Chris De Sa

I am a sixth-year PhD student in the Electrical Engineering department at Stanford University. I am co-advised by Kunle Olukotun and by Chris R‌é in the Pervasive Parallelism Laboratory. 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 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 hold a BS and MS in Electrical Engineering, both from Stanford University.

In the fall, I will be joining the Cornell computer science department as an assistant professor.

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