Upcoming Events

Data Science Seminar

Melanie Weber (Harvard University)

Abstract

Matrix-valued optimization tasks arise in many machine learning applications. Often, exploiting non-Euclidean structure in such problems can give rise to algorithms that are computationally superior to standard nonlinear programming approaches. In this talk, we consider the problem of optimizing a function on a (Riemannian) manifold subject to convex constraints. Several classical problems can be phrased as constrained optimization on matrix manifolds. This includes barycenter problems, as well as the computation of Brascamp-Lieb constants. The latter is of central importance in many areas of mathematics and computer science through connections to maximum likelihood estimators in Gaussian models, Tyler’s M-estimator of scatter matrices and operator scaling. We introduce Riemannian Frank-Wolfe methods, a class of first-order methods for solving constrained optimization problems on manifolds and present a global, non-asymptotic convergence analysis. We further discuss a class of CCCP-style algorithms for Riemannian “difference of convex” functions and explore connections to constrained optimization. We complement our discussion with applications to the two problems described above. Based on joint work with Suvrit Sra.

Industrial Problems Seminar

Julie Mitchell (Oak Ridge National Laboratory)

Abstract

Being effective in industrial and team science settings requires the ability to work across disciplines. In this talk, I will reflect on how to be successful working across disciplines and what types of opportunities exist for mathematicians working at national laboratories. I will also reflect on past projects I’ve pursued, which include high-performance computing and machine learning approaches to the understanding of macromolecular structure and binding.

Data Science Seminar

Yimin Zhong (Auburn University)

Industrial Problems Seminar 

Sandhya Prabhakaran (Moffitt Cancer Centre)

Abstract

The talk will be geared towards a general audience. The goal of this talk is to explain importance of data, and the many ways data can be analyzed to benefit patient care. In this talk, I will focus on Non-small cell lung cancer (NSCLC), the patient data we obtained, the computational approaches used, and the potential biomarkers we identified in this process.

Data Science Seminar

Lecture: Jiajia Yu (Duke University)

Data Science Seminar

Alex Gittens (Rensselaer Polytechnic Institute)

Abstract

Classical linear algebraic algorithms guarantee high accuracy in exchange for high computational cost. These costs can be infeasible in modern applications, so over the last two decades, randomized algorithms have been developed that allow a user-specified trade-off between accuracy and computational efficiency when dealing with massive data sets. The intuition is that when dealing with an excess of structured data (e.g., a large matrix which has low numerical rank), one can toss away a large portion of this data, thereby reducing the computational load, without introducing much additional error into the computation. In this talk we look at the design and performance analysis of several numerical linear algebra and machine learning algorithms--- including linear solvers, approximate kernel machines, and tensor low-rank decomposition--- based upon this principle.

Industrial Problems Seminar 

Christopher Bemis (X Cubed Capital Management)

Data Science Seminar

Dongbin Xiu (The Ohio State University)

Industrial Problems Seminar 

Parker Williams (Rivian)

Industrial Problems Seminar

Vasileios Ioannidis (Amazon Search AI)