Events

Researchers working on graph algorithms use a broad range of different criteria for deciding what makes an algorithm efficient. While in theory the dominant benchmark is the asymptotic running time, in practice the story is more nuanced: an algorithm needs to be simple enough to be implementable, fast on graphs of bounded size, space efficient, cache-friendly, and easy to test. While many of these requirements motivate interesting algorithmic questions that are highly relevant in practice, they are often overlooked by the theory community. The goal of the workshop is to foster the exchange of ideas between researchers working on graph algorithms, which have high practical relevance. The workshop will include overview talks on the various perspectives, research talks, an open problem session, and structured time for collaboration. The topics of the workshop include fundamental data science graph algorithms (e.g., clustering, partitioning, graph embedding), graph neural networks, and modeling data using networks (e.g. approximate nearest neighbor search). Additionally, the workshop program incorporates problems and approaches necessitated by scaling graph algorithms to large datasets (e.g. parallel, distributed, dynamic and external memory models, as well as algorithm engineering).

The Institute for Mathematical Stochastics at the Technical University Braunschweig will host the Workshop "Stochastic processes on random geometries” scheduled from February 17-21 2025. The program will feature two mini lecture series delivered by: Martin Barlow (University of British Columbia) Nina Gantert (TU Munich) In addition, the event will include 14 invited lectures and one session dedicated to young researchers. The specific topics include random walks in random environment, static and dynamic percolation, interacting particle systems, statistical mechanics and their applications in physics, materials science and biology. Our aim is to provide a platform for the presentation of recent results, national and international scientific exchange and the education of younger scientists that are interested in the subject. For more information, including a list of invited speakers and a link for registration, see the workshop webpage. The workshop is supported by the SSP2265 "Random Geometric Systems".

This workshop brings together researchers from three distinct streams of mathematics: the classical rigidity theory of bar-joint and tensegrity frameworks in combinatorics and discrete geometry; the theory of generalized circle packing that arose from the study of 3-manifolds in geometric topology, extending to sphere packing and jamming; and discrete differential geometry. A scattering of results in recent years has started to forge connections among these fields. The main aim of the workshop is to encourage their cross-fertilization, with particular emphasis on the rigidity of inversive distance packings. Participants will attend presentations on cutting-edge research and initiate new collaborations.

This conference is devoted to the mathematical theory of incompressible flows. It will hold 13 talks by international young speakers dealing with different aspects of the analysis of partial differential equations coming from the area of fluid mechanics.

Given an incidence structure, one may model a variety of geometric problems. This Semester Program will revolve around two fundamental examples and their applications to modern challenges in the study, analysis, and design of materials. (1) Packings and patterns of circles where the underlying combinatorics are mixed with advanced geometric concepts and strong links are made to discrete differential geometry. (2) The rigidity and flexibility of bar-joint structures where real algebraic geometry is intertwined with sparse graph theory and matroidal techniques. A prime objective of the program is to advance the applicability of these topics to fundamental applications, most notably in statistical physics and materials science. The program will integrate diverse fields of discrete mathematics, geometry, theoretical computer science, mathematical biology, and statistical and soft matter physics. Various workshops will be designed to attract both theoretical and applied practitioners and to stimulate the cross-fertilization of ideas between these disparate communities.

The great event on Recreational Mathematics and Mathematical Magic. Honoring Martin Gardner.

The study of pattern formation in biological, ecological, physical, and social systems involves a rich interplay between theory, modeling, and computation. In recent years, new opportunities have emerged for pattern detection and identification in applications using data-scientific approaches. These applications include spiral waves in cardiac dynamics, vegetation patterns in arid ecosystems, and cell organization in biological tissues. The aim of this workshop is to bring together researchers at the interface of these diverse aspects of pattern-formation theory, computation, and applications in order to share ideas and identify new challenges and open problems. The scientific program will focus on the use of dynamical systems methods in the study of pattern formation, as well as the integration of dynamics, data, and computation.

Biological systems are typically highly interconnected and complex. With technological advances, it is possible to collect massive amounts of data from these systems, but it is not always clear how to organize the information to draw conclusions and make predictions. In such cases, mathematical formulations are powerful tools allowing researchers to frame questions, explore patterns, and synthesize information. The workshop aims to build research collaboration among researchers in mathematical biology. Participants will spend a week making significant progress on a research project and foster innovation in the application of mathematical, statistical, and computational methods in the resolution of significant problems in the biosciences with the goal of publishing research results in a collected volume. The workshop will also include career development lunchtime sessions. Women and those with underrepresented gender identities are encouraged to apply.

Deep learning is an emerging subfield in machine learning that has in recent years achieved state-of-the-art performance in image classification, object detection, segmentation, time series prediction and speech recognition to name a few. The Northern Lights Deep Learning Conference (NLDL) gathers researchers both on a national and international level to exchange ideas, encourage collaborations and present cutting-edge research.

The NLDL Winter School consists of tutorials by experts in the field and is co-hosted by NORA as part of the NORA Research School. A preliminary program will be made available soon. Getting formal ECTS Credits: UiT The Arctic University of Norway awards 5 ECTS for the Winter School for students who register formally for the course at UiT before the 1st of November (for credits, there is a limit of 40 students). To obtain credits, participants are required to present an ongoing research project (poster presentation) as part of the winter school and complete a home exam afterward. The NLDL Winter School will consist of tutorials from leading experts in the machine learning field. Below we present each tutorial, followed by the tutorial speakers: - Tutorial 1: Aleatoric and Epistemic Uncertainty in Statistics and Machine Learning by Willem Waegeman from Ghent University - Tutorial 2: Responsible and Explainable Artificial Intelligence by Leila Methnani and Virginia Dignum from Umeå University - Tutorial 3: Large Language Models Under the Hood by Andrey Kutuzov, Egil Rønningstad and David Samuel from the Language Technology Group, University of Oslo - Tutorial 3: Multimodal Machine Learning by Michael Kampfmeyer and Kristoffer Wickstrøm from UiT - The Arctic University of Norway.