Recent news


  • Welcome to Vlad, new PhD student in SANDS Lab.
  • Congratulations Dr. Ho for a successful PhD thesis defense!
  • Welcome to Mubarak, new post-doc in SANDS Lab.
  • Congratulations Dr. Dethise for a successful PhD thesis defense!

Older news

  • LineFS wins the Best Paper Award at SOSP’21.
  • Congratulations Atal! Rethinking gradient sparsification as total error minimization accepted as spotlight paper (top 3%) at NeurIPS’21.
  • How expressive of an offload does RDMA support? We find that the answer is a whole lot! RedN accepted at NSDI’22.
  • OmniReduce accepted at SIGCOMM’21. Congratulations Jiawei and Elton! Also, we will run a tutorial on Network-Accelerated Distributed Deep Learning at SIGCOMM. Stay tuned.
  • GRACE accepted at ICDCS’21.
  • SIDCo, an efficient gradient compression technique for distributed deep learning accepted at MLSys’21.
  • SwitchML accepted at NSDI’21.
  • Congratulations Arnaud and Ahmed! Two papers accepted at INFOCOM’21.
  • Congratulations Waleed! Assise is accepted at OSDI’20.
  • Bilal gets papers in at VLDB’20 and SoCC’20! In the VLDB paper, we systematically investigate the problem of cloud configuration using black-box optimization methods and uncover how different methods behave with more than 20 workloads. At SoCC, we propose a new method, Vanir, that reins in configuring data analytics clusters formed by multiple distributed systems whose joint optimization is required.
  • We survey popular gradient compression techniques for distributed deep learning and perform a comprehensive comparative evaluation. Read our technical report.
  • Is there a discrepancy between the theory and practice of gradient compression for distributed deep learning? We argue so in our AAAI’20 paper.
  • Our paper on improving how to reason about and explain the behavior of reinforcement learning agents in networking applications accepted at NetAI’19.
  • A paper on the DAIET project published at HotNets’17.

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