Instructor of Applied Mathematics, Mathematics
Andrej Risteski is the Norbert Wiener fellow at the Institute for Data, Systems, and Society (IDSS) and an instructor of Applied Mathematics at MIT. He received his PhD and BSE from Princeton University.
He works in the intersection of machine learning and theoretical computer science, with the primary goal of designing provable and practical algorithms for problems arising in machine learning. Broadly, this includes tasks like clustering, maximum likelihood estimation, inference, and learning generative models. All of these tend to be non-convex in nature and intractable in general. However, in practice, a plethora of heuristics like gradient descent, alternating minimization, convex relaxations, and variational methods work reasonably well.
In his research, Andrej tries to understand realistic conditions under which guarantees of the performance of these algorithms can be proven, as well as devise new, more efficient methods.