Areas of Interest
Statistics, machine learning, causal inference
Research
My research interests encompass statistics, machine learning, and data science. I develop and analyze predictive and inferential tools for complex data problems such as imbalanced classes, high-dimensional data, transfer learning, and observational studies. My focus is on designing theoretically sound and efficient learning algorithms that address sample, time, and space complexity challenges.
I aim to enhance the trustworthiness and reliability of statistics and machine learning methods, particularly in critical domains like healthcare. My work includes developing user-friendly prediction tools with built-in confidence measures and methods for individualized estimation, prediction, and recommendation from observational and interventional data.
Courses
Fall 2026
| Course |
Section |
Title |
Schedule |
Room |
| Math 534 |
01 |
Practical Data Analysis |
TR 9:45–11:15
|
WH-100E
|
Ph.D. Students
-
Baozhen Wang,
Spring, 2025
— Conformal Prediction Methods for Distribution Shifts & Casual Effect Estimation
-
Xinhai Zhang,
Summer, 2025
— Identification & Estimation of Conditional Average Treatment Effects under Unmeasured Confounding with Instrumental Variables
-
Zhou Wang,
Summer, 2024
— Set-valued Classification and Conformal Prediction in Out-of-distribution Detection and Bandit Feedback Settings
-
Haomiao Meng,
Spring, 2020
— Machine Learning Methods on Selected Topics of Precision Medicine
-
Chen Liang,
Fall, 2019
(Co-advisor:
Ganggang Xu)
— Goodness-of-fit Tests for Spatial Cluster Point Process Models
-
Wenbo Wang,
Spring, 2019
— Set-Valued Classification Via Confidence Set Learning
-
Lin Yao,
Spring, 2019
(Co-advisor:
Ganggang Xu)
— James-Stein-Type Optimal Weight Choice for Frequentist Model Average Estimator
-
Qiyi Lu,
Fall, 2015
— Learning Partially Labeled Data in the High-dimensional, Low-sample Size Setting
Notes
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