2nd Annual TAG in Machine Learning

A Workshop at the 40th International Conference on Machine Learning , Honolulu, HI, July 28th, 2023 in Room 317B

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. Following on the success of the first TAG-ML workshop in 2022, this workshop will showcase work which brings methods from topology, algebra, and geometry and uses them to help answer challenging questions in machine learning. Topics include mathematical machine learning, explainability, training schemes, novel algorithms, performance metrics, and performance guarantees. All accepted full length papers will be included in an associated PMLR volume.  We are also offering the option of submitting non-archival extended abstracts.  

Topics Covered

Important Dates:

Paper Submission Deadline: May 8 May 24, 2023 (AOE)

Final Decisions to Authors: June 12 June 16, 2023 (AOE)

(NEW) Extended Abstracts: June 9, 2023 (AOE)

Camera-Ready Deadline (required for inclusion in proceedings): June 19 June 23, 2023 (AOE)

Main Conference: July 23-29, 2023

Workshop Date: July 28, 2023 

Workshop Location: Hawaii Convention Center, Room 317B

Paper Length and Format

The full paper submission must be at most 8 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.  For the extended non-archieval abstracts please use the same template but limit the submission to 4 pages inclusive of references.  There will be an option on the submission site to differentiate full papers and extended abstracts.  


Submission Site

Call for Papers Poster

(NEW) Topological Deep Learning Challenge

The purpose of this challenge is to foster reproducible research in Topological Deep Learning by crowdsourcing the open-source implementation of neural networks on topological domains. Participants are asked to contribute code for a previously existing Topological Neural Network (TNN) and train it on a benchmark dataset.

Implementations are built using TopoModelX, a Python package for deep learning on topological domains. Each submission takes the form of a Pull Request to TopoModelX containing the necessary code for implementing a TNN from the literature. The implementation leverages the coding infrastructure and building blocks from TopoModelX.

Please see the challenge website for more information: https://pyt-team.github.io/topomodelx/challenge/index.html - the deadline for submissions is July 13th, 20023 at 16:59 PST.




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.

Bastian Rieck

Helmholtz Munich

Bastian Rieck, M.Sc., Ph.D. is the Principal Investigator of the AIDOS Lab at the Institute of AI for Health at Helmholtz Munich, focusing on topology-driven machine learning methods in biomedicine. Bastian is also a faculty member of TUM, the Technical University of Munich, and a member of ELLIS, the European Laboratory for Learning and Intelligent Systems. Wearing yet another hat, he serves as the co-director of the Applied Algebraic Topology Research Network. Bastian received his M.Sc. degree in mathematics, as well as his Ph.D. in computer science, from Heidelberg University in Germany. He is a big proponent of scientific outreach and enjoys blogging about his research, academia in general, and software development.

Nina Miolane

University of California, Santa Barbara

Nina Miolane is an Assistant Professor at the University of California, Santa Barbara where she directs the BioShape Lab. Her research investigates the hidden geometries of life: how the shapes of neuronal activity, proteins, cells, and organs relate to their healthy and pathological biological functions. Her team co-develops Geomstats, an open-source software for differential geometry and machine learning. Prof. Miolane graduated from Ecole Polytechnique and Imperial College, received her Ph.D. from Inria, was a postdoctoral fellow at Stanford and a former software engineer. Research fundings include a NIH R01 grant, the NSF SCALE MoDL, Google Season of Codes, and the Noyce Initiative UC Partnerships in Computational Transformation Program grant. Prof Miolane was the recipient of the France-Stanford Award for Applied Science, the L'Oréal-Unesco for Women in Science Award and co-winner of the C3.aigrand Covid-19 challenge.

Sophia Sanborn

University of California, Santa Barbara

Sophia Sanborn is a Postdoctoral Scholar in the Department of Electrical and Computer Engineering at UC Santa Barbara. Her research lies at the intersection of applied mathematics, machine learning, and computational neuroscience. In her work, Dr. Sanborn uses methods from group theory and differential geometry to model neural representations in biology and construct artificial neural networks that reflect and respect the symmetries and geometry of the natural world. She received her Ph.D. in 2021 from UC Berkeley in the Redwood Center for Theoretical Neuroscience and is the recipient of the Beinecke Scholarship, the NSF GRFP, and the PIMS Postdoctoral Fellowship.Mathilde Papillon is a Physics PhD student in the BioShape Lab at UC Santa Barbara where she develops novel deep learning methods leveraging geometry and topology. She harnesses these models to study relational data, with a special focus on full-body human movement. Mathilde obtained her BSc in Honours Physics from McGill University, and also works as a data scientist in sports analytics.

Mathilde Papillon Sanborn

University of California, Santa Barbara

Mathilde Papillon is a Physics PhD student in the BioShape Lab at UC Santa Barbara where she develops novel deep learning methods leveraging geometry and topology. She harnesses these models to study relational data, with a special focus on full-body human movement. Mathilde obtained her BSc in Honours Physics from McGill University, and also works as a data scientist in sports analytics.