News
Recent news
2023
- 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.
