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Chris De Sa is an Associate Professor in the Department of Computer Science at Cornell University, where he leads the Relax ML Lab and is a member of the Cornell Machine Learning Group. His research focuses on high-performance machine learning, particularly the development of algorithmic, software, and hardware techniques. He explores relaxed-consistency variants of stochastic algorithms and asynchronous low-precision stochastic gradient descent (SGD). De Sa's work integrates advanced techniques to construct data analytics and machine learning frameworks, emphasizing efficiency and scalability in deep learning and distributed systems. A graduate of Stanford University, he has also contributed to various workshops and conferences, receiving several awards for his research and teaching effectiveness, including the National Science Foundation CAREER award and the DARPA Young Faculty Award. He actively mentors PhD students in diverse machine learning domains, reinforcing Cornell's emphasis on practical applications of machine learning in fields such as digital agriculture and plant science.
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