Arctic AI Day 2025

8:30 Registration and coffee

9.00 Welcome: Dean Jukka Riekki, Faculty of Information Technology and Electrical Engineering, University of Oulu

9:15–11:15 Keynote session

Keynote: Vice President Laura Piila, Optomed

Keynote: Director Aleksi Kallio, CSC

Keynote: Professor Sanna Järvelä, University of Oulu

11:15 Poster teaser

11:30–12:30 Lunch & Posters

12.30–14.00 Session 1

ELLIS Institute Finland presentation – Professor Simo Saarakkala and ELLIS PI Associate Professor Andrew Cropper

Assistant Professor Matti Pouke, University of Oulu

Senior Research Fellow Neslihan Yalcin Bayramoglu, University of Oulu

Professor Netta Iivari, University of Oulu

14:00–14:30 Coffee & Posters

14.30–16.00 Session 2

Research Professor Tuomo Alasoini, Finnish Institute of Occupational Health

Postdoctoral Researcher Farid Hamzehaghdam, University of Oulu

Assistant Professor Miguel Caro, Aalto University

University Lecturer Satu Tamminen, University of Oulu

16:00–16:30 Posters

16:30–19:00 Cocktails

Sanna Järvelä, Professor, University of Oulu
Sanna Järvelä is Professor of Learning Sciences at the University of Oulu, where she leads the Learning and Educational Technology Research Lab. Her multidisciplinary research focuses on self-regulated learning, computer-supported collaborative learning, and the transformative role of AI in education. She is internationally recognized for advancing theories of socially shared regulation and for pioneering multimodal research methods. She leads the Hybrid Intelligence research programme, co-leads the CELLA - Center for Learning and Living with AI, and serves on the OECD PISA 2025 “Learning in the Digital World” expert team. Järvelä is past president of EARLI, a Francqui Chair laureate (Ghent University), Learning Sciences Fellow and a member of the Finnish Academy of Science and Letters. In 2024, she was named among the world’s top 2% of scientists by Stanford University.

Laura Piila, Vice President, Optomed
Optomed - Revolutionizing System Health Diagnostics with Handheld Devices
Laura Piila leads Optomed’s Devices Business with 15 years of experience in global HealthTech. Optomed is the leading solution provider for AI integrated handheld retinal imaging. Optomed is publicly listed in Nasdaq Helsinki and operates globally in over 60 countries. Laura has built with the team at Optomed an offering of state-of-the-art handheld fundus cameras and AI solutions that can give insights into systemic health and disease. In 2024 Optomed gained as the first handheld camera provider FDA clearance for an AI integrated autonomous Diabetic Retinopathy detection solution. Laura holds a Master's Degree from Oulu University on Industrial Engineering and Management. Prior to Optomed in 2010 Laura lived in China and worked for Nokia.

Aleksi Kallio, Director, LUMI AI Factory Service Center
Responsible AI can be a competitive advantage
Aleksi Kallio is the director of LUMI AI Factory Service Center. He holds a PhD in computer science (data science) and currently oversees services on AI related training, consultation, and expert support as well as development of computing and data software environments for AI challenges.

Satu Tamminen, University Lecturer, University of Oulu
From data to decisions: the role of responsible AI in evidence-based decision making
Senior Researcher Satu Tamminen is a deputy leader of BISG research unit and a CSE Degree programme director. She received her PhD degree from University of Oulu in 2014 (thesis title: “Modelling the rejection probability of a quality test consisting of multiple measurements”). Her research interests cover evidence-based decision support systems, XAI and understandable machine learning. She has developed smart decision support tools for industry to enable data-driven manufacturing and process monitoring, as well as for the medical field to support individualized treatment pathways and health monitoring.

Neslihan Bayramoglu, Senior Research Fellow, University of Oulu
Trusting Machines with Our Health
Neslihan Bayramoglu is a Senior Research Fellow at the Research Unit of Health Sciences and Technology, University of Oulu, Finland. Granted the title of Docent in Medical Imaging in 2022, she is also a member of the European Lab for Learning and Intelligent Systems (ELLIS), she conducts research in artificial intelligence, deep learning, and computer vision, with applications in medical image analysis, computer-assisted diagnosis, facial expression recognition, image processing, shape analysis, segmentation, classification, and 3D image retrieval. Her work focuses on developing reliable, interpretable, and clinically impactful AI systems for healthcare.

Netta Iivari, Professor, University of Oulu
Fostering Transformative Agency of Children in the Age of AI
Netta Iivari is a Professor in Information Systems and research unit leader of INTERACT research unit in University of Oulu. She has background in Information Systems, Human Computer Interaction and Cultural Anthropology. Her long lasting research interest concerns understanding and strengthening children’s participation in shaping and making their digital futures. She has explored participatory design, critical design, speculative design, empowerment, inclusion, ethics, and criticality in collaboration with children, working in the context of their computing education. Recently, she has focused on fostering transformative agency of children with emerging technologies for social good, exploring for example artificial intelligence (AI) in collaboration with children, children engaging in critical analysis and design of AI futures.

Miguel Caro, Assistant Professor, Aalto University
Atomistic machine learning for materials modeling
Miguel Caro is Assistant Professor at the Department of Chemistry and Materials Science at Aalto University, where he leads the Data-driven Atomistic Simulation research group. His research interests are the development of methods and software to extend the accuracy and performance of materials modeling beyond the state of the art, using a combination of physical models and machine learning techniques. These methods are then used to study important and interesting materials for various applications, from energy storage to electrocatalysis, and to bridge the gap between experiment and simulation.

Tuomo Alasoini, Research Professor, Finnish Institute of Occupational Health
Digitalization as part of the green transition in working life
Tuomo Alasoini works as Research Professor at the Finnish Institute of Occupational Health, with a special focus on digitalization and workplace change. His current research themes include hybrid work, generative artificial intelligence at work, the twin transition (digital and green) as a change in working life, and organizational resilience. He is PhD in social sciences (1990) and industrial engineering and management (2016), and Adjunct Professor of sociology at the University of Helsinki. Before joining the Finnish Institute of Occupational Health, he worked at Tekes, the Ministry of Labour, and the Ministry of Social Affairs and Health in various positions in working life and innovation research and development. In 1996-2010, he worked as Project Manager in the national workplace development programmes TYKE and TYKES.

Andrew Cropper, Associate Professor at University of Helsinki and Principal Investigator at ELLIS Institute Finland
Learning rules that obey your rules
Andrew Cropper is an Associate Professor at the University of Helsinki and a Principal Investigator at ELLIS Institute Finland. His research focuses on combining logical reasoning with machine learning to develop AI systems that learn interpretable rules. Previously, he led a research group at the University of Oxford for eight years and completed his PhD at Imperial College London.

Register for the event here.

Please register by October 29th. Registration includes complimentary coffee breaks, lunch and cocktail event. The event is free for participants, but registration is needed to join as there are limited places available.

During the event, it is possible for you to present your research work as a poster and/or demonstration. The presentations can include ongoing work or already published results. The topics can cover, but are not limited to:

• development of AI methods for your own research
• applying AI methods in your own research
• research ideas to start discussion, such as, recognizing data sources that could be useful to model phenomena of interest
• propose your own topic!

To participate the Arctic AI Day poster session, you need to prepare a 2-page poster paper (before event, peer reviewed) and a poster for the event (event day 6th Nov). You may use your own template for the poster paper.

• Poster format: Posters should be visually engaging, clearly presenting your research objectives, methodologies, and outcomes. Please use the University of Oulu poster template.
• Submission deadline: 27.10.2025 for the posters
• Notification of acceptance: 29.10.2025

If you’re a student (of any level), you can gain 2 credits from the event. Please note that the credits are not available if you have the credits from the previous Arctic AI Days events.

To perform this course, participate in the Arctic AI Days and return a report to the course instructor (ella.peltonen@oulu.fi). The report is evaluated as lessons learned and based on the thoughts and ideas you got during the event.

The report structure should be the following:

• Summary of at least three talks you found engaging and your thoughts based on the research questions presented
• Describe three posters or demos you visited and why you found them engaging or interesting. Please note that if you presented a poster or demo by yourself, there is no need to write this part of the report. Please indicate your poster/demo name in your submission.
• In conclusion, what are your lessons learned and future research or project ideas? How can you utilize your learning in your professional career? Critical thinking is encouraged.

The report is supposed to be four pages long (two pages if the student presented their poster/demo) and returned in a PDF format. The course is graded pass/fail.