Events

This workshop showcases emerging frontiers in ROM (reduced order modeling) by bringing together researchers whose core interests lie in model reduction and approximation theory, but who have also explored and developed novel methods that utilize various aspects of statistical learning and data science. Topics of the workshop will include: 1) new mathematical and computational nonlinear ROM formulations required for prediction of transport phenomena, 2) new model reduction and meta-learning opportunities necessitated by the prevalence of large neural network models, 3) new paradigms for ROM capable of attacking parameter inference and ill-posed inverse problems, 4) new frontiers in the automated learning of latent representations due to the availability of computationally feasible optimization in statistical and machine learning, 5) an appropriate computational balancing of observational or experimental data with simulation-based models and ROM that would lead to the usage of ROM in digital twin-scale applications, and 6) challenges and new developments in ROM that aims to preserve inherent physical structure of the underlying dynamics.

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.

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.

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.

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.

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

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.