Geometry and Topology in Data Science (Spring 2024)

• The Organizers: Alvaro Diaz, Marzieh Eidi, Celia Hacker, Guillermo Restrepo, Daniel Spitz, Diaaeldin Taha, Francesca Tombari
• MPI Seminar Page: Mathematical Methods in Data Science
• Contact: To contact the organizers, email the lab at lab [at] mis [dot] mpg [dot] de.
• Mailing List: To stay informed of Lab activities, including this group's meetings, join the Lab mailing list.

Speaker: Justin Curry (University of Albany, USA)

Coordinates: Fri 10.05, 9–10:30 AM, MiS E1 05

Title: “To Predict is NOT to Explain”

Abstract: Modern day neural networks are amazing prediction machines, but to get at explanations one has to understand higher order relations between data as they fiber over their predictions. In this talk I will connect the urgent questions of modern data science with the distinguished history of applied topology by considering simple geometric examples and probing them with increasingly complicated tools. Ideas from dynamics, stratification theory and sheaf theory will be introduced in a loose and intuitive fashion to trace future directions for research.

CAG Conference - NO MEETING

Speaker: Celia Hacker (Max Planck Institute for Mathematics in the Science, Germany)

Coordinates: Fri 24.05, 9–10:30 AM, G3 10

Title: What are graph neural networks?

Abstract: Graph structured data appears in many different context and with this comes the need for tools to analyse them. One of the most common tools to study data sets of graphs are graph neural networks (GNNs). However, to many of us GNNs remain a black box that magically perform predictions about graphs. In this lecture we will learn about the basics of GNNs, possible generalizations and research directions.

Speaker: Jeff Philips (University of Utah, USA)

Coordinates: Fri 31.05, 9–10:30 AM, MiS G3 10

Title: The Geometry of Kernel Methods and Kernel Range Spaces

Abstract: I will start by overviewing kernel methods in machine learning, and how the simple kernel trick allows one to effortlessly turn intuitive linear methods into non-linear ones. While these methods can seem mysterious, I’ll try to give insight into the geometry that arises, especially in kernel SVM. This will lead into kernel range spaces, which describes all the ways one can inspection a data set with a kernel. From there I will discuss approximation of these with coresets, and just approximating the spaces themselves which leads to surprising results in high dimensions.

Speaker Bio: Jeff Phillips is a Professor in the School of Computing at the University of Utah. He founded the Utah Center for Data Science, and directs the Data Science academic program there. He works on geometric data analysis, algorithms for big data, and how these intersect with data science. His book, Mathematical Foundations for Data Analysis, was published by Springer-Nature in 2021.

Speaker: Simon Telen (Max Planck Institute for Mathematics in the Science, Germany)

Coordinates: Fri 07.06, 9–10:30 AM, MiS E1 05

Title: Chebyshev Varieties

Abstract: Chebyshev varieties are algebraic varieties parametrized by Chebyshev polynomials. They arise naturally when solving polynomial equations expressed in the Chebyshev basis. More precisely, when passing from monomials to Chebyshev polynomials, Chebyshev varieties replace toric varieties. I will introduce these objects, discuss their defining equations and present key properties. Via examples, I will motivate their use in practical computations. This is joint work with Zaïneb Bel-Afia and Chiara Meroni.

Speaker: Bei Wang (University of Utah, USA)

Coordinates: Fri 21.06, 9–10:30 AM, MiS E1 05

Title: Topology-Preserving Data Compression

Abstract: Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data. When performing post hoc analysis with decompressed data using topological methods, preserving topology in the compression process to obtain topologically consistent and correct scientific insights is desirable. In this talk, we will discuss a couple of lossy compression methods that preserve the topological features in 2D and 3D scalar fields. Specifically, we aim to preserve the types and locations of local extrema as well as the level set relations among critical points captured by contour trees in the decompressed data. This talk is based on joint works with Lin Yan, Xin Liang, Hanqi Guo, and Nathan Gorski.

Speaker Bio: Dr. Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member in the Scientific Computing and Imaging (SCI) Institute, University of Utah. She obtained her Ph.D. in Computer Science from Duke University. Her research focuses on topological data analysis, data visualization, and computational topology. She works on combining topological, geometric, statistical, data mining, and machine learning techniques with visualization to study large and complex data for information exploration and scientific discovery. Some of her current research activities involve the analysis and visualization of high-dimensional point clouds, scalar fields, vector fields, tensor fields, networks, and multivariate ensembles. Dr. Phillips is a DOE Early Career Research Program (ECRP) awardee in 2020 and an NSF CAREER awardee in 2022. Her research has been supported by multiple awards from NSF, NIH, and DOE.

ScaDS Summer School - NO MEETING