David Gamarnik

Professor of Operations Research

MIT Sloan School of Management

                   

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Research Interests

Discrete probability and random structures

Algorithms and combinatorial optimization

Statistics and machine learning

Quantum computing and quantum information science

Stochastic processes and queueing theory

 

Contact: email lastname AT mit.edu, Tel 617-253-7779, 100 Main street, Cambridge MA 02139

Education

Employment history and recognition. I was a research staff member in IBM T.J.Watson research center from 1997 till 2005 when I joined MIT as a faculty. I am a fellow of the American Mathematical Society (AMS), the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute for Mathematical Statistics (IMS)

 

Biography. I am a Nanyang Technological University Professor of Operations Research at the Operations Research and Statistics Group, Sloan School of Management of Massachusetts Institute of Technology. I received B.A. in mathematics from New York University in 1993 and Ph.D. in Operations Research from MIT in 1998. Since then I was a research staff member of IBM T.J. Watson Research Center, before joining MIT in 2005.

My research interests include discrete probability, optimization and algorithms, quantum computing, statistics and machine learning, stochastic processes and queueing theory. I am a fellow of the American Mathematical Society, the Institute for Mathematical Statistics and the Institute for Operations Research and Management Science. I am a recipient of the Erlang Prize and the Best Publication Award from the Applied Probability Society of INFORMS, and was a finalist in Franz Edelman Prize competition of INFORMS. I have co-authored a textbook on queueing theory.  Currently I serve as an area editor for the Mathematics of Operations Research journal. In the past I have served as an area editor of the Operations Research journal, and as an associate editor of the Mathematics of Operations Research, the Annals of Applied Probability, Queueing Systems and the Stochastic Systems journals.

 

 

Awards

CV

Books. Queueing Theory: Classical and Modern Methods, with D. Bertsimas. Available on Amazon and Dynamic Ideas (publisher)

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Publications: for most up to date publications see Google Scholar and  arXiv

Recent talks and tutorials with videos

·      Turing in the Shadows of Nobel and Able: An algorithmic Story behind Two Recent Prizes, Plenary lecture at Applied Probability Society of INFORMS, 2025.

·      Geometric Barriers to Classical and Quantum Computing in Random Structures, tutorial at “Towards a theory for typical-case algorithmic hardness”, Les Houches , 2025

·      Turing in the Shadows of Nobel and Able: An algorithmic Story behind Two Recent Prizes, Rejewski, Różycki, Zygalski lecture 2024, Poznan. List of earlier lectures is here

·      Overlap Gap Property: A topological barrier to optimizing over classical and quantum random structures, tutorial at Allerton Conference on Communication, Control and Computing, 2024

·      Overlap gap property: An algorithmic barrier to optimization in random structures and statistical physics, plenary at Randomness and Learning on Networks, Institute of Pure and Applied Mathematics, Brazil, 2024

·      Geometric Barriers to Classical and Quantum Computing in Random Structures, 9th Polish Combinatorial Conference, Poznan, Poland.

·      Summer school of Graph Limits, Groups and Stochastic Processes, Renyi Institute, Budapest, Hungary, 2014

·      Statistical Physics of Random Structures and Algorithms, Plenary lecture at 15th Conference on Random Structures and Algorithms (RSA), 2011

 

 

 

Tutorials and Survey Articles

·      Turing in the shadows of Nobel and Abel: an algorithmic story behind two recent prizes, Notices of the American Mathematical Society, April-May, 2025.

·      Disordered systems insights on computational hardness, Journal of Statistical Mechanics: Theory and Experiment, Volume 2022, November 2022

·      The overlap gap property: A topological barrier to optimizing over random structures. Proceedings of National Academy of Science, October 1, 2021. An MIT News Article about this paper is here Tackling hard computational problems, by Steve Nadis. Here is also Networks Pages article about this work Is it easier to find half a needle than a full needle in a random network?

·      Correlation Decay Method for Decision, Optimization and Inference in Large Scale Networks, TutORials in Operations Research, INFORMS, 2013.

·      Fluid Models of Queueing Networks, Wiley Encyclopedia of Operations Research and Management Science, 2010.