In collaborative with the Boston Symmetry Group, TAG in DS is proud to announce that the 2nd annual TAG-DS conference,
The Boston TAG Party -- "No AI Without Mathematical Representation!"
to be held at Northeastern University in Boston, MA, USA from August 18-20, 2026.
Follow our twitter account (@TAGinDS) for real-time conference updates and news.
If you have questions, please contact the organizers at info@tagds.com
Registration is now open for TAG-DS 2026. The pre-registration deadline is August 7, 2026.
The most recent announcements are listed here for convenience. For older announcements, please visit the Announcement Archive in the FAQ section below.
2026.08.04
The conference schedule is now available for TAG-DS 2026! Check out the wonderful talks we have in store in two weeks!
You can find a TAG-DS 2026 Overview and the complete TAG-DS 2026 Schedule in the Schedule section below.
2026.07.31
The 2026 TDL challenge submission deadline is fast approaching -- there was a typo in the initial announcement and final TDL challenge submissions are due by August 1, 2026 (AoE). We are happy to report that there has already has more than 50 submissions!
You can find more details in the Topological Deep Learning Challenge section, or directly at the TDL challenge website.
2026.07.30
Two updates:
The negotiated hotel rate for the Boston Marriott Copley Place has been extended through the end of the day, Friday, July 31st!
Please register for TAG-DS 2026 by August 7, 2026.
Use this registration link by the pre-registration deadline so we can provide accurate headcounts for food and social activities!
You can find more details about the conference hotel in the Venue section and more details about registration in the Registration section.
2026.07.27
A quick note on the reserved hotel rooms for TAG-DS 2026: The negotiated rate expires today, July 27th! To take advantage of this special deal, please reserve your stay using this direct link. The link will take you to another website where you can complete your booking. And while you're at it, don't forget to register for the conference itself via this registration link.
You can find more details about the conference hotel in the Venue section below.
2026.07.24
A TAG-DS 2026 Powerpoint poster template and UPDATED TAG-DS 2026 LaTeX paper templates are now available. The powerpoint template poster is provided as an example that meets the formatting restrictions, but use of this template is not required. For camera-ready paper submissions, please use the UPDATED LaTeX paper template.
Download the TAG-DS 2026 Powerpoint poster template or [UPDATED] TAG-DS 2026 LaTeX paper template or visit the Tools for Authors section.
For older announcements, please visit the Announcement Archive in the FAQ section below.
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!
June 12 , 2026 - (AoE) June 15, 2026 - (AoE)
(Paper submission deadline extended)
July 15, 2026 - (AoE)
August 12, 2026 - (AoE) August 1, 2026 - (AoE)
August 3, 2026 - (AoE)
August 11, 2026 - (AoE)
August 18, 2026 - (AoE)
We have added a small registration fee this year to offset the cost of hosting this conference, however, we want this event to be accessible to everyone. If the registration fee causes any financial hardship, please reach out to info@tagds.com. Refreshments will be provided!
3 registration options available for authors, sponsors, and general participants: Student, Non-Student, and Sponsor
A reduced-rate 'Local' ticket is available for participants who are only able to join one day
At least one author is required to register at the full rate (Student, Non-Student, or Sponsor) for each accepted paper
The pre-registration deadline is August 7th, 2026 (AoE) -- registering by this date will allow us to provide an accurate headcount for food and social activities
The registration deadline is August 11th, 2026 (AoE)
See all the details and purchase your registration at https://commerce.cashnet.com/SFTAG or using the button below. If you have any questions, please reach out to info@tagds.com.
Student (3-day ticket)
$25.00
Non-Student (3-day ticket)
$50.00
Sponsor (3-day ticket)
$50.00
Local (1-day ticket)
$10.00
The conference schedule is available as in two forms:
a schedule overview is available as a static PNG that can be downloaded for offline use, and
a detailed schedule is available as a Google Doc, which will be updated if any changes arise.
Georgia Institute of Technology
Title: Data-driven, symmetry-informed modeling of physical processes.
Abstract: Mathematical modeling of physical processes is a problem that is both extremely common and often quite hard. To make matters worse, modeling approaches are often ad hoc and rely on empirical assumptions that may not hold for the physical process of interest. This talk will describe a systematic data-driven alternative that uses symmetry as an inductive bias. The modeling framework SPIDER leverages irreducible representations of the symmetry group to construct the search space and sparse regression to identify a complete set of parsimonious governing equations. It will be illustrated via a couple of applications in fluid dynamics: a model of active nematic constructed using experimental data and a model of fluid turbulence constructed using data generated by DNS.
Speaker Bio: Roman Grigoriev is a Professor in the School of Physics at Georgia Tech. He is a recipient of the Frenkiel Award from the APS Division of Fluid Dynamics. Dr. Grigoriev's research interests cover a broad range of topics in dynamical systems and control with applications to fluid dynamics, excitable media, instabilities, and turbulence. His recent work focuses on developing the foundations of scientific machine learning with applications to modeling of hydrodynamic phenomena and mutliscale problems.
University of California, San Diego
Talk Title: TBA
Abstract: TBA
Speaker Bio: Gal Mishne is an associate professor in the Halıcıoğlu Data Science Institute (HDSI) at UC San Diego and is affiliated with the ECE and CSE departments and the Neurosciences Graduate program. Her research lies at the intersection of machine learning, applied mathematics, and computational neuroscience, with a focus on geometric representation learning, high-dimensional data analysis, and methods for multimodal and multiway data. Her group develops theoretically grounded and computationally efficient approaches for uncovering latent structure in complex scientific data, with particular emphasis on neural and biomedical applications. Her research group has been funded by NSF, NIH, the Simons Foundation, and the W. M. Keck Foundation. Before joining UCSD, Dr. Mishne was a Gibbs Assistant Professor in the Applied Math program at Yale University with Prof. Ronald Coifman's research group. She received her PhD in Electrical Engineering from the Technion in 2017. Dr. Mishne was selected in 2017 as Rising Star in EECS and an Emerging Scholar in Science.
University of California, Santa Barbara
Title: Non-Euclidean Learning Deserves Non-Euclidean Interpretability
Abstract: Non-Euclidean deep learning has shown that respecting the structure of data pays off, whether it be topological deep learning for systems with multiway relations, or geometric deep learning for data that lives on manifolds. In this talk, I will discuss theoretical and practical tools at our disposal for understanding these models, and argue that they, too, must be non-Euclidean. First, in the data: for manifold-structured spatiotemporal signals, I will show when respecting that manifold during model explanation improves the quality of that explanation. Second, in the preprocessing: an accessible way to see and measure the structure created when ordinary data is lifted into a topological domain, before any training happens. Third, in the model: the first exact, cell-level explanations of topological neural networks’ predictions. Across all three, interpretability for non-Euclidean learning has to be non-Euclidean too.
Speaker Bio: Mathilde Papillon is a Physics PhD candidate in the Geometric Intelligence Lab at the University of California 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 embodied intelligence. In parallel with her doctoral work, Mathilde is a Research Fellow at Goodfire AI, focusing on interpretability for embodied intelligence. She previously served as the Los Angeles Dodgers’ first AI Research Scientist. Mathilde obtained her BSc in Honours Physics from McGill University and was awarded Canada’s Post Graduate Doctoral Fellowship.
Massachusetts Institute of Technology
https://maurice-weiler.gitlab.io/
Title: TBA
Abstract: TBA
Speaker Bio: Maurice Weiler is a postdoctoral researcher at MIT CSAIL, working on geometric deep learning and equivariant neural networks. He holds an MSc in computational physics from Heidelberg University and a PhD in machine learning from the University of Amsterdam under Max Welling. During his PhD, he characterized the structure of equivariant convolutions through the theory of steerable kernels, and extended this framework to manifolds via a gauge-theoretic formalism — published as the monograph "Equivariant and Coordinate Independent Convolutional Networks". Today, working with Tommi Jaakkola at MIT, he is extending this line of work to geometric attention mechanisms, efficient GPU implementations, and equivariant scaling laws.
More coming soon....
TAG-DS is offering 3 submission tracks this year:
Full archival paper track (8-pages excluding references, appendices, and supplementary material)
Full archival papers must include novel work, results, and theory consistent with requirements for publication as completed research with clear conclusions. Submissions must stand-alone, independent of appendices and supplementary material.
Accepted full archival papers will be published in the associated PMLR volume
Non-archival extended abstract track (4-pages excluding references)
Non-archival extended abstracts may include work in progress or summaries of work that has been or will be published in alternative venues
Will not be published in the associated PMLR volume
Open-problem/conjecture track (3-pages excluding references)
Open-problem/conjecture submissions should describe important open problems within the TAG space
What are the major questions at the interface of AI and math? We are specifically looking for questions that are broad enough to be accessible and of interest to a significant fraction of attendees but remain focused on TAG topics. Examples might include: a better understanding of how specific mathematical perspectives might apply to machine learning; a theoretical explanation of observed phenomena (e.g., “why do equivariant architectures always…”); or methods of measuring TAG-relevant quantities “how can we measure X in large foundation models?” This track is also appropriate for posing open conjectures to engage the community and seed future collaborations.
Will not be published in the associated PMLR volume
**** Only accepted, full archival-track papers will be published in the associated PMLR volume ****
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
Survey papers relevant to the TAG-DS community
The OpenReview submission website is the same for all three tracks:
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 ****
Paper Templates: Please format all submitted papers using the TAG-DS 2026 template:
[UPDATED] TAG-DS 2026 LaTeX paper template
The template provides three formatting options:
submission --> default; for all submitted papers prior to acceptance
proceedings --> use for accepted full archival (proceedings) 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.
The TAG-DS LaTeX template is based on the Journal of Machine Learning Research (JMLR) official style files.
Poster Template: The TAG-DS 2026 poster template is provided as an example that meets the formatting restrictions:
TAG-DS 2026 Powerpoint poster template
Use of the poster template is optional.
Please contact us at info@tagds.com if you run into any issues with the templates.
In collaboration with TopoBench and GraphUniverse, TAG-DS 2026 is proud to once again host the Topological Deep Learning (TDL) Challenge. The challenge is organized by Guillermo Bernárdez, Lev Telyatnikov, Mathilde Papillon, Marco Montagna, Louisa Cornelis, Louis Van Langendonck, Olga Fink, and Nina Miolane.
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)?
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) August 1st, 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.***
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.
Curry Student Center
CSC Ballroom (2nd Floor)
Northeastern University
360 Huntington Ave
Boston, MA 02115
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.
To travel to Northeaster University by car, the following (paid) parking garages are close to the conference venue:
Gainsborough Garage
10 Gainsborough Street
Boston, MA 02115
Renaissance Park Garage
835 Columbus Avenue
Boston, MA 02120
A block of rooms has been reserved at the Boston Marriott Copley Place for TAG-DS 2026 attendees:
The link will take you to another website where you can complete your booking. The negotiate rate is valid through July 31st, 2026. The discounted rooms are available on a first come, first served basis, but there are many other hotels conveniently located near the conference venue if the reserved rooms fill up.
Please contact us at info@tagds.com if you have trouble reserving accomodations.
Eddie Berman
Northeastern University
Guillermo Bernárdez
University of California
Santa Barbara
Samantha Chen
Oberlin College
Alex Cloninger
University of California
San Diego
Timothy Doster
Pacific Northwest
National Laboratory
Tegan Emerson
Pacific Northwest
National Laboratory,
University of Texas, El Paso
J. Elisenda Grigsby
Boston College
Henry Kvinge
Pacific Northwest
National Laboratory,
University of Washington
Hannah Lawrence
Massachusetts Institute of Technology
Tim Marrinan
Pacific Northwest
National Laboratory
Audun Myers
Pacific Northwest
National Laboratory
Mathilde Papillon
University of California
Santa Barbara
Behrooz Tahmasebi
Harvard University
Lev Telyatnikov
Capital One
Robin Walters
Northeastern University
Melanie Weber
Harvard University
YuQing Xie
Massachusetts Institute of Technology
Eric Yeats
Pacific Northwest
National Laboratory
The Boston TAG Party is possible in part due to the support of our sponsors. Check out their websites to see what kind of cool things they are working on and meet with them in-person at the conference for more information.
If you, or your organization, are interesting in joining our TAG-DS research community and sponsoring the Boston TAG Party, we would love to discuss your involvement. Please email us at info@tagds.com for more details on sponsorship information or check out the FAQ section below.
Where can I find up-to-date news about the conference or changes to the schedule?
We will strive to keep this website up-to-date through the end of the conference, but the for real-time conference updates and news please follow the TAG-DS twitter account (@TAGinDS) for real-time conference updates and news. If you do not have a Twitter account and are having trouble viewing the feed, tools like Twitter Web Viewer will enable you to see our posts without logging in.
How do I submit a paper to the Boston TAG Party?
The OpenReview submission website can be found here: https://openreview.net/group?id=TAG-DS/2026/Conference. More details are available in the 'Tools for Authors' section above.
Does my paper submission have to follow particular formatting requirements?
Yes, all submissions must use the provided LaTeX template for paper submissions. The template provides three formatting options, for the initial submissions, manuscripts from all three tracks should use the submission option (this is already set as the default) which anonymizes the authors to facilitate the reveiw process. If your manuscript is accepted, you will submitted a camera-ready version using either the proceedings option, for accepted full archival (proceedings) papers or the nonproceedings option for accepted non-archival extended abstracts and accepted open-problem/conjecture track papers. If you have questions, please email us at: info@tagds.com. More specific details on the formatting requirements are available within the template.
What is the deadline for paper submission?
The Boston TAG Party is offering three submission tracks, a full archival paper track, a non-archival extended abstract track, and a open-problem/conjecture track. The deadline for all three tracks is the same: June 15, 2026 - Anywhere On Earth (AOE). A countdown to the deadline is available in the 'Important Dates' section above.
What is the difference between the three submission tracks? Which track is right for my manuscript?
The a full archival paper track must include novel work, results, and theory consistent with requirements for publication as completed research with clear conclusions. If you want your paper to be considered for publication in the conference proceedings, this is the track to which you should submit. Submissions should be no more than 8 pages long, not including references, appendices, or supplementary materials, and should be self-contained, independent of appendices or supplementary material.
The non-archival extended abstract track is less formal. Submissions to this track might include work in progress or summaries of work that has been or will be published in alternative venues. Manuscripts submitted to the non-archival extended abstract track will not be published in the conference proceedings, but will still be presented at the conference if accepted. Submissions should be no more than 4 pages long, not including references.
The open-problem/conjecture track is intended as a way for our community to define some of future directions of TAG research. Submissions should describe important open problems within the TAG space that are broad enough to be accessible and of interest to a significant fraction of the Boston TAG Party attendees but remain focused on TAG topics. Submissions to the open problems/conjectures track will not be considered for publication in the conference proceedings, however we are working to create a public repository for these ideas for ongoing community engagement. More details will be available closer to the start of the conference. Submissions should be no more than 3 pages long, not including references.
If you are having trouble deciding which track is right for you, feel free to email us at: info@tagds.com. We would be happy to help.
How do I register to attend the Boston TAG Party?
The registration site is not yet live, but check back soon for more details. We will post a link in the 'Registration' section above when it is available.
What should I do if I want to attend the Boston TAG Party but cannot afford the registration fee?
We do not want money to prevent anyone from being able to participate in the Boston TAG Party. Funds are available to subsidize registration fees. Please email us at info@tagds.com to request support. Check back for more details will once the registration site is live.
I am in Boston and trying to attend the conference, but I can't find the venue. What should I do?
The Boston TAG Part is being held at Northeastern University from August 18-20, 2026 in the Curry Student Center. The physical address is: CSC Ballroom (2nd Floor), Northeastern University, 360 Huntington Ave , Boston, MA 02115. Instructions for public transportation or parking are available in the 'Venue' section above. If you need assistance, please send us an email at: info@tagds.com. We will be checking our emails regularly as the conference draws near.
How do I learn more about the Topological Deep Learning Challenge or submit to the challenge?
The 2026 TDL Challenge is hosted by our friends at TopoBench and GraphUniverse and looks to be very interesting. You can find an overview of the challenge in the 'Topological Deep Learning Challenge' section above, and you can read the complete details of the challenge or submit to the challenge at this website: https://geometric-intelligence.github.io/topobench/tdl-challenge-2026/index.html. Final submissions are due by August 12th, 2026 - Anywhere On Earth (AOE). A countdown to the deadline is available in the 'Important Dates' section above. The results of the challenge will be announced and the awards will be presented at the Boston TAG Party.
My company/organization would like to get involved. How can we become a sponsor for the Boston TAG Party and what are the benefits?
Great! It is wonderful to have people invested in our active and growing TAG-DS research community and we would love to discuss your involvement. Please email us at info@tagds.com. Sponsors at the Bronze Tier ($500) receive their logo on website. Sponsors at the Silver Tier ($1500) receive the opportunity to give a 5 minute presentation at the conference and their logo on website and event-day signage. Sponsors at the Gold Tier ($3000+) receive the opportunity to give a 15 minute presentation at the conference, their logo on website and event-day signage, a seat on an industry/domain-focused panel at the event, and acknowledgement in Opening & Closing Remarks.
2026.08.04
The conference schedule is now available for TAG-DS 2026! Check out the wonderful talks we have in store in two weeks!
You can find a TAG-DS 2026 Overview and the complete TAG-DS 2026 Schedule in the Schedule section below.
2026.07.31
The 2026 TDL challenge submission deadline is fast approaching -- there was a typo in the initial announcement and final TDL challenge submissions are due by August 1, 2026 (AoE). We are happy to report that there has already has more than 50 submissions!
You can find more details in the Topological Deep Learning Challenge section, or directly at the TDL challenge website.
2026.07.30
Two updates:
The negotiated hotel rate for the Boston Marriott Copley Place has been extended through the end of the day, Friday, July 31st!
Please register for TAG-DS 2026 by August 7, 2026.
Use this registration link by the pre-registration deadline so we can provide accurate headcounts for food and social activities!
You can find more details about the conference hotel in the Venue section and more details about registration in the Registration section.
2026.07.27
A quick note on the reserved hotel rooms for TAG-DS 2026: The negotiated rate expires today, July 27th! To take advantage of this special deal, please reserve your stay using this direct link. The link will take you to another website where you can complete your booking. And while you're at it, don't forget to register for the conference itself via this registration link.
You can find more details about the conference hotel in the Venue section below.
2026.07.24
A TAG-DS 2026 Powerpoint poster template and UPDATED TAG-DS 2026 LaTeX paper templates are now available. The powerpoint template poster is provided as an example that meets the formatting restrictions, but use of this template is not required. For camera-ready paper submissions, please use the UPDATED LaTeX paper template.
Download the TAG-DS 2026 Powerpoint poster template or [UPDATED] TAG-DS 2026 LaTeX paper template or visit the Tools for Authors section.
2026.07.24
Four of our TAG-DS Keynote Speakers have been announced! We are very eager to hear about the exciting work of our esteemed colleagues Dr. Roman Grigoriev, Dr. Gal Mishne, Mathilde Papillon, and Dr. Maurice Weiler at TAG-DS 2026 in Boston.
Dr. Roman Grigoriev will be sharing his work on, "Data-driven, symmetry-informed modeling of physical processes."
Dr. Gal Mishne's talk title and abstract will be announced soon.
Mathilde Papillon is will be discussing work titled, "Non-Euclidean Learning Deserves Non-Euclidean Interpretability."
Dr. Maurice Weiler's talk title and abstract will be announced soon.
Please see the profiles in the Keynote Speakers section for speaker bios and keynote lecture abstracts.
2026.07.17
Authors have now been notified with decisions for all submitted papers. As a reminder, camera-ready versions of accepted submissions are due by August 3, 2026 (AoE). If you have not yet received a decision, please log into your OpenReview Author Console to see your submission status or contact us at info@tagds.com for help.
Thank you to our amazing peer reviewers and area chairs for your work!
2026.07.15
TAG-DS 2026 has reserved a block of rooms at the Boston Marriott Copley Place! To take advantage of the negotiated lower room rate, please reserve your stay using this direct link. The link will take you to another website where you can complete your booking.
You can find more details about the conference hotel in the Venue section below.
2026.07.15
Registration is open for TAG-DS 2026! We have added a small registration fee this year to offset the cost of hosting this conference, however, we want this event to be accessible to everyone. If the registration fee causes any financial hardship, please reach out to info@tagds.com. Refreshments will be provided!
3 registration options available for authors, sponsors, and general participants: Student, Non-Student, and Sponsor
A reduced-rate 'Local' ticket is available for participants who are only able to join one day
At least one author is required to register at the full rate (Student, Non-Student, or Sponsor) for each accepted paper
The registration deadline is August 11th, 2026 (AoE)
Purchase your registration via this registration link or using the button below.
For more details, please visit the Registration section.
Beginning of announcements