Suvrit Sra is a computer scientist who works in machine learning and optimization. Sra works on several theoretical, algorithmic, and applied questions in machine learning and data science. He is interested in all aspects of optimization for ML, especially scalable convex and nonconvex optimization. Sra is fascinated by geometric optimization, a growing topic with lots of cool math. Beyond OPT & ML, he has a strong interest in matrix theory, differential geometry, metric geometry, probability theory, algebraic combinatorics, fixed-point theory, and several other areas in math.
- Foundations of Machine Learning, Spring 2017. Visiting Scientist.