Dr. Chris De Sa

Biography

Chris De Sa is an Associate Professor in the Department of Computer Science at Cornell University and a member of the Cornell Machine Learning Group. He leads the Relax ML Lab, focusing on the development of algorithmic, software, and hardware techniques for high-performance machine learning. His research emphasizes advanced methods involving relaxed-consistency variants of stochastic algorithms, asynchronous low-precision stochastic gradient descent, and Markov chain Monte Carlo techniques. With a strong emphasis on constructing data analytics frameworks, Chris integrates deep learning with efficient, parallel, and distributed algorithms. He graduated from Stanford University in 2017, where he was advised by Kunle Olukotun and Chris Ré. Chris has received various accolades, including the Student Paper Award Honorable Mention at AAAI-2024 for his work on fair classification benchmarks and a DARPA Young Faculty Award for his research on decentralized online parameter-efficient fine-tuning of compressed models. Additionally, he has contributed significantly to the AI and ML community as a keynote speaker and program chair for major conferences. Chris’s dedication to teaching is evident in his courses on machine learning applications in plant science and his awards for excellence in teaching. He actively supervises several PhD students in computer science and related fields, contributing to the next generation of researchers in machine learning.

Research Interests

Experience