Find Academic Supervisors

Ali Ramezani-KebryaView profile

Associate Professor

• Machine Learning
• Deep Learning
• Federated Learning
• +9 more

Ali Ramezani-Kebrya is an Associate Professor with tenure in the Department of Informatics at the University of Oslo (UiO), where he leads research in machine learning theory. He holds dual Principal Investigator roles at the Norwegian Center for Knowledge-driven Machine Learning (Integreat) and SFI Visual Intelligence, and is an active member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. His service includes Area Chair positions for NeurIPS and AISTATS, and Action Editor for Transactions on Machine Learning Research. His research focuses on theoretical foundations of deep learning with emphasis on understanding input data distribution encoding in neural network layers. Key themes include minimizing statistical risk under resource constraints, addressing distribution shifts in distributed settings, and developing practical tools for robust federated learning. Current applications span emotion recognition, marine data analysis, and neuroscience, reflecting his commitment to real-world machine learning challenges as evidenced by his FRIPRO-funded Machine Learning in Real World (MLReal) project. Recent publication trends reveal three dominant threads: (1) label/covariate shift mitigation in distributed systems through entropy regularization and density ratio estimation; (2) communication-efficient optimization via layer-wise quantization and adaptive compression techniques achieving 150% speedups; and (3) robustness guarantees against tailored attacks and distribution shifts. These works consistently bridge theoretical bounds with empirical validation across domains from GAN training to federated settings. Scientific recognition includes: FRIPRO Grant for Early Career Scientists (2025) for MLReal project SFI Visual Intelligence Spotlight Publication award (2023) for federated learning work He actively mentors 11 graduate students across Oslo and Tromsø universities, with recent PhD placements at Apple and NVIDIA. Current grant portfolio features the FRIPRO Early Career award and leadership roles in two major Norwegian research centers. His lab maintains strong industry collaborations through Vector Institute and EPFL, with recent hiring for PhD and postdoc positions in physics-informed machine learning.

Trym Vegard HaavardsholmView profile

Lecturer

• Computer Vision
• Machine Learning
• Robotics
• +3 more

Trym Vegard Haavardsholm is a 20% part-time Lecturer at the University of Oslo (UiO) within the Section for Autonomous Systems and Sensor Technologies. He also serves as Principal Scientist at the Norwegian Defence Research Establishment (FFI) and is a PhD candidate at the Department of Engineering Cybernetics, NTNU. His research focuses on computer vision, machine learning, robotics, and image analysis, with a particular emphasis on multispectral imaging systems and unmanned aerial vehicles (UAVs). Haavardsholm has contributed to advancements in sensor technologies for tactical reconnaissance, autonomous navigation, and real-time data processing. His work spans applications in defense, environmental monitoring, and emergency response systems. Research Interests Haavardsholm’s research integrates interdisciplinary approaches to develop innovative sensor systems and algorithms. Key areas include compact multispectral imaging for small UAVs, in-operation camera calibration, and anomaly detection in hyperspectral data. His contributions emphasize practical applications such as urban feature classification, collaborative indoor navigation, and bioaerosol detection. His work bridges theoretical computer science with applied engineering, addressing challenges in autonomous systems and sensor fusion. Publications His publications highlight trends in multispectral sensor design, UAV imaging, and real-time georeferencing. Recent work includes compact sensor systems for tactical use and advancements in pushbroom image rectification. Earlier research explored band selection algorithms for target detection and GPU-accelerated anomaly detection. Professional Roles As a lecturer, Haavardsholm contributes to academic supervision and teaching in autonomous systems. His dual role at FFI and UiO reflects his commitment to translating academic research into practical defense and civilian applications. His PhD candidacy at NTNU underscores his ongoing academic engagement in engineering cybernetics.

• Internet of Things
• Fog Computing
• Edge Computing
• +9 more

Amirhosein Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems for emerging computing technologies including Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). Dr. Taherkordi received his Ph.D. from the Informatics Department at the University of Oslo under the supervision of Prof. Frank Eliassen, with his thesis titled "Programming Wireless Sensor Networks: From Static to Adaptive Models." He holds an M.Sc. in Information Technology Engineering (Software Engineering) from University of Science and Technology and a B.Sc. in Computer Engineering from Sharif University of Technology. His research spans multiple domains of distributed systems with emphasis on practical applications. He investigates energy efficiency in wireless sensor networks, communication optimization in IoT systems, and adaptive resource allocation in edge computing environments. His work addresses critical challenges in network traffic classification, federated learning for vehicular networks, and data processing across heterogeneous platforms. Analysis of his recent publications reveals a strong trajectory toward communication-efficient federated learning techniques for vehicular networks, energy-aware protocols for IoT data collection, and advanced machine learning approaches for network traffic analysis. His research consistently focuses on optimizing resource usage while maintaining system performance and privacy in distributed architectures. Dr. Taherkordi actively contributes to several research initiatives including the CPS Lab at UiO for Cyber Physical Systems, DILUTE: Fluid Service Abstraction for Large-Scale Cloud IoT Systems, and the Gemini Centre on IoT at UiO. His work bridges theoretical advances with practical implementations in transportation systems, environmental monitoring, and industrial automation.

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

• Programming Theory
• Algebraic Methods in Software Development
• Programming Language Design
• +12 more

Magne Haveraaen is a Professor in the Department of Informatics at the University of Bergen, where he leads the Bergen Language Design Laboratory (BLDL). He is actively engaged in research on programming theory, formal methods, language design, and high-integrity systems. His work centers on algebraic specifications, generic programming, domain engineering, and the design of the Magnolia programming language. He has been a long-standing instructor of key courses such as INF220 (Program Specification) and INF222 (Programming Languages), and contributes to national and international academic forums. University: University of Bergen School: Faculty of Mathematics and Natural Sciences Department: Department of Informatics Academic Rank: Professor Email: magne.haveraaen@uib.no His research interests include programming language design, formal specifications, generic and mouldable programming, multicore and GPU computing, and algebraic methods in software development. He emphasizes correctness, reuse, and high-integrity software through theoretical and practical innovations. He has contributed extensively to the literature on algebraic reasoning, array programming, and domain-specific abstractions. The recent publications highlight a consistent focus on algebraic semantics, language design (especially Magnolia), array programming, and formal verification. Themes across these works include correctness by design, specification-driven development, and adapting programming models for modern architectures. His involvement in workshops like ARRAY, WGP, and CALCO underscores his leadership in programming language theory. No scientific awards are listed in the provided texts. He advises students and welcomes collaboration in programming theory, language design, and formal methods. He has led funded projects such as MoSIS and SHIP and is involved in the High Integrity Systems Forum. He organizes and participates in key conferences including NWPT, GPCE, and SCAM. He leads the Bergen Language Design Laboratory (BLDL), which focuses on experimental language design, and contributes to the SAGA initiative on scientific computing with algebraic abstractions. His work integrates theory with practical tooling and language implementation.

• Internet of Things (IoT)
• Fog Computing
• Edge Computing
• +16 more

Amir Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, and mobility in distributed systems for emerging technologies like IoT, Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). University: University of Oslo Department: Informatics Academic Rank: Professor His research spans IoT, Edge/Fog Computing, and Cyber-Physical Systems, emphasizing energy efficiency, privacy preservation, and self-adaptive architectures. Key areas include network traffic classification, computation offloading, and federated learning applications in vehicular systems. Recent publications highlight advances in latency-aware IoT data transmission , federated vehicular networks , energy-efficient wireless charging , and privacy-preserving data integration . These works often integrate machine learning with network optimization. Projects include the CPS Lab at UiO, DILUTE (Fluid Service Abstraction), and the Gemini Centre on IoT . He collaborates on initiatives like PACE for energy informatics curricula development.

Hoai Phuong HaView profile

Professor

• Green and energy-efficient computing
• Parallel and distributed computing systems
• Cyber-physical systems
• +3 more

Professor Hoai Phuong Ha is affiliated with UiT The Arctic University of Norway's Department of Computer Science. A leading expert in green computing and cyber-physical systems, they contribute to Arctic research through the Distributed Arctic Observatory (DAO) and Arctic Green Computing (AGC) group. Founded ARC (Arctic Center for Sustainable Energy) PI in EU FP7 EXCESS and H2020 TAILOR projects WP-leader in EEA POLNOR HAPADS and RCN PREAPP projects Their research focuses on energy-efficient computing, including IoT systems, edge computing, and parallel algorithms. Recent work addresses wireless charging trajectories (eU2U, 2025), smart grid networks (GridWatch, 2024), and pollution monitoring (2024). Publications span cyber-physical observatories, sensor calibration, and distributed systems optimization. Key trends in their 15 most recent articles (2017-2025) include: energy-aware data structures, Arctic-adapted IoT deployments, and sustainable computing methods. Collaborations span EU and Norwegian grants with applications in smart grids, environmental sensing, and high-performance computing. Co-founder of Arctic Center for Sustainable Energy (2017) Active in EEA POLNOR (2019-2023) and RCN eX3 infrastructure project Their lab (Realfagbygget A237) develops systems for Arctic tundra monitoring, including UAV-powered networks and energy-harvesting protocols. Students include researchers from multiple international collaborations.

• Applied Mathematics
• Numerical Analysis
• Parallel Computing
• +7 more

André Brodtkorb is a Professor and Head of the Department of Information Technology at Oslo Metropolitan University. His research spans applied mathematics, numerical analysis, and computational science, focusing on physics simulations and GPU computing. He advocates for open and reproducible research and is actively involved in education and societal engagement through the Academy of Young Researchers (2024-2028). Research Interests: His work integrates applied mathematics and computer science to develop high-performance simulations for environmental phenomena, including ocean currents, volcanic ash dispersion, and coastal flooding. He specializes in GPU-accelerated parallel computing, finite-volume methods, and Python-based scientific programming. Publication Trends: Recent articles highlight advancements in GPU computing efficiency, ocean modeling, and inverse ash transport modeling for volcanic plume forecasting. His research bridges computational methods with real-world environmental challenges. Scientific Awards: Member of the Academy of Young Researchers (2024-2028) Contact Information: Office: Pilestredet 35, 0166 Oslo Phone: +47 456 19 070 (Mobile), +47 672 35 924 (Office) Email: andre.brodtkorb@oslomet.no

• Smart Wireless Systems
• Internet of Things (IoT)
• Information Security
• +8 more

Mohammad Derawi is a Professor in the Department of Electronic Systems at the Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik campus. He leads the Smart Wireless Systems (SWS) research group and serves as the scientific leader of the IoT Lab at NTNU Gjøvik. Educational Background: PhD in Information Security from NISLab (Norway) and CASED (Germany) BSc and MSc in Informatics from DTU (Denmark) His research interests span smart wireless systems, Internet of Things (IoT), information security with a focus on biometric authentication, digital electronics, applied machine learning for activity recognition, and e-learning technologies. His work integrates cybersecurity, embedded systems, and data science to develop secure and intelligent IoT solutions for real-world applications. The recent publications highlight a strong trend in mmWave-based sensing for unmanned aerial systems, RF fingerprinting for secure identification, IoT security frameworks, and machine learning applications in education and human resource analytics. His research bridges theoretical innovation with practical implementation, particularly in smart cities, healthcare, and transportation. Scientific Awards and Recognition: Invitation to the Crown Prince and Princess's 50th birthday celebration, 2023 Study Quality Award, NTNU, 2017 Norway’s Youngest Professor Award, 2016 Denmark’s youngest M.Sc. engineering award, 2009 IEEE Commendation for Young Professionals Volunteer, 2011 Multiple best paper awards from IEEE, ACM, and Springer Mohammad Derawi has been involved in several funded research and development projects, including IoT Safetraffic (RFF Inland), Ambulance Drone (NTNU Vice-Rector), Wireless ECG (Innovation Norway), biometric handgun security (RFF Innlandet), and the EU Framework 7 TURBINE project. He mentors students and collaborates with international researchers, contributing significantly to both academic and applied domains. His leadership in the SWS group and IoT Lab fosters innovation in wireless and secure embedded systems. He is actively engaged in laboratory and team-based research, particularly through the Smart Wireless Systems group and the IoT Lab, focusing on developing secure, intelligent, and scalable solutions for next-generation wireless applications.

Fredrik ManneView profile

Professor

• Parallel Computing
• Distributed Computing
• Graph Algorithms
• +3 more

Fredrik Manne is a Professor at the Department of Informatics, University of Bergen, Norway. His work focuses on parallel and distributed computing, particularly in combinatorial scientific computing and self-stabilizing algorithms. He has contributed extensively to graph algorithms, including matching, coloring, and clustering, with applications in high-performance computing, numerical optimization, and wireless networks. His research involves designing algorithms for parallel architectures, including multi-core and GPU-based systems. He has co-authored numerous publications on topics such as spanning forests, vertex cover heuristics, and b-matching. His work often integrates theoretical and applied approaches, addressing challenges in sparse matrix computations and wireless mesh network communication. The recent articles highlight his exploration of graph neural networks for parallel coloring, efficient multithreaded matching algorithms, and GPU-accelerated clustering methods. These studies emphasize scalability, optimization, and real-world applications in computational science.

• Internet of Things (IoT)
• Edge and Fog Computing
• Network Security
• +6 more

Amirhosein Taherkordi is an Associate Professor at the Department of Information Security and Communication Technology within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His academic profile shows continuous research activity with publications spanning from 2011 through 2025, indicating an established career trajectory in computer science and networking research. Dr. Taherkordi's research interests focus on addressing fundamental challenges in distributed computing environments, particularly in resource-constrained scenarios. His work spans Internet of Things (IoT) systems, edge and fog computing architectures, network security protocols, and machine learning applications for network traffic analysis. He has made significant contributions to energy-efficient data collection protocols for wireless sensor networks, privacy-preserving techniques for industrial IoT systems, and communication-efficient approaches for federated learning in vehicular networks. His research consistently bridges theoretical innovation with practical implementation, addressing real-world challenges in smart transportation, environmental monitoring, and industrial automation systems. An analysis of Dr. Taherkordi's recent publication trends (2023-2025) reveals a strong emphasis on federated learning applications for vehicular networks (FedAGL, FedAPT), energy-efficient IoT data collection strategies (eU2U, ECMSH), and the integration of transfer learning with edge computing for transportation applications (TELEGAIT, FOGFLEET). His work increasingly addresses the critical tension between computational efficiency and accuracy in distributed systems, with growing applications in environmental monitoring (PmForecast) and circular economy frameworks. The interdisciplinary nature of his research spans computer science, electrical engineering, and environmental science domains. Dr. Taherkordi maintains an active collaborative research profile, working with international colleagues across multiple institutions as evidenced by his diverse publication venues including IEEE Transactions, ACM journals, and various conference proceedings. His research program appears to be well-established with consistent funding, though specific grant details aren't provided in the available text. He likely leads or contributes significantly to research groups focused on networking, IoT, and edge computing at NTNU, mentoring students in these emerging technology domains.

• Machine Learning
• Computer Vision
• Intelligent Systems
• +7 more

Evi Zouganeli is a Professor at the Department of Mechanical, Electrical and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet). She leads the Automation, Robotics, and Intelligent Systems (ARIS) research group and serves as a board member of the Norwegian Artificial Intelligence Society (NAIS). Her work bridges academic research and industrial applications in intelligent systems. Research Focus: Zouganeli specializes in Machine Learning and Computer Vision applications for Cognitive Robotics, with emphasis on assistive technologies, smart cities, and smart industry. Her research explores neuromorphic computing, sensor network optimization, and multi-modal AI integration. Publications Trends: Recent works focus on AI-enabled robotics, sensor placement optimization in smart homes, and neuromorphic reservoir networks. Earlier contributions include European collaborative research in photonic technologies and broadband networks. Additional Contributions: She actively participates in public discourse about AI implementation challenges and Norway’s AI development potential, engaging with industry leaders and policymakers.

• High Performance Computing
• Parallel Computing
• Heterogeneous Computing
• +12 more

Anne C. Elster is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She is the founder and director of the HPC-Lab, a leading research group in heterogeneous and parallel computing. She also maintains a long-standing affiliation with the Oden Institute at the University of Texas at Austin as a Senior Visiting Scientist until Summer 2025. Research Interests: Her work spans high-performance computing (HPC), GPU computing, parallel algorithms, auto-tuning, performance optimization, and machine learning applications in scientific computing. She leads research in heterogeneous architectures and has contributed significantly to compiler and runtime systems for GPUs and accelerators. Publications Trends: Her recent publications (2021–2024) focus on GPU acceleration, auto-tuning frameworks (e.g., BAT, LS-CAT), performance modeling (Roofline), machine learning integration in HPC, and applications in geophysical and scientific computing. There is a strong emphasis on empirical evaluation, benchmarking, and practical optimization techniques. Scientific Awards and Recognition: IEEE Senior Member (2000) IEEE Computer Society Distinguished Contributor Charter member, NTNU's Board (2021) Distinguished Speaker, IEEE Computer Society (2019–2022) Advising and Grants: She has advised over 100 master’s students and several PhD students. She has led major funded projects including the RCN SFI Centre for Geophysical Forecasting, EU H2020 CloudLightning and TICOH, and NFR FRINATEK on Computational Microscopy. She has served on numerous international program committees and evaluation boards. Labs and Teams: She leads the HPC-Lab at NTNU, which includes postdocs, PhDs, and master’s students, and collaborates with international researchers. The lab is a hub for innovation in GPU computing, auto-tuning, and HPC applications.

Tom RyenView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Digital Signal Processing
• +13 more

Tom Ryen is an Associate Professor and Head of the Department of Electrical Engineering and Computer Science at the University of Stavanger (UiS), Faculty of Science and Technology. He plays a key leadership role in advancing artificial intelligence research and education, including co-founding the Stavanger AI Lab and chairing the board of the Norwegian Artificial Intelligence Research Consortium (NORA). His work bridges technical research and public engagement, particularly on the societal implications of AI. His research focuses on artificial intelligence, machine learning, digital signal processing, and bioinformatics. He has made significant contributions in gene prediction, splice site analysis using neural networks, ECG signal compression, and GPU-based optical flow algorithms. His recent work emphasizes AI ethics, education, and public understanding, reflecting a shift toward societal impact and policy. Tom Ryen's publications from 2024 show a strong trend in public outreach, with articles and lectures on AI literacy, misinformation, workplace integration, and educational challenges. These works highlight his role as a thought leader in Norway’s AI discourse, advocating for responsible adoption, national infrastructure, and ethical guidelines. Scientific Awards: No scientific awards mentioned in the text. Tom Ryen actively mentors students and collaborates across disciplines, though specific advisees are not listed. He has been involved in significant initiatives such as launching new master’s programs, expanding IT education, and promoting AI in medical and urban technologies. He has not received any mentioned grants, but his leadership in NORA and the Stavanger AI Lab suggests substantial project involvement. He is a founding figure in the Stavanger AI Lab, a research unit at UiS dedicated to AI innovation, education, and collaboration with industry and public sectors. The lab focuses on practical applications and ethical deployment of AI, aligning with national and regional development goals.

Julius ParulekView profile

Adjunct Associate Professor

• Molecular Visualization
• Bioinformatics
• Computer Graphics
• +2 more

Julius Parulek is an Adjunct Associate Professor at the University of Bergen's Department of Informatics, specializing in molecular visualization and computational methods for biomolecular structures. His work focuses on developing tools and techniques for interactive exploration of protein dynamics, ligand transportation, and large-scale biological datasets. He collaborates with domain experts in biochemistry and structural biology to create visualization solutions for complex molecular processes. Research interests include: Interactive visualization of protein tunnels and cavities Multiscale rendering of biomolecular complexes Geological modeling and sketch-based visualization Agent-based modeling for biochemical processes Key contributions include: Development of the cellVIEW tool for large-scale biomolecular visualization Pioneering work on continuous levels-of-detail for molecular surfaces Techniques for unfolding protein tunnels to analyze their dynamics His research bridges computer graphics with structural biology, enabling scientists to better understand molecular mechanisms through advanced visualization approaches.