Decision-Making Under Uncertainty
Statistical inference for decisions when outcomes are uncertain and incentives affect the information available.
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, market design, and machine learning to study how information is elicited, communicated, and used in high-stakes decisions. Current projects examine preference signals in academic hiring, fairness in kidney exchange, and uncertainty reporting by LLMs.
Research
My research focuses on how information is elicited and used to make decisions under uncertainty, particularly in matching, resource allocation, and LLM evaluation.
Market Design, Matching & Resource Allocation
Preference-signaling mechanisms for matching; statistical assessment of fairness in resource allocation.
LLM Evaluation & Decision Support
Evaluating LLM probability reports across plausible answers and using them to guide decisions.
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