We are thrilled to announce the first official TAG-DS Stand-Alone Event-- TAG... We're it! This will be a two day event, December 1 & 2, 2025, featuring keynotes, poster sessions, spotlight talks, collaboration activities, and community development. The dates and location were selected to align with NeurIPS 2025-- twice the fun! The event will be hosted on the University of California San Diego campus both days and is readily accessible by public transit from downtown for those already planning to attend NeurIPS. There will be an associated Proceedings of Machine Learning Research volume for papers submitted to the archival track.
Important Dates and Links
Paper and Abstract Submission Deadline: Extended to October 8, 2025 (AOE) October 1, 2025 (AOE)
Register HERE! Participation will be capped at 200 for the first year-- don't wait, register now!
Author Notification Date: November 12, 2025 (AOE)
Camera Ready Papers Due: November 24, 2025 (AOE)
Event Dates: December 1-2, 2025 UC San Diego, La Jolla, CA
Paper Template HERE
Become a Reviewer here!
Download the call for papers and share with your network!
Keynotes
Dr. Richard Baraniuk
Richard G. Baraniuk is the C. Sidney Burrus Professor of Electrical and Computer Engineering at Rice University, a member of the Digital Signal Processing (DSP) research group, a member of the Ken Kennedy Institute, the Founder/Director of OpenStax, and the Principal Investigator and Director of SafeInsights, NSF's national research infrastructure for learning and education. He is also a joint faculty member in the Rice departments of Computer Science and Statistics.
Dr. Baraniuk is a member of the US National Academy of Engineering and American Academy of Arts and Sciences and a fellow of the National Academy of Inventors, American Association for the Advancement of Science, and IEEE. He has received the DOD Vannevar Bush Faculty Fellow Award (National Security Science and Engineering Faculty Fellowship), the IEEE Jack S. Kilby Signal Processing Medal, the IEEE Signal Processing Society Norbert Wiener Society Award and Claude Shannon-Harry Nyquist Technical Achievement Award, the Harold W. McGraw, Jr. Prize in Education, and the IEEE James H. Mulligan, Jr. Education Medal, among others.
Dr. Elisenda Grigsby
Elisenda Grigsby received her PhD in mathematics from UC, Berkeley in 2006, joined the math department at Boston College in 2009, and was awarded the AWM-Birman Research Prize in Topology and Geometry and the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2015 and 2016, respectively, for her contributions to knot theory and low-dimensional topology. Shortly thereafter, she pivoted to studying the mathematical foundations of machine learning algorithms, focusing on geometric aspects of deep learning theory. Grigsby now holds a joint appointment in the CS department at BC, and co-directs the BC Experimental Math & Machine Learning Lab.
Dr. Haggai Maron
Dr. Haggai Maron is an Assistant Professor in the Faculty of Electrical and Computer Engineering at the Technion – Israel Institute of Technology and a Senior Research Scientist at NVIDIA Research. He focuses on deep learning for structured data, particularly sets, graphs, point clouds, weight spaces, and other data with inherent symmetry structures. Dr. Maron's work combines theoretical analysis and design of deep learning architectures with practical applications to real-world problems. Notable achievements include receiving the Outstanding Paper Award at ICML 2020, two Best Paper Awards at NeurIPS and ICML workshops in 2024, and the Alon scholarship for the integration of outstanding faculty from the Israel Council for Higher Education in 2024. He holds a Ph.D. in Computer Science and Mathematics from the Weizmann Institute of Science.
Dr. Robin Walters
Robin Walters is an assistant professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.
Walters’ research focuses on the role of symmetry in deep learning. By exploring ways of building a problem’s symmetry into a deep learning model as hard mathematical constraints, Walters has discovered it’s possible to improve not just that model’s data efficiency, but its generalization and trustworthiness as well. Now, as director of the Geometric Learning Lab, Walters is pushing the limits of these methods, making use of approximate symmetry and exploring everything from the theory of underlying symmetry in neural network structure to the subject’s range of practical applications.
Walters also brings his background as a mathematician into the classroom, where he teaches computational theory. He enjoys watching his students develop the ability to think rigorously and communicate clearly about the complex topic, and he particularly enjoys mentoring graduate students. Walters is a visiting fellow at the Boston Dynamics AI Institute, where he develops equivariant neural networks for sample efficient robot perception and manipulation.
Schedule
8:30 AM - 9:00 AM
Check-in
9:00 AM - 9:15 AM
Opening Remarks
9:15 AM - 10:00 AM
Keynote 1
10:00 AM - 10:30 AM
Coffee Break/ Poster Session A Hanging
10:30 AM - 11:45 AM
Spotlight Talks (5x 15 minutes)
11:45 AM - 12:30 PM
Math in Industry Panel
12:30 PM - 2:00 PM
Lunch + Networking
2:00 PM - 2:45 PM
Keynote 2
2:45 PM - 3:30 PM
Flash Talks (9 x 5 min)
3:30 PM - 3:40 PM
Day 1 Closing Remarks
3:45 PM - 4:00 PM
Break
4:00 PM - 5:15 PM
Poster Session A
5:30 PM - 7:00 PM
Hosted Social Hour
9:00 AM - 9:15 AM
Opening Remarks
9:15 AM - 10:00 AM
Keynote 3
10:00 AM - 10:30 AM
TAG-DS Challenge Intro and Results
10:30 AM - 11:00 AM
Coffee Break/ Poster Session B Hanging
11:00 AM - 11:45 AM
Topology Talks/Algebra Talks/Geometry Talks Across 3 Rooms
11:45 AM - 12:30 PM
Topology Panel/ Algebra Panel/ Geometry Panel Across 3 Rooms
12:30 PM - 1:45 PM
Lunch + Networking
1:45 PM - 3:00 PM
Topology/Algebra/ Geometry Collaboration Sessions Across 3 Rooms
3:00 PM - 3:30 PM
Break
3:30 PM - 4:15 PM
Keynote 4
4:15 PM - 4:20 PM
Closing Remarks
4:20 PM - 5:30 PM
Poster Session B
TAG-DS: TAG... We're it! Senior Organizing Committee
Dr. Tegan Emerson
Pacific Northwest National Laboratory
University of Texas El Paso
Colorado State University
Dr. Alex Cloninger
University of California San Diego
Dr. Bastian Grossenbacher Rieck
University of Fribourg
Dr. Gal Mishne
University of California San Diego
Dr. Katie Rainey
Naval Information Warfare Center- Pacific
Dr. Henry Kvinge
Pacific Northwest National Laboratory
University of Washington
Dr. Yusu Wang
University of California San Diego
Dr. Timothy Doster
Pacific Northwest National Laboratory
TAG-DS: TAG... We're it! Program Committee
Topology Area Chair
Mitchell Black
University of California San Diego
Algebra Area Chair
Henry Kvinge
Pacific Northwest National Laboratory
Geometry Area Chair
Qingsong Wang
University of California San Diego
DS Area Chair
Chester Holtz
University of California San Diego
Committee Chair
Sharvaree Vadgama
University of Amsterdam
Poster Chair
Eric Yeats
Pacific Northwest National Laboratory
Speaker & Panel Chair
Jianke Yang
University of California San Diego
Submission Chair
Ińes García-Rodondo
London School of Geometry and Number Theory
Challenge Co-Chair
Guillermo Bernardez Gil
University of California Santa Barbara
Challenge Co-Chair
Lev Telyatnikov
École Polytechnique Fédérale de Lausanne
Challenge Co-Chair
Mit Kotak
Massachusetts Institute of Technology
Challenge Co-Chair
Mathilde Papillon
University of California Santa Barbara
Proceedings Chair
Alison Pouplin
Aalto University
Sponsors