About

Professor Itai Ashlagi is a faculty member of Management Science and Engineering at Stanford University. He is also a Senior Fellow at the Stanford Institute for Economic Policy Research and holds a courtesy appointment in the Department of Economics. His research focuses on management science and engineering, with particular interest in economic policy, market design, and optimization. As a professor, he contributes to the academic community through his teaching and research, advancing understanding in his field.

Research topics

• Artificial Intelligence
• Computer Science
• Microeconomics
• Economics
• Econometrics
• Internal medicine
• Advertising
• Medicine
• Business

Selected publications

• Stable Matching with Interviews

  ArXiv.org · 2025-01-21 · 1 citations

  preprintOpen access1st authorCorresponding

  In several two-sided markets, including labor and dating, agents typically have limited information about their preferences prior to mutual interactions. This issue can result in matching frictions, as arising in the labor market for medical residencies, where high application rates are followed by a large number of interviews. Yet, the extensive literature on two-sided matching primarily focuses on models where agents know their preferences, leaving the interactions necessary for preference discovery largely overlooked. This paper studies this problem using an algorithmic approach, extending Gale-Shapley's deferred acceptance to this context. Two algorithms are proposed. The first is an adaptive algorithm that expands upon Gale-Shapley's deferred acceptance by incorporating interviews between applicants and positions. Similar to deferred acceptance, one side sequentially proposes to the other. However, the order of proposals is carefully chosen to ensure an interim stable matching is found. Furthermore, with high probability, the number of interviews conducted by each applicant or position is limited to $O(\log^2 n)$. In many seasonal markets, interactions occur more simultaneously, consisting of an initial interview phase followed by a clearing stage. We present a non-adaptive algorithm for generating a single stage set of in tiered random markets. The algorithm finds an interim stable matching in such markets while assigning no more than $O(\log^3 n)$ interviews to each applicant or position.

  PublisherOA PDFDOI

• From signaling to interviews in random matching markets

  ArXiv.org · 2025-01-24 · 1 citations

  preprintOpen access

  In many two-sided labor markets, interviews are conducted before matches are formed. The growing number of interviews in medical residency markets has increased demand for signaling mechanisms, where applicants send a limited number of signals to communicate interest. We study the role of signaling mechanisms to reduce interviews in centralized random matching markets where initial preferences are refined through interviews. Agents can only match with those they interview. For the market to clear, we focus on perfect interim stability: no pair of agents-even if they never interviewed each other-prefers each other to their assigned partners under their interim preferences. A matching is almost interim stable if it is perfect interim stable after removing a vanishingly small fraction of agents. We analyze signaling mechanisms in random matching markets with $n$ agents where agents on the short side, long side, or both sides signal their top $d$ preferred partners. The interview graph connects pairs where at least one party signaled the other. We reveal a fundamental trade-off between almost and perfect interim stability. For almost interim stability, $d=ω(1)$ signals suffice: short-side signaling is always effective, whereas long-side signaling is effective only when the market is weakly imbalanced, i.e., when any size difference between the two sides becomes negligible as the market grows. For perfect interim stability, at least $d=Ω(\log^2 n)$ signals are necessary, and short-side signaling becomes crucial in any imbalanced market. We establish that truthful signaling is a Bayes-Nash equilibrium and extend our analysis to markets with hierarchical structure. As a technical contribution, we develop a message-passing algorithm that efficiently determines interim stability by leveraging local neighborhood structures.

  PublisherOA PDFDOI

• Dynamic Predictions for Assessing Hard-to-Place Deceased Donor Kidneys

  American Journal of Transplantation · 2025-08-01

  articleSenior author
  PublisherDOI

• 305.3: Insights from refusal patterns for deceased donor kidney offers.

  Transplantation · 2025-12-01 · 1 citations

  articleSenior author
  PublisherDOI

• Insights from Refusal Patterns for Deceased Donor Kidney Offers

  American Journal of Transplantation · 2025-08-01

  articleSenior author
  PublisherDOI

• Insights From Refusal Patterns for Deceased Donor Kidney Offers

  Transplantation · 2025-05-21 · 6 citations

  articleOpen accessSenior author

  BACKGROUND: The likelihood that a deceased donor kidney will be used evolves during the allocation process. Transplant centers can either decline an organ offer for a single patient or for multiple patients at the same time. We hypothesize that refusals for a single patient indicate issues with individual patients, whereas simultaneous refusals for multiple patients indicate issues with organ quality. METHODS: We investigate offer refusal patterns between January 1, 2022, and December 31, 2023, using Organ Procurement and Transplantation Network data. We aggregate refusals at the same timestamp by a center and define a multiple patient refusal as >1 or >5 patients simultaneously refused. We report the refusal codes associated with single and multiple patient refusals and the nonutilization rate after receiving single and multiple patient refusals by cross-clamp. RESULTS: Patient-related refusal reasons are more commonly single patient refusals, whereas organ-related refusal reasons are more commonly multiple patient refusals. Multiple patient refusals before cross-clamp are associated with nonutilization, but single patient refusals are positively correlated with utilization. The nonutilization rate was 28% for organs without pre-clamp refusals, 35% with a single center sending a multiple patient refusal, but only 12% with a single center sending a single patient refusal. CONCLUSIONS: The risk of nonutilization can be assessed early in the offering process based on the number of single and multiple patient refusals received by a specific time (e.g., cross-clamp). Understanding refusal patterns can guide the development of transparent protocols for accelerated placement.

  PublisherDOI

• From Signaling to Interviews in Random Matching Markets

  2025-06-15 · 1 citations

  article
  PublisherDOI

• Organ Procurement Following the Centers for Medicare and Medicaid Services Performance Evaluations

  JAMA Surgery · 2025-11-19 · 2 citations

  articleOpen accessSenior author

  This longitudinal study examines changes in organ procurement organizations’ organ recovery practices following the initial Centers for Medicare and Medicaid Services performance report released in September 2021.

  PublisherOA PDFDOI

• 305.4: Dynamic predictions for assessing hard-to-place deceased donor kidneys.

  Transplantation · 2025-12-01

  articleSenior author
  PublisherDOI

• Transplant surgeons already account for inaccuracies in the Kidney Donor Profile Index (KDPI) calculation

  Clinical Transplantation · 2024-05-01 · 1 citations

  letter

  The data is available upon request from the OPTN: https://optn.transplant.hrsa.gov/data/.

  PublisherDOI

Recent grants

• CAREER: Novel Designs for Kidney Exchange and Other Markets, in the Intersection of Operations Research, Economics and Computer Science

  NSF · $400k · 2013–2016

• CAREER: Novel Designs for Kidney Exchange and Other Markets, in the Intersection of Operations Research, Economics and Computer Science

  NSF · $274k · 2015–2019

• Collaborative Research on Kidney Exchange

  NSF · $293k · 2011–2015

Frequent coauthors

• Alvin E. Roth

  Stanford University

  23 shared

• Ömer Karaduman

  23 shared

• Eduardo M. Azevedo

  University of Pennsylvania

  22 shared

• Clayton R. Featherstone

  Baylor University

  22 shared

• Nikhil Agarwal

  21 shared

• Mark Braverman

  Princeton University

  16 shared

• Afshin Nikzad

  University of Southern California

  13 shared

• Michael A. Rees

  University of Toledo Medical Center

  11 shared

Education

• Ph.D., Management Science and Engineering

  Stanford University

  2008

• M.S., Management Science and Engineering

  Stanford University

  2003

• B.S., Industrial Engineering and Management

  Technion - Israel Institute of Technology

  2001

Awards & honors

• Franz Edelman Laureate
• outstanding paper award in the ACM conference of Electronic…
• NSF-CAREER award (year not specified)
• Lanchester Prize (December 17, 2024)