Chris De Sa
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.
CS 6787 Advanced Machine Learning Systems (Fall 2017)
A Two Pronged Progress in Structured Dense Matrix MultiplicationIn SODA: ACM-SIAM Symposium on Discrete Algorithms (SODA18), January 2018.
Gaussian Quadrature for Kernel Features SpotlightIn NIPS: Proceedings of the 30th Neural Information Processing Systems Conference, December 2017.
Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient DescentIn ISCA: 44th International Symposium on Computer Architecture, June 2017.
Flipper: A Systematic Approach to Debugging Training SetsIn HILDA: Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, May 2017.
Data Programming: Creating Large Training Sets, QuicklyIn NIPS: Proceedings of the 29th Neural Information Processing Systems Conference, December 2016.