Daniel M. Roy is a Full Professor at the University of Toronto, with cross-appointments in the Department of Computer Science, Department of Statistical Sciences, and Department of Electrical and Computer Engineering. He serves as Research Director at the Vector Institute and holds the CIFAR Canada AI Chair. Research Focus: Foundational principles of prediction, inference, and decision-making under uncertainty across machine learning, statistics, mathematical logic, applied probability, and computer science. Scientific Contributions: Key work in learning theory, statistical network analysis, probabilistic programming, and information-theoretic frameworks for generalization. Awards: ICML 2024 Best Paper Award for "Information Complexity of Stochastic Convex Optimization" and promotion to Full Professor in 2024. Student Advising: Actively mentors Ph.D. candidates and postdoctoral researchers with strong quantitative backgrounds, particularly at the intersection of machine learning, statistics, and computer science. Email: daniel.roy@utoronto.ca
Inga Berre is a Professor at the Department of Mathematics, University of Bergen, and serves as Director of the Center for Modeling of Coupled Subsurface Dynamics (CSD). She leads the Porous Media Research Group and was appointed Argyris Visiting Professor at the University of Stuttgart's SimTech Cluster of Excellence in 2023. Research Interests: Mathematical modeling, partial differential equations, numerical methods for coupled thermo-hydro-mechanical-chemical processes in subsurface systems, and fault reactivation induced by injection/production. Scientific Leadership: Member of SIAM Council (2022-2027), Chair of SIAM GS activity group (2021-2022), Co-Chair of SET-Plan Deep Geothermal Implementation Working Group (2019-2021), and Chair of the Joint Program Geothermal, European Energy Research Alliance (2018-2021). Awards: 2011 Meltzer Award for Young Researchers. Advisory Roles: Member of Scientific Advisory Boards for GFZ (2024-2027) and SFB1313 (2018-), among others. Teaching: Developed courses on calculus, functional analysis, mathematical modeling, and numerical methods at the Bergen Summer Research School.
Behzad Alaei serves as an Associate Professor in the Section for Study of Sedimentary Basins within the Department of Geosciences at the University of Oslo's Faculty of Mathematics and Natural Sciences. His office is located in room K38 of the Geology Building at Sem Sælands vei 1, 0371 Oslo, with a professional email contact at behzad.alaei@geo.uio.no. Dr. Alaei maintains an active research profile with publications spanning from 2005 to the present, demonstrating his ongoing contributions to geological sciences. Dr. Alaei's research spans multiple critical areas within structural geology and sedimentary basin analysis, with particular expertise in fault zone architecture, seismic interpretation techniques, and CO2 storage site assessment. His work bridges theoretical geological concepts with practical applications in petroleum geology and carbon sequestration. A significant portion of his research focuses on the Norwegian Barents Sea region, where he has conducted extensive studies on normal fault systems and their geometric characteristics. His recent work increasingly integrates machine learning and deep learning approaches with traditional geological analysis, reflecting the evolving nature of geoscience research methodology. The analysis of Dr. Alaei's publication record from 2018-2024 reveals a strong thematic continuity in fault characterization research, with progressive incorporation of advanced computational methods. Early publications focused primarily on traditional structural analysis of fault systems in sedimentary basins, while more recent work demonstrates increasing integration of machine learning techniques for fault detection and characterization. A notable trend is the application of these geological insights to practical challenges in carbon capture and storage, particularly regarding fault risk assessment for CO2 storage sites in the North Sea region. His collaborative work with Anita Torabi appears consistently throughout this period, suggesting a strong research partnership. Dr. Alaei maintains an active research program with multiple ongoing projects related to sedimentary basin analysis and fault characterization. His work appears to involve significant collaboration with both academic and industry partners, particularly in the context of CO2 storage research. While specific grant details aren't provided in the available information, his consistent publication record across multiple high-impact journals suggests successful funding of his research activities over the past two decades.
Ronny Scherer is Center Director and Professor at CEMO (Center for Educational Measurement) and Deputy Director at CREATE (Center for Research on Equality in Education) at the University of Oslo's Faculty of Educational Sciences. His work bridges educational measurement, assessment, and evaluation with a focus on research syntheses and complex sampling surveys. Dr. Scherer's research spans two interconnected domains: substantive areas including digital divides, equity and equality in education, and measurement of complex cognitive skills (such as complex problem solving, adaptability, computational thinking, and executive functioning); and methodological areas focusing on advanced meta-analytic techniques, multilevel structural equation modeling, and spatial analysis of complex survey data. His work frequently utilizes international large-scale assessment data from PISA, ICILS, TIMSS, PIRLS, PIAAC, and TALIS. His publication record demonstrates a clear trajectory toward increasingly sophisticated meta-analytic approaches, with recent work focusing on second-order meta-analyses, AI-assisted screening methods, and advanced techniques for handling complex survey data. His research consistently addresses critical educational challenges related to equity, digital literacy, and measurement of 21st century skills. Dr. Scherer has secured significant research funding for projects including ARISE (Academic resilience in mathematics and science among vulnerable students), DiDiRes (Digital inequalities in education), and ADAPT21 (Educational assessments of the 21st century: Measuring and understanding students' adaptability in complex problem solving situations). Co-director of CREATE (Centre for Research on Equality in Education) since 2023 Professor of Educational Assessment and Measurement at CEMO since 2019 Extensive experience with international large-scale assessments including ICILS, TALIS, and PIAAC As an educator, Dr. Scherer teaches advanced courses in measurement models, multilevel models, meta-analysis, and equity in education. He actively supervises graduate students interested in his research areas and has developed numerous workshops on structural equation modeling and meta-analytic methods for international audiences.
Bettina Sandgathe Husebø is a Professor and Head of the Center for Geriatric and Nursing Home Medicine at the Department of Global Health and Community Medicine, Faculty of Medicine, University of Bergen (UiB). She also serves as Innovation Manager at IGS, UiB since 2019. Her extensive career spans clinical practice, research, and leadership roles in geriatric and palliative care. Dr. Husebø completed her medical education at the University of Bonn, Germany in 1988, followed by specialization in Anaesthesiology and Intensive Care in 1995. Her Norwegian qualifications include Medical Specialization in Palliative Medicine (2012) and Nursing Home Medicine (2014) from UiB, along with a PhD from the Faculty of Medicine Dentistry at UiB in 2008. She further enhanced her expertise with a Postgraduate Safety, Quality, Informatics and Leadership (SQIL) Program from Harvard University in 2021. Her research focuses on critical geriatric issues including pain assessment and management in dementia patients, behavioral disturbances in dementia, palliative care in nursing homes, and digital phenotyping applications for elderly care. She has pioneered work on the relationship between pain, agitation, and neuropsychiatric symptoms in dementia patients, particularly through the COSMOS trial and LIVE@Home.Path study. Her recent publications (2023-2025) demonstrate a strong emphasis on digital health solutions for dementia care, with particular focus on activity monitoring, pain assessment through technology, and community-based interventions for aging populations. Her work bridges clinical geriatrics, technology innovation, and patient-centered care models. Among her notable recognitions are the National Dementia Award by His Majesty King Harald of Norway (2022) and multiple awards for research excellence in pain management and palliative care. Her work has significantly influenced Norwegian healthcare policy regarding dementia care and end-of-life practices. As an educator, she lectures in English, German, and Norwegian on dementia, pain in dementia, innovation technologies for older adults, symptom management at end-of-life, systematic medication review, and advance care planning. She has received teaching awards including 'Teacher of the Year' from the Faculty of Medicine and Dentistry at UiB. Dr. Husebø leads the Center for Geriatric and Nursing Home Medicine (SEFAS) and has been instrumental in establishing Norway's first palliative care ward in a nursing home. Her research group focuses on translating evidence into practice to improve quality of life for elderly patients, particularly those with dementia.
Eivind Rudjord Hillesund is an Associate Professor in the Department of Mathematical Sciences at the University of Agder. He holds qualifications in teaching mathematics and physics from the University of Oslo's lektorprogrammet and defended his doctoral thesis in January 2021 on engineering students' use of learning resources in mathematics courses. His teaching focuses on statistics courses within GLU programs and assignments in EVU courses at UiA since 2019. Research Interests: Resource use and decision-making in undergraduate mathematics education Educational strategies for engineering students Development of tools for tracking student resource utilization Publications include studies on resource systems analysis, didactical purposes of resources, and data collection methodologies. His work emphasizes improving understanding of how students interact with learning materials in STEM fields.
John Rognes is a Professor at the Department of Mathematics , University of Oslo, specializing in Algebraic Topology, Algebraic K-Theory, and Geometric Topology. His research bridges number theory and homotopy theory, with a focus on structured ring spectra and topological modular forms. Education : International Baccalaureate (1984), Cand. Mag. in Mathematics (1985), Princeton MA (1987), and PhD (1990) under Gunnar Carlsson. Positions : Professor at UiO since 1998, Visiting roles at Stanford (1996), Chicago (1996), and Bonn (2005-2006). His research areas include Algebraic K-Theory , Stable Homotopy Theory , Topological Cyclic Homology , and Motivic Homotopy . Articles highlight work on Adams spectral sequences, redshift phenomena, Segal conjectures, and topological Hochschild homology of modular forms. Scientific awards include the 1999 Professor Ingerid Dal and Ulrikke Greve Dals prize, Fulbright-Hays Fellowship, and multiple grants from the Research Council of Norway (YFF, SUPREMA). He supervised 18 Master’s and 9 PhD students, including Paul Arne Østvær, Vigleik Angeltveit, and Alice Hedenlund. Leadership : Chairman of the Abel Committee (2014-2018), Program Leader for Master programs in Mathematics (2021-2024), and organizer of international symposia. Grants : YFF program 'Brave new rings' (7.1 MNOK), RCN projects on topology and motivic homotopy (total >30 MNOK).
Associate Professor Eilif Hugo Hansen works at the Department of Electrical Energy, Norwegian University of Science and Technology (NTNU). His primary research areas are electrical installations and lighting technology, with a focus on energy efficiency, daylight utilization, and applications in aquaculture. He has contributed to standardization committees like NEK/NK64 and IEC TC64, and co-developed the LYSSTYR lighting control software. Department: Electrical Energy School: Faculty of Information Technology, Mathematics, and Electrical Engineering University: Norwegian University of Science and Technology Education includes a Civil Engineering degree (1985) and Doctorate in Engineering (1990) from NTH (now NTNU). He is a certified electrical contractor with expertise in low-voltage systems. Research Highlights : Over 30 years of publications in lighting technology, electrical safety, and energy systems. Key themes include ground fault detection , non-radial electrical networks , and daylight integration in buildings. His work with Lysforsk center and SINTEF collaborations underscores his industry impact. Teaching involves courses like TET4165 Light and Lighting and TET4170 Electrical Installations . He has supervised over 40 master's theses, including topics on smart lighting , power distribution , and energy-saving technologies . Professional Engagement : Member of NEK/NK64 Low-Voltage Installations committee Contributor to IEC TC64 MT12 standards Former head of the Energy Transformation and Electrical Installations program (1997-2000) Co-developer of the LYSSTYR software for simulating daylight rhythms
Morten Hovd is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His work focuses on advanced control systems, particularly in model predictive control, optimization, and power electronics. He has contributed to control design for uncertain systems, bilinear models, and modular multilevel converters. Research Interests Control Theory and Model Predictive Control (MPC) Optimization Techniques in Control Systems Power Electronics and Smart Grid Applications Stability Analysis of Hybrid and Discrete-Time Systems Teaching TTK4210 - Advanced Control of Industrial Processes TK8118 - Mini-seminar in Cybernetics
Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.
Jon Olav Vik is a Professor at the Norwegian University of Life Sciences (NMBU), affiliated with the Department of Mathematical Sciences and Technology within the Faculty of Science and Technology. He leads the DigiSal project—"Towards the Digital Salmon: From a reactive to a pre-emptive research strategy in aquaculture"—funded under the Research Council of Norway’s Digital Life initiative. He is also a lead modeller in the GenoSysFat project, which aims to enhance omega-3 content in farmed salmon through integrated genomics and systems biology approaches. His research spans systems biology , computational physiology , genotype-phenotype modeling , and ecological dynamics . He works at the intersection of biology, mathematics, and computer programming, developing models to understand how genetics, nutrition, and environment interact in fish and ecological systems. His pedagogical focus includes biostatistics and programming in R, and he teaches courses such as STIN100, STIN300, and STAT100. The 15 most recent publications reflect a consistent focus on systems-level understanding in biology, particularly in salmon aquaculture, metabolic regulation, and genotype-phenotype relationships. These works appear in high-impact journals like Nature , Science , PLOS Computational Biology , and Journal of The Royal Society Interface , demonstrating interdisciplinary reach across computational biology, genomics, ecology, and biostatistics. Key themes include metabolic modeling, microbiome stability, lipidome remodeling, and sensitivity analysis in dynamic models. Jon Olav Vik has contributed to major collaborative efforts including the Infrastructure for Systems Biology Europe (ISBE) , where he helped develop frameworks for "modelling as a service." He has also authored book chapters and technical deliverables on systems biology and modeling practices. He actively supervises students and invites master’s thesis candidates with interests in quantitative biology. While no specific awards are listed, his leadership in national and international research projects underscores his scientific impact. His work supports both fundamental science and sustainable aquaculture innovation.
Egor Kostylev serves as an Associate Professor in the Department of Informatics within the Faculty of Mathematics and Natural Sciences at the University of Oslo. His research focuses on the theoretical foundations connecting symbolic and sub-symbolic artificial intelligence, particularly examining relationships between formal logic systems and machine learning approaches. His educational background includes an MSc (Specialist, 2005) and PhD (Candidate, 2009) from Lomonosov Moscow State University under Prof. Vladimir A. Zakharov. He subsequently held research positions at the University of Edinburgh (2010-2013) and the University of Oxford (2013-2020) before joining the University of Oslo in 2020. Kostylev's research interests center on bridging symbolic AI formalisms with sub-symbolic approaches. He investigates connections between various logics (Description Logics, Temporal Logics, Datalog), query languages (SPARQL, Regular Path Queries, OTTR), and machine learning formalisms (Graph Neural Networks, Markov Logic Networks). His work addresses critical challenges in Explainable, Trustworthy, and Green AI through theoretical foundations that connect different AI paradigms. His publication record demonstrates consistent high-impact contributions in theoretical computer science and AI, with numerous publications in top venues including AAAI, LICS, Journal of the ACM, and ICLR. His recent work shows a clear trajectory toward unifying logical reasoning with neural network approaches, particularly through graph neural networks and their connections to logical formalisms. The research spans theoretical foundations of knowledge representation, temporal reasoning in knowledge bases, and the logical expressiveness of modern neural architectures. As a research leader, Kostylev supervises multiple PhD students including Shuwen (Aurora) Liu, Maximilian Pflüger, Roxana Pop, Dongzhuoran Zhou, and Erik Snilsberg. He serves as a Research Theme Leader for the Integreat SFF: Norwegian Centre for Knowledge-driven Machine Learning. His teaching responsibilities include IN3020/4020 Database Systems courses. He leads the Data and Knowledge Management (DKM) research group at the University of Oslo, which focuses on foundational aspects of knowledge representation, database theory, and the intersection with modern machine learning techniques. The group actively collaborates with international researchers and contributes to advancing theoretical understanding of how symbolic and neural approaches to AI can complement each other.
Lars Kristiansen is Professor II (part-time Professor) at the Department of Mathematics, University of Oslo . His research centres on the intersection of mathematical logic, computability theory, and computational complexity, with recent emphasis on computable analysis, weak first-order theories, subrecursive degree structures, and implicit computational complexity. Research Interests Computable Analysis: Representations and computational complexity of irrational and real numbers. Weak First-Order Theories: Decidability, interpretability and fragments of concatenation theories. Subrecursive Degree Theory: Fine structure of honest subrecursive degrees and the Grzegorczyk hierarchy. Implicit Computational Complexity: Reversible computing, type systems that capture complexity classes, and resource-bounded program analysis. Across more than 50 refereed publications since 1996, a clear trend emerges: an early focus on subrecursive hierarchies and honest degrees evolved into an intensive study of the computational content of real number representations and the logical strength of weak arithmetics. Recent work (2023-2025) deepens this agenda, analysing the complexity of converting between alternative representations of reals and the degree structures that these induce. Scientific Output While no named awards are listed, Kristiansen’s contributions are disseminated in top venues such as Bulletin of Symbolic Logic , Annals of Pure and Applied Logic , Archive for Mathematical Logic , Science of Computer Programming , and leading LNCS conferences. His work is frequently co-authored with Amir Ben-Amram, Jakob Grue Simonsen, Juvenal Murwanashyaka, Ivan Georgiev, and Neil D. Jones, indicating active collaborative networks in both logic and theoretical computer science. Research Groups & Collaborations Logic research group at the University of Oslo Data and Knowledge Management (DKM) group
Emily Annika Burger is a Professor at the Department of Health Management and Health Economics, University of Oslo, where she leads research on health economics and cancer prevention. She holds a PhD from the University of Oslo and maintains a joint appointment as Research Scientist at Harvard T.H. Chan School of Public Health. Her education includes an MPhil from the University of Oslo and a BS from the University of Denver. Her research applies mathematical modeling to evaluate health policies, with emphasis on HPV vaccination strategies, cervical cancer screening, and cost-effectiveness analyses in global health contexts. Primary interests include optimizing prevention programs, health technology assessment, and addressing healthcare disparities. Recent publications demonstrate a focus on modeling vaccination impacts during pandemics, health economic evaluations of cancer interventions, and innovative screening methodologies. Her work frequently employs microsimulation models to project long-term public health outcomes. Awards and Honors 2023 Cancer Society of Norway Young Researcher Award 2024 University of Oslo Award for Young Researchers She leads multiple international collaborations, including projects with the Cancer Registry of Norway and Harvard Chan School. Her research group focuses on economic evaluation methodologies for healthcare technologies.
Kristin Y. Pettersen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a Professor II at the Norwegian Defence Research Institute (FFI). She is a co-founder of Eelume AS, a company specializing in underwater robotics solutions. Education: Civil Engineering and PhD in Technical Cybernetics from NTNU Her research focuses on advanced control systems for marine and underwater vehicles, particularly snake robots and autonomous underwater vehicles (AUVs). Key areas include formation control, path following, adaptive guidance algorithms, and safety-critical control in dynamic environments. Recent work explores machine learning integration and energy-shaping techniques for robust locomotion. Publications highlight trends in Model Predictive Control (MPC) , Collision Avoidance , and Task-Priority Operational Space Control for redundant and underactuated systems. Her work bridges theoretical control theory with practical applications in marine robotics, including autonomous inspections and cooperative transport. Labs/Teams: Collaborates with NTNU's Faculty of Information Technology and Electrical Engineering and co-founded Eelume AS, advancing subsea robotic manipulation technologies.
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