Find Academic Supervisors

• Machine Learning
• Graph Neural Networks
• Time Series Analysis
• +7 more

Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano

• Geriatric Medicine
• Nursing Home Medicine
• Palliative Care
• +6 more

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.

• Tsetlin Machines
• Reinforcement Learning
• Machine Learning
• +3 more

Xuan Zhang is an Associate Professor at the Department of Information and Communication Technology, University of Agder. His research focuses on Tsetlin Machines, learning automata, and their applications in machine learning, computer vision, and hyperspectral imaging. Research Trends: Zhang’s recent work includes developing interpretable machine learning models (e.g., Tsetlin Machines), optimizing convolutional architectures for image processing, and applying automata theory to solve multi-armed bandit problems and channel selection in cognitive networks. His field spans theoretical analysis and practical implementations in AI, remote sensing, and health informatics. Scientific Contributions Co-developed advanced Tsetlin Machine variants for XOR/NOT operator convergence, disease forecasting, and image restoration Published in journals like IEEE Transactions on Pattern Analysis and Machine Intelligence , Information Sciences , and Applied Intelligence Explored Bayesian pursuit algorithms, hierarchical learning automata, and particle swarm optimization techniques Contact: xuan.zhang@uia.no

Hans Jonas Fossum MoenView profile

Associate Professor

• Autonomous Systems
• Sensor Technologies
• Robotics
• +3 more

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.

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +4 more

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

• Music Technology
• Music Psychology
• Embodied Music Cognition
• +5 more

Alexander Refsum Jensenius is a Professor of Music Technology and Director of the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion at the University of Oslo. He also leads the fourMs Lab and co-founded the MishMash Centre for AI and Creativity. His work bridges musicology, psychology, and technology, focusing on embodied music cognition, human motion analysis, and creative applications of AI. Notably, he pioneered research on air guitar motion and human micromotion through projects like the Oslo Standstill Database . Educated at the University of Oslo (BA in Music and Mathematics, MA in Musicology) and Chalmers University of Technology (MSc in Applied IT), Jensenius holds a PhD in Music Technology from UiO. He has held visiting researcher roles at UC Berkeley, McGill University, and KTH. Leadership roles include Department of Musicology Head (2013–2016) and Steering Committee Chair for the International Conference on New Interfaces for Musical Expression (NIME, 2011–2022). Research interests span music-related body motion, AI in creative contexts, and open research practices. Key contributions include the Music Moves and Motion Capture MOOCs, the Musical Gestures Toolbox software, and monographs like Sound Actions and Sonic Design . His work emphasizes interdisciplinary collaboration, with projects addressing ventilation systems' acoustic properties and cell culture vibrational effects. Awards include the European Open Data Champion recognition. He advocates for open science and maintains extensive digital archives of research materials, emphasizing institutional web pages as critical research infrastructure.

• Biomedical Instrumentation
• Medical Technology
• Clinical Engineering
• +3 more

Jan Olav Høgetveit is an Associate Professor in the Department of Physics at the University of Oslo (UiO), within the Faculty of Mathematics and Natural Sciences. He also serves as Head of Research & Development in the Department of Biomedical and Clinical Engineering at Rikshospitalet, Norway’s national hospital. His work bridges physics and clinical practice, focusing on medical instrumentation. Education: Bachelor of Electronic Engineering (Technical Cybernetics), Oslo University College, 1993 Master of Electronics, University of Oslo (Department of Physics), 1997 Ph.D. in Physics (Technology Applications for Medical Devices), University of Oslo, 2008 Research Interests: Høgetveit specializes in biomedical instrumentation and clinical engineering, particularly in surgical technology and wireless communication impacts on medical devices. His work addresses challenges like real-time physiological monitoring during heart-lung machine use, non-invasive blood glucose detection, and bioimpedance-based viability assessment of organs. He emphasizes interdisciplinary collaboration between engineering and medicine to enhance patient safety and surgical outcomes. Scientific Contributions: His research trends span bioimpedance applications in ischemia/reperfusion injury, machine learning for surgical decision support, and electrosurgery safety. He has explored ventilator optimization during pandemics and implant-related thermal risks. Contributions highlight both hardware development (e.g., optically isolated current sources) and software innovations (e.g., neural networks for viability prediction). Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: Høgetveit received a 1998–2001 research council scholarship. As Head of R&D since 2001, he oversees translational projects. No formal advisees/students listed, though he collaborates extensively with teams on device development and clinical trials. Labs & Teams: Affiliated with UiO’s Department of Physics and Rikshospitalet’s Biomedical and Clinical Engineering department. Active in the Electronics research group at UiO. Engages with multidisciplinary teams addressing surgical instrumentation and physiological monitoring challenges.

• Control Systems
• Robotics
• Marine Robotics
• +3 more

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.

Tor SkeieView profile

Professor

• High-Performance Networking
• Distributed Systems
• InfiniBand Technology
• +2 more

Tor Skeie is a Professor at the University of Oslo's Department of Informatics, specializing in networks and distributed systems. His research focuses on high-performance networking, InfiniBand technologies, and adaptive routing systems. Current investigations include automated parameter tuning for reservoir simulations, adaptive routing in InfiniBand hardware, and modeling WiFi quality attenuation. His work develops efficient solutions for virtualized HPC environments and cloud computing infrastructures. Recent publications demonstrate innovations in network modeling, adaptive routing algorithms, and performance analysis of distributed systems. Research collaborations span European projects on high-performance networking infrastructures. Leads the Networks and Distributed Systems (ND) research group investigating fault-tolerant routing, network virtualization, and congestion control mechanisms.

Solveig BruvollView profile

Associate Professor

• Autonomous Systems
• Cybersecurity
• Military Technology
• +4 more

Solveig Bruvoll is an Associate Professor at the University of Oslo (15% position) and holds a 100% research position at the Norwegian Defence Research Establishment (FFI). She specializes in Autonomous Systems and Cybersecurity , focusing on security in military operations and software resilience. Her work bridges engineering, mathematics, and defense applications. Research interests include autonomous vehicle security, defense simulation, and mathematical modeling. She teaches the course TEK5510 and has published extensively in conferences like IEEE CNS and journals such as Journal of Defense Modeling and Simulation . Her FFI affiliation emphasizes practical military technology and operational analysis. Publications highlight contributions to path planning algorithms, command agent modeling, and cybersecurity frameworks for autonomous systems. She collaborates on NATO projects, as seen in her work on M&S support for operational tasks. No awards are explicitly listed, but her research impacts critical defense and civilian infrastructure security domains.

• Cryptographic Voting Systems
• Key Exchange Protocols
• Cloud Cryptography
• +3 more

Kristian Gjøsteen is a Professor at the Department of Mathematical Sciences within the Norwegian University of Science and Technology (NTNU) . He actively contributes to the Algebra Group and specializes in cryptographic systems with a focus on electronic voting , security proofs , and privacy-enhancing technologies . Educational Background: MSc and PhD from NTNU Research Interests: His work spans cryptography , key exchange protocols , cloud security , and formal verification of security mechanisms. Particular emphasis is placed on coercion-resistant voting systems , lattice-based encryption , and blockchain privacy models . Article Trends: Recent publications demonstrate expertise in post-quantum cryptography , machine-checked security , and privacy-preserving voting architectures . Collaborative efforts explore hybrid cryptographic schemes , verifiable decryption , and mix-net implementations for secure elections.

Bjarte HannisdalView profile

Associate Professor

• Quantitative Paleobiology
• Geobiology
• Earth System Evolution
• +7 more

Bjarte Hannisdal is an Associate Professor in the Department of Geosciences at the University of Bergen, Norway, and affiliated with the Bjerknes Centre for Climate Research. He plays a key leadership role in iEarth, a national Centre of Excellence in geoscience education, serving as the iEarth Education Chair and Head of Focus Area 1, which aims to develop innovative frameworks for higher education in geosciences. Department of Geosciences, University of Bergen Bjerknes Centre for Climate Research iEarth – Centre of Excellence in Geoscience Education His research lies at the intersection of geobiology, paleontology, and Earth system science, with a strong focus on quantitative methods. He investigates Earth system evolution, causality in dynamical systems, and the co-evolution of life and the planet using advanced statistical and information-theoretic approaches applied to geological and fossil records. His work spans microbial ecology in deep-sea sediments, paleoclimatology through isotope analysis, and the detection of causal interactions in deep time. Hannisdal has made significant contributions through a high-impact publication record, with articles in top journals such as Science , Nature Geoscience , PNAS , and Physical Review E . His recent publications highlight trends in applying machine learning and information theory to geoscience problems, including microbial responses to oxygen, causality detection in incomplete records, and the calibration of geochemical proxies. These works reflect a strong interdisciplinary trend, integrating biology, physics, and computational methods into Earth sciences. He is actively involved in higher education, having developed and taught courses such as GEOV114 (Introduction to Geobiology) and GEOV302 (Data Analysis in Geosciences), and contributes to others like GEOV344 and BIO318. His educational research explores student-centered learning and computational skill development. He has supervised doctoral research, including Dario Blumenschein’s project on educational change. His work is supported by funding from the Research Council of Norway, Trond Mohn Foundation, and EU Horizon 2020. Hannisdal collaborates widely across institutions and disciplines, as evidenced by his co-authorship with researchers from Norway, the US, Germany, and others.

Karl-Heinz Frank ReichertView profile

Professor

• Mobile Communication Systems
• E-Learning and Education Technology
• Telehealth and IoT Applications
• +4 more

Frank Reichert is a Professor of Mobile Systems at the University of Agder (UiA), affiliated with the Department of Information and Communication Technology under the Faculty of Engineering & Science. He served as UiA's Rector (2016-2019) and Dean of Engineering & Science (2007-2015). With over 30 years in fixed/wireless communication systems, he has led national initiatives like the SFI Offshore Mechatronics Center (200 MNOK budget) and established UiA's Mechatronics Innovation Lab. His industry experience includes roles at Ericsson (1995-2005) and founding Ericsson Cyberlab Singapore (1999). Education : PhD in Electrical Engineering from RWTH Aachen University (Germany). Early career included research at the Royal Institute of Technology (Sweden) and Televerket Radio (Sweden). Research Interests : Focus on mobile communication, e-learning innovation, IoT-driven telehealth, and lifelong learning. His work bridges academia and industry, emphasizing user-centric design, rapid prototyping, and future education technologies. Projects include multi-touch collaborative learning systems, AI-assisted educational tools, and digital health twin architectures. Grants & Leadership : Managed EU projects (e.g., FP4-ACTS OnTheMove, PRO-COM) and served on steering boards for Norwegian and EU research bodies. Current roles include advisor to UiA's strategic initiatives and contributor to national bullying/harassment prevention (UHRMOT). Labs & Teams : Co-founded the Mechatronics Innovation Lab, leading projects in VR rehabilitation solutions and smart healthcare systems. Active in research groups like Multimedia and e-Learning, and ReSex (Research in Sexology).

Giulia Di NunnoView profile

Professor

• Stochastic Analysis
• Financial Mathematics
• Risk Management
• +4 more

Giulia Di Nunno is a Professor in the Department of Mathematics at the University of Oslo, specializing in stochastic analysis and its applications to finance and risk management. She also holds an adjunct professorship at the Norwegian School of Economics (NHH). Her research focuses on stochastic calculus, control theory, financial modeling, and energy finance, with a particular interest in dynamic risk measures. She has led major projects like the STORM initiative on time-space risk models and is involved in interdisciplinary research on sustainability and energy markets. Di Nunno has served as President of the Scientific Council of CIMPA and is an associate editor for several prestigious journals, including Finance and Stochastics and Stochastics . Her work bridges theoretical advancements with practical applications in finance and energy sectors. Education: PhD in Mathematical Statistics (University of Pavia, 2003), Degree in Mathematics (University of Milan, 1998). Research Groups: Risk and Stochastics, STORE (completed). Key Projects: SURE-AI (AI-driven risk modeling), Unruly Sustainability (interdisciplinary research), STORM (ToppForsk project). Editorial Roles: Associate Editor for Finance and Stochastics , DEAF , FMF , and others. Her publications emphasize stochastic processes, volatility modeling, and risk measurement, with recent contributions on time-changed dynamics and applications to energy finance. She actively contributes to the international academic community through research networks like AMaMeF and ModSimFIE.

Gudmund GrovView profile

Associate Professor

• Digital Security
• Cybersecurity
• Network Anomaly Detection
• +7 more

Gudmund Grov is an Associate Professor in Digital Security at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo. His research focuses on cybersecurity, with emphasis on anomaly detection, explainable AI, and security modeling. He is actively contributing to advancements in autonomous cyber defense and machine learning applications in security operations. His research interests span Digital Security , Network Anomaly Detection , Explainable AI (XAI) , Machine Learning for Cyber Defense , Security Modeling , and Risk Modeling in Enterprise Architecture . His work integrates formal reasoning with practical modeling to enhance system security and interpretability. The recent publications reflect a strong trend towards intelligent and interpretable cybersecurity systems, combining deep learning with formal methods to detect and explain network threats. His work bridges the gap between theoretical modeling and real-world security applications, particularly in autonomous defense and labeled dataset generation for APTs. No scientific awards were mentioned in the provided text. Gudmund Grov collaborates extensively with researchers in cybersecurity and formal methods. While specific grants and advising roles are not detailed, his co-authorship on multiple projects indicates active research supervision and collaboration. He is involved in developing frameworks for labeled data generation, contextual anomaly detection, and explainable alerts in SOC environments. There is no explicit information about labs or research teams, but his work suggests involvement in cybersecurity research groups at the University of Oslo focusing on AI-driven security and formal verification.