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His research spans the areas of applied probability and stochastic networks, with applications in queueing theory, performance analysis, random graphs, randomized algorithms, and probabilistic combinatorial optimization. His primary focus is to address fundamental theoretical challenges that arise in large-scale systems, such as data centers and cloud networks, and provide key insights in understanding various trade offs in designing efficient systems. Mukherjee received the Best Student Paper Award at ACM SIGMETRICS 2018 for introducing a stochastic comparison framework to study the impact of underlying network topologies on the performance of load balancing schemes in large-scale systems.
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MATHEMATICS OF OPERATIONS RESEARCHno. 1 (2024): 476-508
CoRR (2023)
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SIGMETRICS '23: Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systemsno. 1 (2023): 71-72
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SIGMETRICS Perform. Evaluation Rev.no. 2 (2023): 21-23
CoRR (2023)
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arxiv(2023)
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