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.

I will be graduating this year, and am applying for academic jobs in the 2016-2017 cycle.

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