Ali Kaazempur-Mofrad

A Data Envelopment Analysis Approach for Assessing Fairness in Resource Allocation: Application to Kidney Exchange Programs

Ali Kaazempur-Mofrad and Xiaowu Dai

The Annals of Applied Statistics, 20(1):385–407, 2026

Fairness in kidney exchange involves waiting time, access to donor organs, and transplant outcomes. We develop a conditional data envelopment analysis framework to evaluate these dimensions together, with conformal prediction to quantify uncertainty. An analysis of U.S. transplant data identifies disparities across ethnic groups.

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A Statistical Market-Design Framework for Academic Job Markets

Ali Kaazempur-Mofrad, Xiaowu Dai, and Xuming He

Departments allocate interviews with limited information about candidates’ interest. We combine structured preference signals with confidence-calibrated statistical ranking to guide interview selection. The framework studies incentives for truthful participation and improves matching outcomes in simulations informed by U.S. statistics departments.

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• Project website
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Beyond a Single Answer: Proper Scoring of LLM Uncertainty

Ali Kaazempur-Mofrad, Xiaowu Dai, and Xuming He

A single answer can hide a language model’s uncertainty over alternatives. We study how proper scoring rules can elicit probability reports across plausible answers, and how those reports can support decisions under uncertainty.

• Code & experiments