Fairness in Resource Allocation
Studying fairness in scarce allocation systems such as kidney exchange using multi-criteria efficiency analysis and uncertainty-aware statistical evaluation.
Profile
About
I am a PhD candidate in the Department of Statistics & Data Science at the University of California, Los Angeles. My research sits at the intersection of statistical inference and market design, with an emphasis on allocation problems where fairness, uncertainty, and strategic behavior all matter.
As a member of the SCALE Lab, I develop statistical methods and mechanism-design frameworks for resource allocation and matching. One line of work studies fairness in kidney exchange through multi-criteria efficiency evaluation and uncertainty-aware inference. Another develops structured preference signaling and confidence-calibrated interview ranking for the academic job market. I am also interested in extending these ideas to broader questions of truthful elicitation and strategyproof mechanism design.
Research
Current Research
My work develops statistical and market-design tools for allocation problems with scarce resources, latent preferences, and strategic behavior. Current applications span organ transplant allocation and congested academic labor markets.
Statistical Market Design
Designing mechanisms for congested two-sided markets, including structured preference signaling for academic hiring.
Ranking Under Uncertainty
Building confidence-calibrated ranking and inference procedures that support high-stakes selection decisions with statistical guarantees.
Strategyproof Mechanisms
Studying how incentive-compatible and truthful mechanisms can improve allocation, matching, and selection decisions in strategic environments.
Publications
Selected Work
Current work spans fairness evaluation in kidney exchange and statistical market design for congested academic hiring.
Published paper
A data envelopment analysis approach for assessing fairness in resource allocation: Application to kidney exchange programs
This paper develops a unified data envelopment analysis framework for evaluating priority, access, and outcome fairness in kidney exchange. Using UNOS data, it studies disparities in allocation efficiency across ethnic groups and impements uncertainty quantification through conformal prediction and reference frontier mapping.
Working paper
A Statistical Market-Design Framework for the Academic Job Market
This paper frames interview allocation in the academic job market as a ranking problem under uncertainty. It proposes a structured preference-signaling mechanism, candidate-specific utility and acceptance modeling, and a confidence-calibrated ranking procedure with truthful-participation, welfare, and stability guarantees.
Teaching
Teaching Experience
I have supported undergraduate and graduate statistics courses across probability, regression, computation, consulting, and applied data analysis at UCLA.
Graduate Student Instructor
- STATS 10: Introduction to Statistical Reasoning
- STATS 13: Introduction to Statistical Methods for Life and Health Sciences
- STATS 100B: Introduction to Mathematical Statistics
- STATS 100C: Linear Models
- STATS 101A: Introduction to Data Analysis and Regression
- STATS 102A: Introduction to Computational Statistics with R
- STATS 102C: Introduction to Monte Carlo Methods
- STATS 140XP: Practice of Statistical Consulting
- STATS 403: Mathematical Statistics
Reader / Grader
- STATS 12: Introduction to Statistical Methods for Geography and Environmental Studies
- STATS 13: Introduction to Statistical Methods for Life and Health Sciences
- STATS 100B: Introduction to Mathematical Statistics
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