of familiar tools and familiar problems, we will fail to grasp the new opportunities. —— Leo Breiman