Tapan Chugh


I am a fifth year PhD student at the University of Washington, where I am advised by Arvind Krishnamurthy and Ratul Mahajan. I am broadly interested in distributed systems and machine learning systems. My recent projects revolve around optimizing resource efficiency of machine learning applications.

My academic journey is complemented by valuable industrial experiences. I am currently engaged as a student researcher at Systems Research @ Google where I work on optimizing accelerator efficiency for ML serving. Previously, I collaborated with Srikanth Kandula and Ishai Menache as a research intern and visitor at Microsoft Research. Before starting my PhD, I spent two years as a Research Fellow at Microsoft Research India working with Muthian Sivathanu.

selected publications


  1. Efficient Direct-Connect Topologies for Collective Communications
    Liangyu Zhao, Siddharth Pal, Tapan Chugh, Weiyang Wang, Jason Fantl, Prithwish Basu, Joud Khoury, and Arvind Krishnamurthy
  2. SoCC
    Anticipatory Resource Allocation for ML Training
    Tapan Chugh, Srikanth Kandula, Arvind Krishnamurthy, Ratul Mahajan, and Ishai Menache
    In Proceedings of the 14th ACM Symposium on Cloud Computing, 2023
    Dremel: Adaptive Configuration Tuning of RocksDB KV-Store
    Chenxingyu Zhao, Tapan Chugh, Jaehong Min, Ming Liu, and Arvind Krishnamurthy
    Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2022
    Gimbal: enabling multi-tenant storage disaggregation on SmartNIC JBOFs
    Jaehong Min, Ming Liu, Tapan Chugh, Chenxingyu Zhao, Andrew Wei, In Hwan Doh, and Arvind Krishnamurthy
    In Proceedings of the 2021 ACM SIGCOMM 2021 Conference, 2021
    Astra: Exploiting predictability to optimize deep learning
    Muthian Sivathanu, Tapan Chugh, Sanjay S Singapuram, and Lidong Zhou
    In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, 2019