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Kamesh Munagala

Professor of Computer Science
Computer Science
Box 90129, Computer Science Department, Durham, NC 27708-0129
D205, LSRC, Research Drive, Durham, NC 27708

Overview


My research is in the general area of theoretical computer science, particularly the areas of Approximation Algorithms, Online Algorithms, and Computational Economics.  I work on developing models, algorithms, and markets for resource allocation, decision making, and provisioning problems.

Current Duke Appointments & Affiliations


Professor of Computer Science · 2016 - Present Computer Science, Trinity College of Arts & Sciences

Recent Scholarly Works


The Price of Competitive Information Disclosure

Conference Proceedings of the Annual ACM Symposium on Theory of Computing · June 9, 2026 In many decision-making scenarios, individuals strategically choose what information to disclose to optimize their own outcomes. It is unclear whether such strategic information disclosure can lead to good societal outcomes. To address this question, we co ... Full text Cite

Fair Multi-Agent Persuasion with Submodular Constraints

Conference Leibniz International Proceedings in Informatics Lipics · June 1, 2026 We study the problem of selection in the context of Bayesian persuasion. We are given multiple agents with hidden values (or quality scores), to whom resources must be allocated by a welfare-maximizing decision-maker. An intermediary with knowledge of the ... Full text Cite

Balanced Spanning Tree Distributions Have Separation Fairness

Conference Proceedings of the Annual ACM SIAM Symposium on Discrete Algorithms · January 1, 2026 Sampling-based methods such as ReCom are widely used to audit redistricting plans for fairness, with the balanced spanning tree distribution playing a central role since it favors compact, contiguous, and population-balanced districts. However, whether suc ... Full text Cite
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Recent Grants


III: Medium: Responsive Optimization for Algorithmic Decision Systems

ResearchCo-Principal Investigator · Awarded by National Science Foundation · 2024 - 2027

AF: Small: Algorithm and Incentive Design for Modern Resource Allocation Platforms

ResearchPrincipal Investigator · Awarded by National Science Foundation · 2021 - 2025

HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms

ResearchSenior Investigator · Awarded by National Science Foundation · 2019 - 2023

View All Grants

Education


Stanford University · 2003 Ph.D.
Stanford University · 2002 M.S.

External Links


Personal Webpage