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Chris De Sa
Biography
I am an Assistant Professor in the Computer Science department at Cornell University. My research interests include algorithmic, software, and hardware techniques for high-performance machine learning, with a focus on relaxed-consistency variants of stochastic algorithms such as asynchronous and low-precision stochastic gradient descent (SGD). My work builds towards using these techniques to construct data analytics and machine learning frameworks, including for deep learning, that are efficient, parallel, and distributed.
I graduated from Stanford University in 2017, where I was advised by Kunle Olukotun and by Chris Ré.
Teaching
CS 6787 Advanced Machine Learning Systems (Fall 2017)
Publications
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A Two Pronged Progress in Structured Dense Matrix MultiplicationIn SODA: ACM-SIAM Symposium on Discrete Algorithms (SODA18), January 2018.
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Gaussian Quadrature for Kernel Features SpotlightIn NIPS: Proceedings of the 30th Neural Information Processing Systems Conference, December 2017.
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Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient DescentIn ISCA: 44th International Symposium on Computer Architecture, June 2017.
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Flipper: A Systematic Approach to Debugging Training SetsIn HILDA: Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, May 2017.
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Data Programming: Creating Large Training Sets, QuicklyIn NIPS: Proceedings of the 29th Neural Information Processing Systems Conference, December 2016.