Currently, I am an Associate Professor in the Department of Mathematics at the City College and Graduate Center of the City University of New York. Before joining CUNY, I was a Courant Instructor at NYU Math Department. I obtained my bachelor's (B.Stat) and master's (M.Stat) degrees in Statistics from Indian Statistical Institute. I obtained my MS and Ph.D degrees in Operations Research & Information Engineering from Cornell University.

At CUNY and CCNY, I have received

• the Jerome Krase Award for Outstanding Research for Associate Professors, 026-2027,
• the 2026 President’s Award for Excellence,
• the Division of Science Excellence in Faculty Research Award, 2023,
• "Salute to Scholars" recognition, 2016.

So far, my research has been supported by

• multiple U.S. National Science Foundation Division of Mathematical Sciences grants,
• one U.S. Department of Agriculture National Institute for Food and Agriculture grant, and
• one Simons Foundation grant,
• several CUNY internal grants including PSC-CUNY Enhanced (Track 2) research awards.

My research is broadly categorized (with overlaps) into three Groups based on primary focus, aim, and approach.

Statistics and Data Science (Algorithms, Inference Method and Framework Development, Theoretical Analysis, Applications)
Theoretical and empirical analysis of statistical inference procedures, machine learning algorithms, and data science approaches to address inference questions involving complex data-types (including network data, high dimensional data, and complex time series data) and develop efficient inference procedures.

Applied and Theoretical Probability
Mathematical and probabilistic modeling of different biological, social, and physical phenomena and complex structures along with their theoretical analysis, stochastic spatial models, percolation and related models, random graph models, processes and dynamics on random graphs.

Interdisciplinary Research (in collaboration with a wide range of scientists and domain experts in various disciplines including social, bio, and physical sciences)
Developing relevant inference frameworks and methods to address inferential questions arising in various practical areas (including biosciences, social and behavioral sciences, computer science, and physical sciences).