Upcoming Events

Industrial Problems Seminar 

Thomas Grandine (University of Washington)

Abstract

In this talk, I will discuss the details of two algorithms for parametrizing planar curves in an industrial design context. The first algorithm, developed in an academic setting by world class researchers, solves the problem posed by the researchers in a very satisfying and elegant way. Yet that algorithm, elegant though it may be, turns out to be ineffective in a real world engineering environment. The second algorithm is an extension of the first that eliminates the issues that caused it to be inadequate for industrial use.
 

Data Science Seminar

Melanie Weber (Harvard University)

Abstract

The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of Representation Learning. In this talk, we study this problem from the perspective of Discrete Geometry. We start by reviewing discrete notions of curvature with a focus on discrete Ricci curvature. Then we discuss how curvature is linked to mesoscale structure in graphs, which gives rise to applications in graph machine learning, such as (unsupervised) node clustering. For downstream machine learning and data science applications, it is often beneficial to represent graph-structured data in a continuous space, which may be Euclidean or Non-Euclidean. We show that discrete curvature allows for characterizing the geometry of a suitable embedding space both locally and in the sense of global curvature bounds, which have implications for spectral graph machine learning.

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)