Centers & Initiatives
David Gamarnik is the Nanyang Technological University Professor of Operations Research at the MIT Sloan School of Management.
His research interests include applied probability and stochastic processes with application to queuing theory, theory of random combinatorial structures and algorithms, scheduling, and various business processes, including call centers, manufacturing, and communications networks.
Gamarnik has served as a research staff member at the Department of Mathematical Sciences, IBM Research, where he worked on various projects with industrial applications, including disaster recovery, performance in business processes, call centers, and operational resilience. Gamarnik is a member of the Institute of Mathematical Statistics, Bernoulli Society, INFORMS, and the American Mathematical Society, and serves on the editorial board of both Operations Research and the Annals of Applied Probability. He is the recipient of the 2004 Erlang Prize from the INFORMS Applied Probability Society, as well as two National Science Foundation grants in 2007.
Gamarnik holds a BA in mathematics from New York University and a PhD in operations research from MIT.
"Computing the Partition Function of the Sherrington-Kirkpatrick Model is Hard on Average."
Gamarnik, David, and Eren Kizildag. Annals of Applied Probability. Forthcoming. arXiv Preprint.
"Sparse High-dimensional Linear Regression. Algorithmic Barriers and a Local Search Algorithm."
Gamarnik, David, and Ilias Zadik. Annals of Statistics. Forthcoming. arXiv Preprint.
Gamarnik, David, and Aukosh Jagannath. Annals of Applied Probability Vol. 49, No. 1 (2021): 180-205. arXiv Preprint.
David Gamarnik, Aukosh Jagannath, and Alexander S. Wein. In Proceedings of the 2020 IEEE 61st Annual Symposium on Foundations of Computer Science, Durham, NC: November 2020. arXiv Preprint.
Gamarnik, David. Communications on Pure and Applied Mathematics. Vol. 73, No. 9 (2020): 2043-2048. arXiv Preprint.
Gamarnik, David, John N. Tsitsiklis, and Martin Zubeldia. Annals of Applied Probability Vol. 30, No. 2 (2020): 870-901.