I am a postdoctoral researcher at the
MIT Institute for Foundations of Data Science.
I was previously (2014-2018) a PhD student in the Department of Computer Science at Cornell University advised by Karthik Sridharan. I received my BS and MS in electrical engineering from the University of Southern California in 2014.
I work on theory for machine learning.
I am interested in all aspects of generalization, sample complexity, and related algorithmic problems, especially for real-world problems such as interactive learning, deep learning, and non-convex learning. Specific topics include
interactive learning (contextual bandits, reinforcement learning,...).
statistical learning, especially agnostic learning.
online learning and sequential prediction.
adaptivity and instance dependence.
concentration inequalities and empirical process theory.
Jayadev Acharya, Christopher De Sa, Dylan J. Foster, and Karthik Sridharan.
Best spotlight talk award, 13th Annual New York Academy of Science ML Symposium.
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan,
and Blake Woodworth.
Dylan J. Foster and Vasilis Syrgkanis.
Dylan J. Foster
Ph.D. Thesis. Department of Computer Science, Cornell University, 2019.
Machine Learning Theory
Cornell University, Spring 2018.
Professor Karthik Sridharan.
Introduction to Analysis of Algorithms
Cornell University, Spring 2015.
Professors Éva Tardos and David Steurer.
Received outstanding teaching award.
Foundations of Artificial Intelligence
Cornell University, Fall 2014.
Professor Bart Selman.
I can be reached at dylanf at mit dot edu. My office is E17-481 in IDSS.
© Dylan Foster 2015.