Research

Overview

I am broadly interested in the field of nonparametric statistics and machine learning. The overarching theme of my research is on developing novel statistical and machine learning methods that are motivated by real-world problems and supported by rigorous theory. One of my current focuses is the statistical problems that involve functional data. On the one hand, I develop methodology and theory for functional data analysis with modern data challenges such as multidimensionality and sparse design. On the other hand, I am interested in the applications of functional data in other statistical areas, such as causal inference. Another branch of my current research is handling imperfect data where observations are partially missing or collected under an undesirable distribution. In particular, I study such problems in different areas such as causal inference, matrix completion, and reinforcement learning.

Research Interests

• Functional Data
• Low-rank Modeling
• Missing Data
• Causal Inference
• Reinforcement Learning