Debankur Mukherjee

Dr. Debankur Mukherjee is the Leo and Louise Benatar Early Career Professor and Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. His research bridges applied probability, stochastic processes, and computer science with applications to data centers, cloud networks, and machine learning.

• Ph.D. in Stochastic Operations Research (Eindhoven University of Technology, 2018)
• M.Stat. in Mathematical Statistics and Probability (Indian Statistical Institute, 2014)
• B.Sc. in Statistics (University of Calcutta, 2012)

His work focuses on load balancing in large-scale systems, stochastic process scaling limits, random graphs, and online sequential decision-making. He develops foundational theories for modern algorithms in cloud networks and data centers, including power-of-d load balancing and smoothed optimization. Google Scholar articles (2025-2023) highlight his contributions to mean-field models, distributed algorithms, and ML-augmented capacity scaling.

Scientific awards include the ACM SIGMETRICS Rising Star Award (2025), Best Paper Award at ACM SIGMETRICS (2023), INFORMS JFIG Paper Competition Finalist (2022), Best Student Paper Award at ACM SIGMETRICS (2018), and Ph.D. thesis cum laude (2018). He serves on editorial boards of Stochastic Systems, Queueing Systems, and Stochastic Models.

Dr. Mukherjee leads NSF-funded projects: $800k CPS grant (2023-2026) on data-driven robustness in dynamic teams and a $439k CIF grant (2021-2024) on cloud network load balancing. He mentors Ph.D. students in probability theory and stochastic processes.

Affiliated with Georgia Tech's Algorithms, Combinatorics and Optimization (ACO), Algorithms, Randomness and Complexity Center (ARC), and Center for Machine Learning (ML@GT), he contributes to conferences like ACM SIGMETRICS, IFIP Performance, and International Teletraffic Congress.