Sam Hopkins studies algorithms and computational complexity. Recent work examines computational complexity and algorithm design for challenging statistical inference problems, especially using techniques from convex programming and the sum of squares method. Other interests include approximation algorithms, hardness of approximation, high-dimensional probability, and statistical physics in computer science.
He is a 2018--2021 Miller Fellow at UC Berkeley. Previously, he obtained a PhD in Computer Science from Cornell University, and a BS in Mathematics and Computer Science from the University of Washington. He is the recipient of a Microsoft PhD Fellowship and an NSF Graduate Research Fellowship.
- Computational Complexity of Statistical Inference, Fall 2021. Workshop Organizer.
- Probability, Geometry, and Computation in High Dimensions, Fall 2020. Visiting Postdoc.
- Foundations of Data Science, Fall 2018. Visiting Postdoc.