Ali Kaazempur-Mofrad

PhD Candidate in Statistics & Data Science at UCLA

I develop statistical and market-design methods for scarce allocation systems, with current work on fairness in kidney exchange and preference signaling in the academic job market.

Department
Statistics & Data Science
University of California, Los Angeles
Portrait of Ali Kaazempur-Mofrad

About

I am a PhD candidate in the Department of Statistics & Data Science at the University of California, Los Angeles and a member of the SCALE Lab. My research lies at the intersection of statistical market design, ranking and inference under uncertainty, and fairness in resource allocation.

I study allocation and matching problems where incentives, uncertainty, and fairness interact. Current applications include kidney exchange and the academic job market, alongside broader interests in mechanism design, matching markets, game theory, and statistical inference for high-stakes decisions.

Research

My research is at the intersection of matching markets, mechanism design, and statistical inference, including ranking under uncertainty.

Matching Markets & Mechanism Design

Allocation and matching systems shaped by incentives, strategic behavior, and market design.

Statistical Inference and Ranking

Statistical methods for learning, comparison, and ranking under uncertainty.

Publications

Teaching

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 (Graduate course)