Naya Banerjee

Naya Banerjee is an Associate Professor in the Department of Mathematical Sciences at the University of Delaware, part of the College of Arts & Sciences. Previously, she served as an Assistant Professor at the same university (2012–2016), held adjunct roles at UC Berkeley (2011–2012), and conducted postdoctoral research at the Hebrew University of Jerusalem and UC Berkeley. She earned her PhD in Algorithms, Combinatorics, and Optimization from Georgia Tech, advised by Dana Randall and Eric Vigoda.

Her research focuses on probability theory and combinatorics, with applications to statistical physics, theoretical computer science, and statistics. Key areas include mixing times of Markov chains, random walks, random permutations, and Gibbs measures on trees and random graphs. Her work bridges foundational mathematical theory with computational challenges in sampling and optimization.

Banerjee’s publications span topics such as cutoff phenomena in transpositions, simulated tempering algorithms, and phase transitions in mean-field models. Her interdisciplinary research occasionally intersects with public health, as seen in a 2020 study on antibiotic prescribing behaviors in India. While no formal awards are listed, her contributions reflect a strong commitment to advancing probabilistic methods in discrete mathematics.

Her academic journey includes roles at prestigious institutions, and her research has been supported through various academic appointments. She advises students in theoretical mathematics and collaborates on projects at the intersection of combinatorics and computer science.