2025 NAIS Symposium

The 2025 Symposium of the Norwegian AI Society will take place from June 18 to June 19 at the Arctic University of Norway in Tromsø. The symposium aims at bringing together researchers and practitioners in the field of Artificial Intelligence from Norway and Scandinavia to present on-going work and discuss the future directions of AI. With the symposium NAIS provides a forum for networking among researchers as well building links with related research fields, practitioners and businesses.

Dates

Submission Deadline: April 11, 2025 April 18, 2025

Notification Date: May 14, 2025 May 16, 2025

Camera-Ready Deadline: May 28, 2025

Registration Deadline: June 9, 2025

Symposium date: June 18 - 19, 2025

Call for Papers

Contributions are welcome from all areas of Artificial Intelligence and topics of interest include but are not limited to:

  • Machine Learning,
  • Knowledge Representation,
  • Robotics
  • Planning and Scheduling
  • Natural Language Processing,
  • Computer Vision,
  • Search Algorithms,
  • Multi-Agent-Systems,
  • Industrial Applications, and
  • Philosophical and Ethical Foundations.

Submission Instructions

We welcome and encourage the submission of high-quality papers that have not already been published elsewhere. We welcome submissions on all aspects of AI. Submissions will be subject to single-blind peer review by the programme committee. They will be evaluated based on relevance, clarity, significance, originality, soundness, reproducibility, scholarship, and quality of presentation.

Generative AI: The use of AI systems to generate text for inclusion in a submission is only allowed if its role is properly documented in the manuscript (e.g., when reporting on experiments on such systems). However, the use of AI-powered systems to assist with the polishing of human-authored text is permissible.

Full papers should be written in English, formatted according to the CEUR Workshop Proceedings style, and not exceed 10 pages plus bibliography. Author's instructions, are available in this Overleaf template. We also invite submission of position papers. They must not exceed 5 pages and they should relate to an ongoing research, but can also present work that has been presented elsewhere. Please state clearly where the paper has been presented before. Position papers will be presented by the authors alongside regular papers. Selected papers will be invited for an oral or poster presentation. All papers containing original work will be included in the proceedings - resubmissions will be mentioned in the preface.

We highly encourage early stage researchers and PhD students to submit their work.

Paper submission is electronic via CMT at: https://cmt3.research.microsoft.com/NAIS2025/.

The proceedings of the symposium will be published in the CEUR Free Open-Access Proceedings for Computer Science Workshops. These proceedings are approved as a scientific publication channel at level 1 in Norway.

Program Outline

Time Presentation
Wednesday, June 18, 2025
11:00 - 12:00 Registration and Light Lunch
12:00 - 12:15 Welcome and Introduction
Robert Jenssen and Kerstin Bach
12:15 - 13:00 Keynote: Elisabeth Wetzer (UiT The Arctic University of Norway)
Representation Learning for Multimodal Image Registration and Retrieval
Abstract: Combined information from different imaging modalities enables an integral view of a specimen, offering complementary information about a diverse variety of its properties. To efficiently utilize such heterogeneous information, spatial correspondence between acquired images has to be established. The process is referred to as image registration and is highly challenging due to complexity, size, and variety of multimodal biomedical image data. In this talk, I will give an overview of commonly used techniques from classic image analysis and learning-based approaches, their limitations and how to efficiently combine tools from both worlds, particularly when very small training data is available.
Biography: Elisabeth Wetzer is associate professor in machine learning in the Machine Learning Group at UiT The Arctic University of Norway and part of the SFI Visual Intelligence and SFF Integreat. Prior to joining UiT in 2024, she did her PhD in Image Processing at Uppsala University in Sweden and worked as a bioinformatician in translational studies on the tumor microenvironment at Karolinska Institute, Sweden. Her research focuses on developing deep learning methodology, with particular focus on biomedical application. As most of these applications suffer from small training data or limited access to labels, she works on methodology which exploits domain context, symmetries in the data, or constraints which may be drawn from other modalities or classic image processing techniques. Show biography ...
13:00 - 16:30 Technical Session I
16:30 - 17:00 Dissertation Award 2024 The winner of the Dissertation Award 2024 (Dissertation Award 2024) will receive the certificate and give a presentation of the PhD project.
17:00 - 18:00 NAIS General Assembly (chair: Odd Erik Gundersen)
20:00 Symposium Dinner
Thursday, June 19, 2025
9:00-10:30 Technical Session II
10:00 - 10:30 Coffee Break and Poster Session
10:30 - 11:30 Technical Session III
11:30 - 12:15 Keynote: Steve Marron (University of North Carolina)
Data Integration Via Analysis of Subspaces (DIVAS)
Abstract: A major challenge in the age of Big Data is the integration of disparate data types into a single data analysis. That is tackled here in the context of data blocks measured on a common set of experimental cases. Joint variation is defined in terms of modes of variation having identical scores across data blocks. That allows mathematically rigorous formulation of individual variation within each data block in terms of individual modes. These are mathematically defined through modes of variation with common scores. DIVAS improves earlier methods using a novel random direction approach to statistical inference, and by treating partially shared blocks. Usefulness is illustrated using mortality, cancer and neuroimaging data sets.
Biography: J. S. (Steve) Marron is the Amos Hawley Distinguished Professor of Statistics and Operations Research, Professor of Data Science and Society, Professor of Biostatistics and Adjunct Professor of Computer Science at the University of North Carolina, Chapel Hill. His research lies in many areas of statistics, data science and machine learning, with a special emphasis on gaining simultaneous insights from very diverse data types, including genomics, genetics, imaging and demographics. He enjoys using deep concepts from diverse mathematical areas including geometry and topology in novel data analyses. Marron’s PhD was earned at UCLA in 1982, under the supervision of Charles J. Stone. Early in his career he worked on the asymptotics of nonparametric curve estimation, with an emphasis on smoothing parameter selection, jointly with many research leaders including Peter Hall and Wolfgang Haerdle. He moved into interdisciplinary research with some foundational work on “code decay” in software engineering, and the invention of fundamental machine learning methods for analyzing gene expression in cancer research. Marron coined the term “Object Oriented Data Analysis”, which is a framework for addressing the coming trend for data to not only get big, but also to get complex. OODA draws deeply from diverse areas of mathematics including geometry (critical for e.g. shapes as data points) and topology (shown useful for tree / graph data). These ideas have motivated current research on data integration, that has been feeding into continuing cancer collaborations, as well as research on osteo-arthritis, including analysis of human movement data using amplitude-phase decomposition ideas. In addition to a large amount of reviewing and associate editing, and many NSF Grants, Marron was a founding Associate Director of SAMSI. He also has an unusually strong mentoring record with 60 graduated PhD students. Show biography ...
12:15 - 13:00 Networking Lunch
13:15 - 14:00 Public Keynote: Keith Downing (Norwegian University of Science and Technology (NTNU))
Prediction at the core of both natural and artificial intelligence
Abstract: Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction, across a wide range of temporal and spatial scales. In this lecture, I will investigate the origins and anatomy of natural and artificial neural networks as predictive mechanisms. In addition, links to nascent large-language models (LLMs) will be hard to avoid, since their underlying intelligence appears to emerge from performing simple predictive tasks, albeit with complex attention-based representations and massive transformer architectures.
Biography: Keith Downing is a professor of Artificial Intelligence (AI) at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. He is also an Affiliate Scholar in the Neuroscience Department of Oberlin College. His main interests, experiences and publications are at the intersection of AI, Artificial Life (ALife), and Computational Neuroscience. He spends most of his time either a) teaching several of NTNU’s AI classes, b) programming neural networks and evolutionary algorithms, or c) lecturing to the general public on technical and ethical aspects of AI. Keith's AI and ALife research culminated in two books: "Intelligence Emerging" (MIT Press, 2015) and "Gradient Expectations" (MIT Press, 2023). Show biography ...

Technical papers will have a 20 min presentation, please leave 3-5 min for a discussion.

Registration

The registration for the symposium is now open: registration page

Fees

Student (Master, PhD): 1500 NOK

Regular Participant: 2300 NOK

Fees include participation, symposium dinner as well as coffee and lunch breaks. During the registration, members of the Norwegian AI society (see details here) will receive a NOK 500 discount during the registration process. Please note that there will be no possibility to refund registration fees if you register for the event and cannot attend.

Organizers

Program Committee Members

  • Changkyu Choi, UiT The Arctic University of Norway
  • Laurence Dierickx, University of Bergen
  • Fred Godtliebsen, UiT The Artic University of Norway
  • Odd Erik Gundersen, Norwegian University of Science and Technology
  • Nektaria Kaloudi, SINTEF Digital
  • Michael Kampffmeyer, UiT The Artic University of Norway
  • Benjamin Kille, NTNU
  • Helge Langseth, Norwegian University of Science and Technology
  • Bjørn Magnus Mathisen, SINTEF
  • Ole Jakob Mengshoel, Norwegian University of Science and Technology
  • Andreas Lothe Opdahl, University of Bergen
  • Kiran Raja, Norwegian University of Science and Technology
  • Adín Ramírez Rivera, University of Oslo
  • Signe Riemer-Sørensen, SINTEF
  • Arnt Salberg, Norwegian Computing Center
  • Bjørnar Tessem, University of Bergen
  • Samia Touileb, University of Bergen
  • Valeria Vitelli, University of Oslo
  • Elisabeth Wetzer, UiT The Arctic University of Norway
  • Kristoffer Wickstrøm, UiT The Arctic University of Norway
  • Zhirong Yang, Norwegian University of Science and Technology

Venue

The symposium will take place at The Arctic University of Norway in Tromsø. UiT The Arctic University is a medium-sized research university that contributes to knowledge-based development at the regional, national and international level. UiT is the northernmost university of the world.

The 2025 NAIS Symposium will take place at the following venues at UiT in Tromsø: the June 18th program will be at Auditorium 3 (Teorifagbygget hus 6) and the June 19th program will be at Auditorium 2 (Teorifagbygget hus 1)

Picture: Jakob Bjørvig Henriksen
Picture: Kjetil Rydland
Picture: Jakob Bjørvig Henriksen

Accomodation

Tromsø is a popular place and hotels sell out easily. Make sure you book your hotel early. Most Norwegian organisations have agreements with hotel chains for fixed prices. Please make sure to check the options with your organisation.
A selection of hotels in the center of Tromsø are the following:

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