Michael Dinitz

Michael Dinitz is an Associate Professor in the Department of Computer Science at Johns Hopkins University with a secondary appointment in the Department of Applied Mathematics and Statistics. He is a member of the Algorithms and Complexity group and the Mathematical Institute for Data Science.

His research focuses on Theoretical Computer Science, with emphasis on approximation algorithms, online algorithms, and distributed algorithms, particularly in applications to computer networking, distributed systems, and machine learning. His recent work involves differential privacy, dynamic networks, and resilient network design.

His publications cover diverse topics in graph theory, privacy-preserving algorithms, and online optimization, with a focus on practical applications of theoretical results. He has received multiple NSF grants including a CRII award and Algorithms in the Field funding, and was honored with the Professor Joel Dean Excellence in Teaching Award. He advises a research group with PhD, MSE, and undergraduate students, and serves on program committees for leading theory conferences like FOCS, STOC, and ICALP.

Education

• PhD in Computer Science, Carnegie Mellon University (2010), advised by Anupam Gupta
• AB in Computer Science, Princeton University (2005), advised by Sanjeev Arora

Grants & Awards

• NSF CRII Grant (2015)
• NSF Algorithms in the Field Grant (2016)
• Multiple NSF Algorithmic Foundations grants (2019, 2022, 2025)
• NSF Graduate Research Fellowship (2005-2010)
• ARCS Foundation Scholarship (2005-2010)
• Best Paper Awards at ICDCS (2014) and DISC (2017)