Events

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.

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 International Conference on Mathematics and Applications (ICMA-2024) aims to bring together academicians, students, and researchers from the fields of pure and applied mathematics as well as from a wide range of other disciplines where mathematics finds applications. The primary goal of the conference is to provide a forum for research experts, early career faculty and students from all over the world to interact, exchange ideas and experiences, and discuss topics of mutual interests in complex and widely applied areas of mathematical sciences. The conference also offers participants an opportunity to be aware of the latest developments and recent trends in the fields and introduce early career participants to research and other mathematical activities.

The *International School on Fractional Calculus and Systems Theory* aims to provide a comprehensive exploration of fractional calculus theory and its applications across a wide range of domains. Participants will engage with the core principles of fractional calculus, such as fractional differentiation, integration, and differential equations, gaining a thorough understanding of the mathematical foundations behind these concepts. Through lectures and specialized courses, attendees will delve into the diverse applications where fractional calculus is instrumental. These include fields like physics, engineering, biology, finance, signal processing, and beyond. The school will also emphasize interdisciplinary collaboration, encouraging participants to share ideas and methodologies across various disciplines. By discussing the latest research developments and challenges in fractional calculus, the event aims to inspire innovative thinking and push the boundaries of the field. In addition, the school will provide ample networking opportunities, allowing participants to connect with leading experts, mentors, and peers. Early-career researchers and students will benefit from mentorship and guidance, helping them to hone their professional skills and broaden their research perspectives. In conclusion, the *International School on Fractional Calculus and Systems Theory* is dedicated to offering an in-depth exploration of fractional calculus theory and its applications, fostering interdisciplinary collaboration, advancing research in the field, and supporting the professional growth of its participants.

In recent years, there has been a significant increase in the interaction between harmonic analysis and convex geometry, leading to solutions for several longstanding open problems, discovery of new phenomena, as well as many new intriguing open questions. These connections were studied during the Fall 2022 ICERM semester on “Harmonic Analysis and Convexity”. The objective of this workshop is to revisit and review the results produced during the semester and the subsequent year. The primary areas of focus for the workshop will encompass: The Fourier approach to Geometric Tomography; Volume and Duality; Bellman technique for extremal problems in harmonic analysis; Convexity of solutions to Hamilton–Jacobi–Bellman equations; as well as numerical computations and computer-assisted proofs: Exploring the use of computational methods for theoretical aspects, including optimal algorithms, as well as practical implementation.

The objective of the Interdisciplinary Conference on Mathematics, Science, and Linguistics is to foster collaboration and the exchange of ideas among participants from various disciplines. It aims to explore the intersection of these fields and promote innovative approaches to addressing complex challenges and advancing knowledge. The conference provides a platform for experts to present their latest research findings, theories, and methodologies, facilitating the sharing of knowledge and encouraging discussions on interdisciplinary topics. It fosters collaboration and research across disciplines, offering an opportunity for participants to interact, exchange ideas, and explore potential collaborations that can lead to advancements. By bringing together experts from different fields, the conference facilitates the cross-pollination of ideas and perspectives, leading to new insights and solutions to existing problems. It seeks to bridge gaps between disciplines by exploring commonalities and synergies among them, encouraging participants to look beyond boundaries and discover connections that enhance understanding and problem-solving capabilities. Moreover, the conference aims to explore the practical applications and societal implications of interdisciplinary research, fostering discussions on how it can contribute to addressing societal challenges, advancing technology, and shaping policy

The aim of MAMM 2024 is to bring together leading international experts to discuss the latest trends and advances in Matrix Analysis and Mathematical Modelling. The conference covers all domains of science and engineering, where real-world problems are modelled using mathematical modelling.

This seminar will provide an overview of the recent developments and new horizons of the burgeoning field found at the intersection of control theory and machine learning. This perspective, in which deep neural networks are viewed as controlled flow maps, has shown to be fruitful in analyzing a myriad of problems of interest in machine learning, including interpolation and generalization properties of deep neural networks, or normalizing flows for generative modeling, as well as clustering properties for Transformers – to name a few. The week-long seminar will address the topic from different points of view taking in particular recent developments in machine learning into account. Deadline for application: 1 September 2024