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Jørgen Afseth SugarView profile

Associate Professor

• Neuroscience
• Episodic Memory
• Neurophysiology
• +5 more

Jørgen Afseth Sugar is an Associate Professor at the Faculty of Medicine , University of Oslo, specializing in Neuroscience and Neurophysiology . His research focuses on understanding the neural mechanisms underlying episodic memory formation and retrieval in humans, utilizing clinical epilepsy patient data through intracranial recordings. Education: Medicine (NTNU), PhD in Neuroanatomy (NTNU), Postdoc in Neurophysiology (NTNU) Current projects: Brainsight , Memory maps , Neurophysiological Mechanisms of Human Auditory Predictions (AudioPred) His work bridges Neuro-Surgery and Neurocell Signaling , emphasizing neuronal network oscillations in memory processes. Collaborations include FRONT neurolab, RITMO, and the Voytek lab. Publication trends reveal a focus on hippocampal connectivity , retrosplenial cortex interactions , and temporal coding across rodent models and human studies.

• Electronics
• Signal Processing
• Digital Education
• +3 more

Ola Jetlund is an Associate Professor at the Faculty of Technology, Art and Design, Department of Mechanical, Electrical and Chemical Engineering, Oslo Metropolitan University. His work focuses on electronics, signal processing, and digital education. Education: Doctor Engineer (PhD) in Electronics and Signal Processing Research Interests: Jetlund's research spans adaptive multimedia streaming, channel coding optimization, and digital pedagogy. He contributes to the ADvanced hEalth intelligence and brain-insPired Technologies (ADEPT) research group. Publication Trends: His recent works emphasize community-based digital teaching methods and historical contributions to adaptive coded modulation schemes for wireless networks. Administrative Roles: Currently serves as Head of Studies in Electronics and Electrical Engineering.

• Chemistry Education
• Biochemistry
• Soft Matter
• +3 more

Andreas Haraldsrud serves as an Associate Professor in the Department of Physics at the University of Oslo, with primary affiliations in the Center for Computational Science Education (CCSE) and Chemistry Didactics Section. His academic profile bridges chemistry education research and nanobiochemistry, focusing on interdisciplinary computational approaches. His research centers on Chemistry Education, investigating cognitive models and meaning-making processes in physical chemistry and biochemistry through computer simulations and artificial intelligence. Concurrently, his Nanobiochemistry work examines soft materials like lipids and proteins using laboratory techniques (confocal microscopy, TIRF, microfluidics) combined with molecular dynamics and machine learning simulations. Teaching responsibilities include Programming for Chemists, Biochemistry, and Science Programming courses for school teachers. Recent publications demonstrate a cohesive research trajectory where computational methods serve as conceptual catalysts in chemistry education, with emerging focus on programming assessment tools and simulation-based sensemaking. His work consistently integrates educational theory with practical computational applications across chemistry and physics domains. Haraldsrud actively contributes to the Center for Computational Science Education and Chemistry Didactics Section, collaborating on projects that develop computational pedagogy frameworks and investigate cognitive processes in science learning through integrated laboratory-simulation methodologies.

• Medical Artificial Intelligence
• Machine Learning
• Clinical Data Analysis
• +2 more

Karl Øyvind Mikalsen is an Associate Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway. He is also a division leader at the Center for Patient-Near Artificial Intelligence (SPKI) at University Hospital of North Norway (UNN). His research focuses on applying artificial intelligence (AI) to healthcare, particularly in analyzing medical images, speech, and text using language models and machine learning, with applications such as anonymizing patient records and evaluating AI tools for clinical use. Research Interests: Mikalsen’s work bridges medical informatics and computer science, emphasizing explainable AI, clinical time series analysis, and medical image processing. He collaborates with UiT and UNN to integrate AI into healthcare systems. Recent Publications: His studies span AI-driven clinical coding, breast cancer detection via mammography, surgical infection prediction, and self-supervised representation learning for medical data. Key methodologies include transformer models, kernel methods for time series, and uncertainty-aware ensembles. Collaborations: Mikalsen works with researchers such as Robert Jenssen, Michael Kampffmeyer, and Arthur Revhaug, focusing on clinically relevant AI applications in Scandinavia.

• Scientific Computing
• Finite Element Methods
• Mixed-Dimensional Coupling
• +4 more

Cécile Daversin-Catty is a Research Scientist at Simula Research Laboratory in the Department of Numerical Analysis and Scientific Computing. Her work bridges computational mathematics and biomedical engineering through advanced finite element methods. Education: PhD in Applied Mathematics (2016, Université de Strasbourg), thesis titled 'Reduced basis method applied to large non-linear multi-physics problems: application to high field magnets design' Her research focuses on scientific computing , finite element methods , and mixed-dimensional coupling , with applications spanning fluid mechanics , cardiac electromechanics , and neurovascular modeling . Recent publications highlight her expertise in computational fluid dynamics for cardiac flow simulations, development of FEniCS-based tools for mixed-domain problems, and modeling of perivascular networks. Key trends in her work include: Development of robust numerical frameworks for multi-physics problems Advancing cardiac electromechanical modeling for heart failure studies Exploring glymphatic system dynamics via mixed-dimensional finite elements Software innovation in computational science tools She collaborates extensively with researchers across computational cardiology and neuroscience, contributing to open-source platforms like FEniCS.