CV

SAS Programmer

Baim Institute for Clinical Research

• Supported the continued success and quality of biostatistics clinical research projects
• Performed SAS programming using such techniques as macro language, advanced data manipulation techniques, and statistical procedures (e.g., PROC GLM, PROC FREQ, PROC REPORT)
• Reviewed and provided feedback regarding data management plans and research manuscripts for publication

Global IT Risk Management Assistant

• Completed 12 main technology risk governance and supplier risk assessments and data protection projects
• Aligned objectives and bridged communications with Oracle teams to perform internet data analysis using SQL

Business Analytics Assistant

• Tasked with collating, stratifying, and visualizing 20% of North Region sales data in Q3 & Q4 2017
• Optimized and enhanced sales prediction models by leveraging historical data and market research

Doctor of Philosophy

Indiana University

Statistical Science, with a M.S. in Computer Science

• Advanced Statistical Theory I & II
• Seminar on Statistical Theory
• High-dimensional Statistical Analysis
• Theory of Probability
• Elements of Artificial Intelligence
• Data Mining
• Machine Learning
• Applied Algorithms
• Computer Vision

Master of Science (Dual Degrees)

University of Michigan

Applied Statistics & Sport Management

• Statistical Inference
• Bayesian modeling and computation
• Nonparametric Statistics
• Financial Management for the Sport Industry
• Strategic Management in Sport

Bachelor of Engineering

Sun Yat-sen University (中山大学)

Theoretical and Applied Mechanics, with a minor in Statistics

• Advanced Mathematics 1(Highest Level) I & II
• Linear Algebra
• Computer Algorithmic Language
• Ordinary Differential Equations
• Methods of Mathematical Physics
• Operations Research

Department of Statistics Graduate Student Fellowship

Department of Statistics, Indiana University

Received fellowships for graduate students twice from Department of Statistics in 2022 and 2023.

Obstructive sleep apnea in older adults: geographic disparities in PAP treatment and adherence

Journal of Clinical Sleep Medicine

Significant state-level and regional disparities of PAP treatment and adherence among Medicare beneficiaries with OSA suggest gaps in delivery of OSA care for older Americans.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning

Continuous-time reinforcement learning (CTRL) provides a principled framework for sequential decision-making in environments where interactions evolve continuously over time. Despite its empirical success, the theoretical understanding of CTRL remains limited, especially in settings with general function approximation. In this work, we propose a model-based CTRL algorithm that achieves both sample and computational efficiency. Moreover, we introduce structured policy updates and an alternative measurement strategy that significantly reduce the number of policy updates and rollouts while maintaining competitive sample efficiency. Our proposed algorithms are validated through experiments on continuous control tasks and diffusion model fine-tuning, demonstrating comparable performance with significantly fewer policy updates and rollouts.

• Continuous-time Reinforcement Learning
• Sampling and Computationally Efficient
• Policy Update and Measurement Strategy Algorithm