The conference will reflect current developments in motivic homotopy theory and its applications in arithmetic geometry and geometric representation theory. It aims to bring together experts from these fields to facilitate the exchange of ideas in a collaborative and engaging environment.

SumTopo is an established conference series since 1986, attracting 120-200 participants annually. As always, this meeting will engage discussion in modern advances in topology through the activities of several special sessions, as well as invited plenary and semi-plenary speakers.

SumTopo 2025 will be held at the University of South Alabama in Mobile, AL from August 11-14, with a few additional pre-conference activities happening on August 10. For more information, please contact the local organizers (Steven Clontz, Joanna Furno, and Elena Pavelescu) at <[email protected]>.

We are grateful for the support of the National Science Foundation for this conference.

The Annual International Conference of the Georgian Mathematical Union was established in 2010 and has been held traditionally at Batumi Shota Rustaveli State University. Batumi is the city of Georgia and the capital of the Autonomous Republic of Adjara. It is located along the coast of the Black Sea in the southwest region of Georgia. In accordance with recent developments, the conference has been conducted in a hybrid format since 2021.

The purpose of the conference is to bring together mathematicians from various fields to present their original research results and provide opportunities to establish new connections within the fields of pure and applied mathematics, as well as science, engineering, and technology. The conference also provides valuable networking opportunities for you to meet great personnel in these fields.

This 2-day special session will be dedicated to a range of mathematical problems related to motion planning algorithms and their properties. A central role is played by the notion of topological complexity (TC), which is a homotopy invariant depending only on the configuration space of the robot that can be studied using diverse tools from a variety of fields, such as geometry, topology, algebra, combinatorics, etc. This session on theoretical and applied aspects of TC and related sectional category invariants aims to bring together scientists from all over the world working on different aspects of motion planning and TC and foster collaboration among them, expose graduate students and junior colleagues to these rich and fascinating areas of research, and identify directions for future work and interaction in these areas.

This workshop, sponsored by AIM and the NSF, will be devoted to recent advances in computing the stable homotopy groups of spheres. The last 10 years have seen significant progress in this area, driven first by applications of motivic homotopy theory and then more recently by the invention of synthetic/filtered spectra, which generalizes motivic techniques. Last year, Weinan Lin, Guozhen Wang, and Zhouli Xu significantly extended the known range of stable homotopy groups and used these computations to resolve the remaining case of the Kervaire Invariant One problem, which has remained open for about 60 years. This workshop will focus on the advances that made these computations possible, especially those involving machine computations and synthetic techniques, and look for applications of these new techniques, for example to the equivariant slice spectral sequence.

Topological Data Analysis (TDA) is a research area at the intersection of Algebra, Topology, Geometry, Statistics and Machine Learning. While methods from TDA have been applied successfully to data from a variety of domains — from financial mathematics, to materials science, climate science, biomedical imaging, or social science —, there has been less work done on studying in a principled way how such methods can be applied to data that changes dynamically over time. The goal of this workshop is to bring together experts in TDA with experts in application domains in which time-varying data is particularly prevalent. Examples include time series in climate modelling and financial mathematics, as well as dynamic networks in epidemiology and social science modelling.

IEEE VIS Workshop on Topological Data Analysis and Visualization (TopoInVis 2025) Co-located with IEEE VIS 2025 - Vienna, Austria, 2-7 November

Call for Contributions

Topological methods have become an established framework for the extraction and analysis of structural patterns in complex data. It has been successfully applied in a variety of application fields, including quantum chemistry, astrophysics, fluid dynamics, combustion, material sciences, biology, and data science. In particular, the genericity, efficiency, and robustness of topological methods have made them particularly well suited for the multi-scale, interactive analysis and visualization of the underlying structural information of data.

Despite their rising mainstream popularity, topological methods still face a number of challenges, including, for instance, efficient computational methods for large-scale time-varying data, the characterization of noise and uncertainty, or the support of emerging data types, such as ensemble data or high-dimensional point clouds.

The IEEE VIS Workshop on Topological Data Analysis and Visualization aims to be an inclusive forum for the fast dissemination of the latest results in theory, algorithms, and applications of topological methods for the interactive and visual analysis of data. This workshop is open to members of the visualization community interested in topological methods and to experts in topological methods from other communities willing to experiment with interactive and visual applications.

The workshop welcomes submissions of both full-length papers and posters. The accepted papers will be presented during the workshop, and the accepted posters will be presented at the main poster event at VIS and give a lightning talk during the workshop.

Scope

Relevant topics include (but are not limited to): Topological methods for the analysis and visualization of all types of data, including but not limited to: Graph data Scalar, vector, tensor, multi-field data Time-series data High dimensional point cloud data Ensemble data Data with uncertainty Topological methods for data science (dimensionality reduction, clustering, etc.) Topological methods and machine learning Computational methods for topological data analysis and visualization Software systems for topological data analysis and visualization Visual analytic frameworks relying on topological methods Applications of topological data analysis and visualization

Submission

We welcome contributions as regular papers in the IEEE VGTC format (up to 9 pages of content, plus up to 2 pages of references). Paper submissions will be peer-reviewed by an international program committee, including experts in topological methods for scientific data, information visualization, visual analytics, computational geometry, computational topology, and machine learning. Accepted papers will be published in the IEEE Xplore Digital Library.

Poster contributions should adhere to the IEEE VIS Guidelines. While VIS has no specific formatting requirements, posters can be no larger than size A0 (841 x 1189 mm / 33.1 x 46.8 inches) and must be in a portrait orientation. The accepted posters will be presented at the main poster event at VIS.

Important Dates * June 14, 2025: abstract deadline for full papers * June 30, 2025: submission deadline for full papers * July 31, 2025: author notification * August 31, 2025: submission deadline for posters, lightning talks * November 2-7, 2025: IEEE VIS All deadlines are in Anywhere on Earth (AoE) time zone.

Instructions for submission and further details will appear soon on the workshop website: https://topoinvis.org/

Organizing Committee:

Workshop Chairs: Divya Banesh, Los Alamos National Laboratory Federico Iuricich, Clemson University

Paper Chairs: Tobias Günther, Friedrich-Alexander Universität Erlangen-Nürnberg Yue Zhang, Oregon State University

Communications Chair: Lin Yan, Iowa State University Raghavendra Sridharamurthy, International Institute of Information Technology, Hyderabad

Contact: [email protected]

This workshop, sponsored by AIM, the NSF, the Topos Institute, and the US NSF Center for Analysis and Prediction of Pandemic Expansion, will consider how category-theoretic foundations for modeling as decision support for multidisciplinary collaboration might advance insights into pandemic science. Multidisciplinary modeling is extremely useful and also extremely difficult (for many reasons). By taking the very concept of "building a model" as itself a sort of model, and phrasing this in the formal mathematical language of (double) category theory, we can develop systems that greatly improve our capabilities for collaborative modeling.

The workshop will bring together a wide range of research communities: category theory, software engineering, dynamical systems, data science, epidemiology, infectious disease modeling, medical geography, behavioral psychology, social and urban networks, and economics.