Upcoming Events
What makes an algorithm industrial strength?
Friday, Sept. 29, 2023, 1:25 p.m. through Friday, Sept. 29, 2023, 2:25 p.m.
Lind Hall 325 or Zoom
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.
Discrete Curvature and Applications in Graph Machine Learning
Tuesday, Oct. 3, 2023, 1:25 p.m. through Tuesday, Oct. 3, 2023, 2:25 p.m.
Lind Hall 325 and Zoom
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.
Navigating Interdisciplinary Research as a Mathematician
Friday, Oct. 6, 2023, 1:25 p.m. through Friday, Oct. 6, 2023, 2:25 p.m.
Lind Hall 325 or virtually by Zoom
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.
Lecture: Yimin Zhong
Tuesday, Oct. 10, 2023, 1:25 p.m. through Tuesday, Oct. 10, 2023, 2:25 p.m.
Lind Hall 325 and Zoom
Data Science Seminar
Yimin Zhong (Auburn University)
Distinct spatiotemporal tumor-immune ecologies define therapeutic response in NSCLC patients
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.
Lecture: Jiajia Yu
Tuesday, Oct. 17, 2023, 1:25 p.m. through Tuesday, Oct. 17, 2023, 2:25 p.m.
Lind Hall 325 and Zoom
Data Science Seminar
Lecture: Jiajia Yu (Duke University)
Trading off accuracy for reduced computation in scientific computing
Tuesday, Oct. 24, 2023, 1:25 p.m. through Tuesday, Oct. 24, 2023, 2:25 p.m.
Lind Hall 325 or via Zoom
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.
Lecture: Christopher Bemis
Friday, Oct. 27, 2023, 1:25 p.m. through Friday, Oct. 27, 2023, 2:25 p.m.
Lind Hall 325
Industrial Problems Seminar
Christopher Bemis (X Cubed Capital Management)
Lecture: Dongbin Xiu
Tuesday, Oct. 31, 2023, 1:25 p.m. through Tuesday, Oct. 31, 2023, 2:25 p.m.
Lind Hall 325 and Zoom
Data Science Seminar
Dongbin Xiu (The Ohio State University)
Lecture: Parker Williams
Friday, Nov. 3, 2023, 1:25 p.m. through Friday, Nov. 3, 2023, 2:25 p.m.
Lind Hall 325 or Zoom
Industrial Problems Seminar
Parker Williams (Rivian)