Debankur Mukherjee

Leo and Louise Benatar Early Career Professor and
Associate Professor

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  • Debankur Mukherjee Google Scholar

Education

  • Ph.D. (2018), Eindhoven University of Technology
  • M.Stat. (2014), Indian Statistical Institute
  • B.Sc. (2012), University of Calcutta

About

Debankur Mukherjee is the Leo and Louise Benatar Early Career Professor and an Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. Before joining Georgia Tech in 2019, he was a Prager Assistant Professor in the Division of Applied Mathematics at Brown University. Debankur received his Ph.D. in Stochastic Operations Research from the Eindhoven University of Technology in the Netherlands. His research spans the area of applied probability, at the interface of stochastic processes and computer science, with applications to performance analysis, online algorithms, and machine learning. His primary focus is to develop a foundational understanding of the challenges that arise in large-scale systems, such as data centers and cloud networks. 

Debankur’s work has received several recognitions, including the Best Paper Award at ACM SIGMETRICS 2023, the Best Student Paper Award at ACM SIGMETRICS 2018, and was a finalist in the INFORMS JFIG paper competition in 2022. In 2025, he received the ACM SIGMETRICS Rising Star Award and serves as a cluster co-chair of the INFORMS Applied Probability Society. His research has been supported by the NSF, and he currently serves on the editorial boards of Stochastic Systems, Queueing Systems (QUESTA), and Stochastic Models.

Research

  • Load balancing in large-scale systems, such as, data centers and cloud networks

  • Online sequential decision making and learning

  • Performance analysis of data centers and cloud networks

  • Stochastic processes scaling limits

  • Randomized algorithms on networks

Teaching

Dr. Mukherjee teaches probability, stochastic processes, and sequential decision-making. He emphasizes analytical modeling, mathematical rigor, and algorithmic thinking, connecting theory to applications through carefully structured lectures, illustrative examples, and problem-driven learning.

Representative Publications

  • Zhisheng Zhao, Debankur Mukherjee “Optimal Rate-Matrix Pruning For Large-Scale Heterogeneous Systems”, Queueing Systems: Theory and Applications, 2026 accepted.
  • Bihan Chatterjee, Siva Theja Maguluri, Debankur Mukherjee “Higher-Order Approximations of Sojourn Times in M/G/1 Queues via Stein’s Method”, Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) , 2025 accepted.
  • Zhisheng Zhao, Sayan Banerjee, Debankur Mukherjee “Many-server Asymptotics for Join-the-Shortest Queue in the Super-Halfin-Whitt Scaling Window”, Mathematics of Operations Research, 2025 accepted.
  • Daan Rutten, Debankur Mukherjee, “‘Mean-field Analysis for Load Balancing on Spatial Graphs”, in Annals of Applied Probability, 2024, Vol. 34, No. 6, Pages 5228-5257.
  • Zhisheng Zhao, Debankur Mukherjee, Ruoyu Wu, “Exploiting Data Locality to Improve Performance of Heterogeneous Server Clusters”, in Stochastic Systems, 2024, Vol. 14, No. 3, Pages 229-361.
  • Souvik Dhara, Debankur Mukherjee, Kavita Ramanan, “On r-to-p norms of random matrices with nonnegative entries: Asymptotic normality and ℓ∞-bounds for the maximizer,” in Annals of Applied Probability, 2024, Vol. 34, No. 6, 5076-5115.
  • Daan Rutten, Debankur Mukherjee, “A New Approach to Capacity Scaling Augmented With Unreliable Machine Learning Predictions”, in Mathematics of Operations Research, 2024, Vol. 49, No. 1, Pages 476–508.
  • Daan Rutten, Debankur Mukherjee, “Load balancing under strict compatibility constraints”, Mathematics of Operations Research, 2023, Vol. 48, No. 1, Pages 227–256.
  • Daan Rutten, Nicolas Christianson, Debankur Mukherjee, Adam Wierman, “Online Optimization with Unreliable Machine Learning Predictions”, Proceedings of the ACM on Measurement and Analysis of Computing Systems 2023, Vol. 7, No. 1, Article 12.
  • Mark van der Boor, Sem Borst, Johan van Leeuwaarden, Debankur Mukherjee, “Scalable load balancing in networked systems: A survey of recent advances”, SIAM Review 2022, Vol. 64, No. 3, Pages 554–622.
  • Debankur Mukherjee, “Rates of Convergence of the Join the Shortest Queue Policy for Large-System Heavy Traffic” (short paper), Queueing Systems, 2022, Vol. 100 No. 3–4, Pages 317–319.
  • Diego Goldsztajn, Sem Borst, Johan van Leeuwaarden, Debankur Mukherjee, and Philip Whiting, “Self-learning threshold-based load balancing”, INFORMS Journal on Computing, 2022, Vol. 34, No. 4, Pages 39–54.
  • Sayan Banerjee, Debankur Mukherjee, “Join-the-Shortest Queue Diffusion Limit in Halfin-Whitt Regime: Sensitivity on the Heavy-traffic Parameter,” Annals of Applied Probability 2020, Vol. 30, No. 1, Pages 80–144.
  • Debankur Mukherjee, Sem Borst, Johan van Leeuwaarden, Philip Whiting, “Asymptotic Optimality of Power-of-d Load Balancing in Large-Scale Systems”, Mathematics of Operations Research 2020, Vol. 45, No. 4, Pages 1193–1620.
  • Debankur Mukherjee, Alexander Stolyar, “Joint-the-Idle Queue with Service Elasticity and Infinite Buffers: Large-Scale Asymptotics,” Stochastic Systems 2019, Vol. 9, No. 4, Pages 338–358.
  • Amarjit Budhiraja, Debankur Mukherjee, Ruoyu Wu, “Supermarket Model on Graphs,” Annals of Applied Probability 2019, Vol. 29, No. 3, Pages 1740–1777.
  • Sayan Banerjee, Debankur Mukherjee, “Join-the-Shortest Queue Diffusion Limit: Tail Asymptotics and Scaling of Extrema,” Annals of Applied Probability 2019, Vol. 29, No. 2, Pages 1262–1309.
  • Debankur Mukherjee, Sem Borst, Johan van Leeuwaarden, Philip Whiting, “Universality of Power-of-d Load Balancing in Many-Server Systems,” Stochastic Systems 2018, Vol. 8, No. 4, Pages 265–292.
  • Souvik Dhara, Johan van Leeuwaarden, Debankur Mukherjee, “Corrected Mean-field Model for Random Sequential Adsorption on Random Geometric Graphs,” Journal of Statistical Physics 2018, Vol. 173, No. 3-4, Pages 872–894.
  • Debankur Mukherjee, Sem Borst, Johan van Leeuwaarden, “Asymptotically Optimal Load Balancing Topologies,” Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) 2018, Vol. 2, No. 1, Article 14.
  • Mark de Berg, Bart Jansen, Debankur Mukherjee, “Independent-Set Reconfiguration Thresholds of Hereditary Graph Classes,” Discrete Applied Mathematics, 2018, Vol. 250, Pages 165–182.
  • Souvik Dhara, Debankur Mukherjee, Subhabrata Sen, “Phase Transitions of Extremal Cuts for the Configuration Model,” Electronic Journal of Probability 2017, Vol. 22 No. 1.
  • Debankur Mukherjee, Souvik Dhara, Sem Borst, Johan van Leeuwaarden, “Optimal Service Elasticity in Large-Scale Distributed Systems,” Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) 2017, Vol. 1, No. 1, Article 25.
  • Souvik Dhara, Johan van Leeuwaarden, Debankur Mukherjee, “Generalized Random Sequential Adsorption on Erd˝ os-R´ enyi Random Graphs,” Journal of Statistical Physics 2016, Vol. 164 No. 5, Pages 1217–1232.
  • Debankur Mukherjee, Sem Borst, Johan van Leeuwaarden, Philip Whiting, “Universality of Load Balancing Schemes on the Diffusion Scale,” Journal of Applied Probability 2016, Vol. 53 No. 4, Pages 1111–1124.