TAG in Machine Learning

A Workshop at the 39th International Conference on Machine Learning (ICML 2022), Baltimore, MD, July 22, 2022

Call for Papers

Much of the data that is fueling current rapid advances in machine learning is: high dimensional, structurally complex, and strongly nonlinear. This poses challenges for researcher intuition when they ask (i) how and why current algorithms work and (ii) what tools will lead to the next big break-though. Mathematicians working in topology, algebra, and geometry have more than a hundred years worth of finely-developed machinery whose purpose is to give structure to, help build intuition about, and generally better understand spaces and structures beyond those that we can naturally understand. This workshop will show-case work which brings methods from topology, algebra, and geometry and uses them to help answer challenging questions in machine learning. With this workshop we will create a vehicle for disseminating machine learning techniques that utilize rich mathematics and address core challenges described in the ICML call for papers. Additionally, this workshop creates opportunity for presentation of approaches which may address critical, domain-specific ML challenges but do not necessarily demonstrate improved performance on mainstream, data-rich benchmarks. To this end our proposed workshop will open up ICML to new researchers who in the past were not able to discuss their novel but data set-dependent analysis methods. We interpret topology, algebra, and geometry broadly and welcome submissions ranging from manifold methods to optimal transport to topological data analysis to mathematically informed deep learning. Through intellectual cross-pollination between data-driven and mathematically-inspired communities we believe this workshop will support the continued development of both groups and enable new solutions to problems in machine learning. All papers accepted for inclusion in the workshop are eligible for inclusion in the PMLR Volume entitled "Topology, Algebra, and Geometry in Learning."

Topic areas of include but are not limited to:

  • Geometric Machine/Deep Learning

  • Optimal Transport

  • Topological Data Analysis

  • Mathematical Machine Learning

  • Graph-based Methods

  • Manifold Methods

  • Abstract Algebra in Machine/ Deep Learning

Important Dates:

Paper Submission Deadline: May 16, 2022 (Anywhere on Earth) May 19, 2022 (Anywhere on Earth)

Final Decisions to Authors: June 6, 2022 (Anywhere on Earth)

Camera-Ready Deadline (required for inclusion in proceedings): June 16, 2022 (Anywhere on Earth)

Main Conference: July 17-23, 2021

Workshop Date: July 22, 2022

Workshop Location: Baltimore Convention Center Rooms 318-320

Paper Length and Format

The paper submission must be at most 6 pages in length (excluding references and supplementary materials) and double blind. We will be following the ICML general conference submission criteria for papers - for details please see: ICML Call For Papers. As a note the reviewers will not be required to review the supplementary materials so make sure that your paper is self-contained.


Submission Site:



Dr. Michael Kirby

Colorado State University

Michael Kirby received the SB degree in mathematics from the Massachusetts Institute of Technology and PhD degree from the Division of Applied Mathematics, Brown University. He is currently a professor with the Department of Mathematics, Colorado State University with a joint appointment in the Department of Computer Science. His research interests include low dimensional modeling, geometric models for data and optimization. He authored the textbook Geometric Data Analysis. He was an Alexander von Humboldt fellow at the Institute for Information Verarbeitung, University of Tuebingen, Germany. He also was awarded an IBM Faculty fellowship, and the College of Natural Sciences Award for Graduate Education. He is a member of the IEEE.

Dr. Bastian Rieck

AIDOS Lab, Institute of AI for Health, Helmholtz Zentrum München

Bastian is the principal investigator of the AIDOS Lab at the Institute of AI for Health at Helmholtz Munich, Germany. His main research interests are developing multi-scale algorithms for analysing complex data sets, with a focus on biomedical applications and healthcare topics. Bastian is also enticed by finding new ways to explain neural networks using concepts from algebraic and differential topology. He is a big proponent of scientific outreach and enjoys blogging about his research, academia, supervision, and software development. Bastian received his M.Sc. degree in mathematics, as well as his Ph.D. in computer science, from Heidelberg University in Germany.

Dr. Shubhendu Trivedi

Shubhendu Trivedi's current research focuses on developing theoretical and methodological tools to incorporate geometric structure into machine learning models, employing statistical physics based approaches for neural network analysis, and developing conformal prediction methods for theoretically-grounded uncertainty quantification. Shubhendu received his PhD in 2018 for work on group equivariant neural networks, working at the University of Chicago and the Toyota Technological Institute; a MS from TTI-C for work in Computer Vision; a MS from Worcester Polytechnic for work on the Szemeredi Regularity Lemma, and a BE in Electrical Engineering. Shubhendu has been a research associate at MIT and an NSF Institute Fellow in Computational Mathematics at Brown University working on problems in algebraic machine learning. Apart from academic research, Shubhendu has led multiple teams for industrial research on health analytics, equivariant models for relational data, knowledge graph engineering and zero-shot transfer learning. He has also held positions at Rutgers, ZS, NEC Labs America amongst others, and has been associated with a semi-conductors startup.

Dr. Soledad Villar

Johns Hopkins University

Soledad Villar is an Assistant Professor at the Department of Applied Mathematics & Statistics, and at the Mathematical Institute for Data Science, Johns Hopkins University. She received her PhD in mathematics from University in Texas at Austin and was a research fellow at New York University as well as the Simons Institute in University of California Berkeley. Her mathematical interests are in computational methods for extracting information from data. She studies optimization for data science, machine learning, equivariant representation learning and graph neural networks. Soledad is originally from Uruguay.


... coming soon ...


Dr. Tegan Emerson

Pacific Northwest National Laboratory

Colorado State University

University of Texas, El Paso

Tegan Emerson received her PhD in Mathematics from Colorado State University. She was a Jerome and Isabella Karle Distinguished Scholar Fellow in optical sciences at the Naval Research Laboratory from 2017-2019. In 2014 she had the honor of being a member of the American delegation at the Heidelberg Laureate Forum. Dr. Emerson is now a Senior Data Scientist and Team Leader in the Data Sciences and Analytics Group at Pacific Northwest Laboratory. In addition to her role at Pacific Northwest National Laboratories, Dr. Emerson also holds Joint Appointments as Affiliate Faculty in the Departments of Mathematics at Colorado State University and the University of Texas, El Paso. Her research interests include geometric and topological data analysis, dimensionality reduction, algorithms for image processing and materials science, deep learning, and optimization.

Dr. Henry Kvinge

Pacific Northwest National Laboratory

University of Washington

Henry Kvinge received his PhD in Mathematics from UC Davis where his research focused on the intersection of representation theory, algebraic combinatorics, and category theory. After two years as a postdoc in the Department of Mathematics at Colorado State University where he worked on the compressive sensing-based algorithms underlying single-pixel cameras, he joined PNNL as a senior data scientist. These days his work focuses on leveraging ideas from geometry, and representation theory to build more robust and adaptive deep learning models and frameworks.

Dr. Tim Doster

Pacific Northwest National Laboratory

Tim Doster is a Senior Data Scientist at the Pacific Northwest National Laboratory. He received the B.S. degree in computational mathematics from the Rochester Institute of Technology in 2008 and the Ph.D. degree in applied mathematics and scientific computing from the University of Maryland, College Park, in 2014. From 2014 to 2016, he was a Jerome and Isabella Karle Distinguished Scholar Fellow before becoming a Permanent Research Scientist in the Applied Optics division with the U.S. Naval Research Laboratory. During his time with the U.S. Naval Research Laboratory he won the prestigious DoD Laboratory University Collaboration Initiative (LUCI) grant. His research interests include machine learning, harmonic analysis, manifold learning, remote sensing, few-shot learning, and adversarial machine learning.

Dr. Sarah Tymochko

Michigan State University

Sarah Tymochko received her Ph.D. in Computational Mathematics, Science, and Engineering at Michigan State University. Her dissertation research focused on topological tools for time series analysis. Beginning summer 2022, she will be a Hedrick assistant adjunct professor at University of California, Los Angeles in the Department of Mathematics. Her research interests include topological data analysis, dynamical systems, time series analysis, network science, and machine learning.

Dr. Alex Cloninger

University of California. San Diego

Alex Cloninger is an Associate Professor in Mathematics and the Halıcıoğlu Data Science Institute at UC San Diego. He received his PhD in Applied Mathematics and Scientific Computation from the University of Maryland in 2014, and was then an NSF Postdoc and Gibbs Assistant Professor of Mathematics at Yale University until 2017, when he joined UCSD. Alex researches problems in the area of geometric data analysis and applied harmonic analysis. He focuses on approaches that model the data as being locally lower dimensional, including data concentrated near manifolds or subspaces. These types of problems arise in a number of scientific disciplines, including imaging, medicine, and artificial intelligence, and the techniques developed relate to a number of machine learning and statistical algorithms, including deep learning, network analysis, and measuring distances between probability distributions.