Kamesh Munagala

Kamesh Munagala is a Professor of Computer Science at Duke University's Trinity College of Arts & Sciences since 2016. His primary affiliation is with the Computer Science Department, where he conducts research in theoretical computer science with a focus on algorithm design, fairness, and economic mechanisms.

His educational background includes a Ph.D. (2003) and M.S. (2002) from Stanford University. Munagala has maintained a strong research trajectory with continuous publications spanning over two decades.

Munagala's research focuses on theoretical computer science, particularly approximation algorithms, online algorithms, and computational economics. His recent work centers on two main themes: (1) persuading optimizers or learners toward specific objectives through information revelation and pricing, and (2) achieving group fairness based on proportionality and stability in resource allocation contexts. His research addresses practical applications including data network design, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting.

His publication record shows consistent output with multiple high-impact papers in top conferences like STOC, SODA, and ICML. Recent publications (2024-2025) demonstrate his focus on algorithmic fairness, differential privacy, and mechanisms for resource allocation. His work often intersects computer science with economics, game theory, and social choice theory.

• Best Paper Award at WINE '18 for 'A simple mechanism for a budget constrained buyer'
• Best Paper Award at WWW '09 for 'Hybrid keyword search auctions'

Munagala has secured significant research funding including an NSF Medium grant (2024-2027) for 'Responsive Optimization for Algorithmic Decision Systems' and an NSF Small grant (2021-2025) for 'Algorithm and Incentive Design for Modern Resource Allocation Platforms'. His teaching portfolio includes graduate and undergraduate courses in algorithm design, randomized algorithms, and algorithmic game theory, with courses scheduled through Fall 2025.