Topology, Algebra, and Geometry in Data Science (TAG-DS) 2026

2nd Annual Conference
August 18-20, 2026
Northeastern University, Boston, MA, USA

Sections

Announcements

Important dates

Registration

Tools for authors

Topological Deep Learning Challenge

Attendance

Venue

Organization

Announcements

Follow our twitter account (@TAGinDS) for real-time conference updates and news.

If you have questions, please contact the organizers at info@tagds.com

TAG in DS and the Boston Symmetry Group are proud to present The Boston TAG Party 2026 – a collaborative conference rallying together to ensure “No AI without Mathematical Representation!”

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 century’s 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.

Building on the success of past TAG-DS workshops, this event will showcase work which brings methods from topology, algebra, and geometry and uses them to help answer challenging questions in machine learning and artificial intelligence. Symmetry as a unifying theme of the three TAG branches, and in collaboration with the Boston Symmetry Group, the 2026 Boston Symmetry Day will form the first day of the event.

Submissions are welcome for both a full archival paper track and a non-archival extended abstract track; only accepted full, archival-track papers will be published in the associated volume. Want to get an idea out there or looking for collaborators on a problem of interest? New this year, we are adding an open-problem/conjecture track! Please join us for 3 days filled with collaboration and networking activities, exciting talks, illuminating panels, and mathematical ideation to advance and understand machine learning!

Important dates

Paper submission deadline:

June 12, 2026 - (AOE)

Author notification deadline:

July 15, 2026 - (AOE)

Camera-ready paper deadline:

August 3, 2026 - (AOE)

TDL challenge submission deadline:

August 12, 2026 - (AOE)

TAG-DS 2026 conference start:

August 18, 2026 - (AOE)

Registration

Coming soon!

Tools for authors

Submission information

TAG-DS is offering 3 submission tracks this year:

full archival paper track
non-archival extended abstract track
open-problem/conjecture track

**** Only accepted, full archival-track papers will be published in the associated PMLR volume ****

Submission topics (non-exhaustive list)

Mathematically-constrained representation learning
Symmetry in data and learning
Novel architectures
Alternative learning objectives
Training schemes
Robustness
AI for Math
Model Evaluation
Datasets, Explainability
Reduced-order and Energy Efficient Models
Domain-driven Data Analytics

Submission website

The OpenReview submission website is the same for all three tracks:

https://openreview.net/group?id=TAG-DS/2026/Conference

Within the submission form you must choose the track to which you wish to submit.

**** Only accepted, full archival-track papers will be published in the associated PMLR volume ****

Templates

Please format all submitted papers using the TAG-DS 2026 template:

LaTeX template for paper submissions

The template provides three formatting options:

submission --> default; for all submitted papers prior to acceptance
proceedings --> use for accepted archival (proceedings) track papers
nonproceedings --> use for accepted non-archival extended abstracts and open-problem/conjecture track papers

All submissions should be formatted using the submission option prior to acceptance.

Please contact us at info@tagds.com if you run into any issues with the template.

The TAG-DS LaTeX template is based on the Journal of Machine Learning Research (JMLR) official style files.

Topological Deep Learning Challenge

Overview

In collaboration with TopoBench and GraphUniverse, TAG-DS 2026 is proud to once again host the Topological Deep Learning (TDL) Challenge.

The theme of this year's challenge is, "Topological Deep Learning Challenge 2026: Bridging the Gap," with the goal of connecting Topological Neural Networks (TNNs) and Graph Neural Networks (GNNs). For the first time, the TDL Challenge will go beyond implementation to feature a rigorous performance analysis of the submitted models. Through a shared benchmarking ecosystem of GNNs and TNNs, the 2026 TDL Challenge aims to formulate data-driven answers to long-standing scientific questions:

Structural Sensitivity: How do specific graph properties (e.g., severe heterophily) impact the performance of classical GNNs versus their higher-order topological counterparts
The Topological Component: Under what specific data regimes and controlled environments do TDL models consistently provide unique capabilities over standard state-of-the-art GNN approaches (if any)?

TDL Challenge Submissions

Submissions to the TDL challenge are made directly through the challenge website:

https://geometric-intelligence.github.io/topobench/tdl-challenge-2026/index.html

All submissions are due by August 12th, 2026 (AoE).

***Every submission that meets the requirements will be included in a white paper summarizing the challenge’s findings (planned via PMLR through Topology, Algebra, and Geometry in Machine Learning/Data Science 2026). Authors of qualifying submissions will be offered co-authorship.***

TDL Challenge Awards

Two winning teams (one per track) will be announced at TAG-DS 2026 during the Awards Ceremony, and will receive the following prizes:

Track 1 (GNNs): 1st place $1,000 USD, 2nd place $400 USD (sponsored by New Theory).
Track 2 (TNNs): 1st place $1,000 USD, 2nd place $400 USD (sponsored by Arlequin AI).
Honorable mentions: $700 USD split across other outstanding submissions (additional evaluation notebook with further benchmarking, particularly challenging implementations, participants who submit multiple high-quality submissions, etc).

Additionally, the Geometric Intelligence Lab, University of California, Santa Barbara and the Intelligent Maintenance and Operations Systems (IMOS) Lab at EPFL in Lausanne, Switzerland are each offering research internships to qualifying teams.

Check out the challenge website for more details and official rules.

Attendance

Schedule

Coming soon!

Keynote speakers

Coming soon!

Venue

Conference venue

Curry Student Center
CSC Ballroom (2nd Floor)

Northeastern University

360 Huntington Ave

Boston, MA 02115

Travel information

To travel from Boston to Northeastern University using public transportation (MBTA):

Subway (T)

Take the Green Line E branch (also known as the “E Line”) outbound towards Heath Street.
Exit the train at the “Northeastern University” station.
Northeastern University is within walking distance from the station.

Commuter Rail

If you are coming from a location outside of Boston and closer to a Commuter Rail station, you can take a Commuter Rail train that services the Ruggles station.
From Ruggles station, it's just a short walk to Northeastern University.

Bus

Various MBTA buses service the Northeastern University area.
You can check the MBTA website or use the Transit app for specific bus routes and schedules, as they may change from time to time.

For the most up to date information on public transportation, please refer to the official Massachusetts Bay Transportation Authority (MBTA) website or Transit app.