About Me

I am a Ph.D. student in Statistics at Washington University in St. Louis, where I am advised by Prof. Ran Chen. Meanwhile, I am a scholar of the McDonnell International Scholars Academy at WashU.

My research focuses on the statistical foundations of reinforcement learning. I am particularly interested in developing efficient and adaptive learning methods for heterogeneous and evolving environments, and understanding how the availability and structure of data affect sequential decision-making. My work draws on reinforcement learning, nonparametric statistics, and statistical learning theory, with potential applications in healthcare and business.

Before joining WashU, I obtained my Bachelor of Science in Mathematics from Tsinghua University.

Research Interests

Reinforcement Learning: Personalized RL, Non-stationary RL, Offline and Online Learning

Nonparametric Statistics: Kernel Methods, Statistical Learning Theory

Applications: AI for Healthcare and Business

Working Papers

1. Personalized Reinforcement Learning with Nonparametric Kernel Embeddings
   Yicheng Teng, Ran Chen.
   Manuscript in preparation.

2. How offline data shape the value of online interaction
   Yicheng Teng, Ran Chen.
   Working paper.

Talks

1. Personalized Reinforcement Learning with Nonparametric Kernel Embeddings
   Joint Statistical Meetings, Boston, 2026.

Teaching

1. TA: SDS5525 Theory of Statistics (Fall 2026)
2. TA: SDS5515 Probability Theory for Statistics and Data Science (Fall 2026)