I am a fifth year grad student at UW-Madison. Advised by Dimitris Papailiopoulos and Shivaram Venkataraman . My research interests are primarily in Systems for Machine Learning, especially around distributed training and inference of ML workloads. During my PhD I have been very fortunate to intern with Bilge Acun at FAIR, Amar Phanishayee at Microsoft Research and Yucheng Low at Apple.
When I am not being a grad student, I can be found racing keelboats on Lake Mendota or alpine skiing in the winters. I also double up as a sailing instructor at the UW-Madison’s Hoofers Sailing club.
Service
Reviewer: ICML ‘23, ICLR ‘23, Neurips ‘22, Neurips ‘21
ERC: MLSys ‘22, Usenix ATC ‘23
Publications
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CHAI: Clustered Head Attention for Efficient LLM Inference.
S Agarwal, B Acun, B Homer, M Elhoushi, Y Lee, S Venkataraman, D Papailiopoulos, C Wu.
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Decoding Speculative Decoding.
M Yan, S Agarwal, S Venkataraman.
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Blox: A Modular Toolkit for Deep Learning Schedulers.
S Agarwal, A Phanishayee, S Venkataraman. Eurosys’24.
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Bagpipe: Accelerating deep recommendation model training.
S Agarwal, C Yan, Z Zhang, S Venkataraman. SOSP’23.
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Cuttlefish: Low-rank Model Training without All The Tuning.
H Wang, S Agarwal, Y Tanaka, E Xing, D Papailiopoulos. MLSys’23.
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Pufferfish: Communication-efficient models at no extra cost.
H Wang, S Agarwal, D Papailiopoulos. MLSys’22.
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On the utility of Gradient compression
S Agarwal, H Wang, S Venkataraman, D Papailiopoulos. MLSys’22.
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Adaptive Gradient Communication via Critical Learning Regime Identification.
S Agarwal, H Wang, K Lee, S Venkataraman, D Papailiopoulos. MLSys’21.
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AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning.
Y Liu, S Agarwal, S Venkataraman.
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Attack of the tails: Yes, you really can backdoor federated learning.
H Wang, K Sreenivasan, S Rajput, H Vishwakarma, S Agarwal, J Sohn, K Lee, D Papailiopoulos.
[paper]