Chris De Sa is an assistant professor in the department of computer science at Cornell University. He is a member of the Cornell Machine Learning Group. His 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). De Sa's work builds toward using these techniques to construct data analytics and machine learning frameworks, including for deep learning, that are efficient, parallel, and distributed.
Chris De Sa