Research interests
My research lies in the broad area of Statistics, Data Science and Machine Learning, with a special interest in developing novel and principled methods to handle real data with spatial-temporal dependence.
Research interests
My research lies in the broad area of Statistics, Data Science and Machine Learning, with a special interest in developing novel and principled methods to handle real data with spatial-temporal dependence.
During my PhD study, much of my work focuses on answering “what if” counterfactual questions and elucidating causal connections within the context of healthcare, aiming to empower healthcare professionals with enhanced situational awareness, promote individualized treatment, and potentially steer biomedical research to validate the identified causal relationships.
During my PhD study, much of my work focuses on answering “what if” counterfactual questions and elucidating causal connections within the context of healthcare, aiming to empower healthcare professionals with enhanced situational awareness, promote individualized treatment, and potentially steer biomedical research to validate the identified causal relationships.
Specifically, I work on:
Trustworthy AI: causality (causal discovery, causal inference, and difference in difference) and fairness,
Reinforcement learning for generative AI (e.g., RLHF),
Time series and point process,
Hypothesis testing (two-sample test, goodness-of-fit test, and change-point detection),
and their applications in healthcare, power systems, finance, psychology, criminology, and so on.