Dylan Foster is a PhD candidate in Computer Science at Cornell University, advised by Karthik Sridharan.

His research focuses on theory for machine learning in real-world settings, where data is too large to fit in memory or where other computational constraints are present. He develops provable and efficient algorithms as well mathematical tools for characterizing computational and statistical tradeoffs in such settings, and is currently working on problems in deep learning, online learning and optimization, and bandit learning. Dylan previously received his BS and MS in Electrical Engineering from USC in 2014, and was a recipient of the NDSEG fellowship.