Statistical Decision-Making & Inference
Learning and quantifying uncertainty in complex decision systems.
About
I am a PhD candidate in Statistics at UCLA, advised by Professor Xiaowu Dai. My research develops statistical methods for decision-making in complex systems where uncertainty, incentives, and consequential decisions interact.
My work combines statistical inference, machine learning, and ideas from mechanism design and game theory to study how information should be learned, communicated, and used in high-stakes settings. Current applications include matching and allocation markets, healthcare resource allocation, and reliable AI systems. More broadly, I am interested in statistical decision-making, market design, ranking and inference under uncertainty, and reliable AI.
Research
My research develops statistical methodology for decision-making under uncertainty, with an emphasis on strategic interactions, allocation and matching systems, and AI evaluation.
Markets, Mechanisms & Resource Allocation
Designing systems where incentives, preferences, fairness, and limited resources interact.
AI Evaluation & Decision-Making
Developing statistical foundations for evaluating and deploying modern AI systems.
Selected Papers
A data envelopment analysis approach for assessing fairness in resource allocation: Application to kidney exchange programs
A Statistical Market-Design Framework for Academic Job Markets
Teaching
Teaching Assistant, UCLA Statistics & Data Science
Undergraduate and graduate teaching experience spanning introductory and mathematical statistics, regression and linear models, statistical computing, Monte Carlo methods, statistical consulting, and large language models in text mining.
View teaching experience