Machine Learning: Theory and Algorithms

The goal of Machine Learning Theory is to understand fundamental principles and capabilities of learning from data, as well as designing and analyzing machine learning algorithms. We invite you to the Machine Learning Theory Session of CSL student conference if you are curious about when, how, and why machine learning algorithms work.

The session consists of a keynote speech followed by several student talks in which students present their current research. Besides the theoretical aspects of machine learning, this session covers topics including (but not limited to) statistical inference, algorithms, graphical models, signal processing, etc.

Confirmed Speakers

Keynote Speaker: Aleksander Madry – MIT

Talk Title: Coming Soon
Time and Place: Wednesday Feb. 6, 9am-10am / TBA

Summary – Coming Soon
Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL. Aleksander’s research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and 2018 Presburger Award.

Invited Student Speaker

Talk title
time and place


UIUC Speakers