Find Academic Supervisors

• EEG Signal Processing
• Brain-Computer Interfaces
• Artificial Intelligence
• +5 more

Marta Molinas is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). Her research spans multiple interdisciplinary domains with a focus on EEG technology and brain-computer interfaces. She actively supervises numerous Master's projects and maintains extensive international collaborations with institutions including Kavli Institute for Systems Neuroscience, RIKEN Center for Brain Science, University of Tsukuba, Juntendo University, and several European universities. Professor Molinas' research interests center on developing innovative EEG technologies, particularly her FlexEEG concept for reduced-channel EEG systems with brain imaging capabilities. Her work integrates signal processing, artificial intelligence, and neuroscience to create practical applications in mental health, sleep research, neurorehabilitation, and human-computer interaction. She specializes in EEG source imaging, machine learning for brain signal analysis, and the development of brain-computer interfaces for various applications including locked-in syndrome communication, ADHD treatment, and driver monitoring systems. Her publication portfolio demonstrates strong trends in interdisciplinary research combining neuroscience with electrical engineering and artificial intelligence. The work shows particular emphasis on developing practical EEG-based systems that minimize invasiveness while maintaining analytical power, with applications spanning healthcare, rehabilitation, and human augmentation. Her research bridges theoretical signal processing with real-world implementations through numerous student projects and international collaborations. Professor Molinas actively supervises a large team of Master's and PhD students across multiple projects, with each project typically requiring two students working collaboratively. Her research is supported through numerous international collaborations with institutions in Japan, India, and Europe, indicating substantial research funding and project leadership. She has developed a pipeline of student projects that build upon previous work, creating a cumulative knowledge base within her research group. She leads the EEG ITK research team at NTNU, which focuses on developing the FlexEEG headset prototype featuring flexible, wireless, dry electrodes designed to move across the scalp. This team works at the intersection of neuroscience, electrical engineering, and computer science, developing applications for sleep research, mental health monitoring, neurorehabilitation, and brain-computer interfaces. The team collaborates extensively with international partners including the Kavli Institute for Systems Neuroscience, the International Institute of Integrative Sleep Medicine at University of Tsukuba, and several engineering departments across Europe and Asia.

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.

Johannes SkaarView profile

Professor

• Quantum Field Theory
• Quantum Optics
• Electromagnetics
• +3 more

Johannes Skaar is a Professor at the Department of Physics, University of Oslo (UiO). He holds a 100% position there since 2017, previously at NTNU. His research focuses on quantum field theory, quantum optics, electromagnetics, metamaterials, photonics, and quantum information. He teaches advanced courses such as FYS4170 Relativistic Quantum Field Theory and FYS1005 Classical Mechanics. His work spans theoretical physics with notable contributions to single-photon states, metamaterial properties, and quantum cryptography security. Skaar’s research integrates foundational physics with applied technologies like metamaterials and quantum communication systems. His studies on Fresnel equations and magnetic permeability have advanced electromagnetic theory. He frequently publishes in top journals like Physical Review A and Physical Review Letters . Research groups: Theoretical Physics at UiO.

• Metal Forming
• Aluminum Alloys
• Product Development
• +3 more

Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.

• Control Theory
• Partial Differential Equations
• Drilling Engineering
• +6 more

Ole Morten Aamo is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. His office is located at Elektro D/B2, D344, Gløshaugen, O. S. Bragstads plass 2, and he can be reached at aamo@ntnu.no or by phone at 73594386. Professor Aamo's research focuses on control theory with particular emphasis on partial differential equations (PDEs) and their applications in drilling engineering and the petroleum industry. His work spans multiple areas including boundary control of hyperbolic systems, adaptive control methodologies, vibration control in drilling operations, and leak detection systems for pipe networks. His research combines theoretical control developments with practical applications in the oil and gas sector, particularly addressing challenges related to stick-slip phenomena, torsional vibrations, and pressure oscillations in drilling operations. His publication record reveals a consistent trajectory of high-impact research in control systems, with a notable shift toward integrating machine learning approaches with traditional control theory in recent years. The majority of his work centers around hyperbolic PDE systems, with applications primarily in drilling engineering and fluid dynamics. His research demonstrates a strong connection between theoretical control developments and practical implementations in the petroleum industry. Professor Aamo has actively supervised multiple graduate students, as evidenced by the master's theses listed in his publication record. His work often appears in top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters, as well as petroleum engineering venues like SPE Journal and ASME publications.

Arnoldo FrigessiView profile

Professor

• Statistics
• Biostatistics
• Machine Learning
• +5 more

Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.

• 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.

Sergej StoppelView profile

Researcher

• Visual Data Science
• Data Visualization
• Human-Computer Interaction
• +3 more

Sergej Stoppel is currently a researcher (PostDoc) in the visualization group at the University of Bergen. His academic background includes a Mathematics master's degree (2014) and a PhD in 2018, both from the University of Bergen. His research focuses on visual data science, with an emphasis on interactive visualization techniques, human-computer interaction, and physical visualization systems. He has authored/co-authored numerous papers on topics such as hexagonal map enhancements, spatio-temporal data interaction, and low-cost physical art generation. Education: M.Sc. Mathematics (2014), University of Bergen PhD in Visualization (2018), University of Bergen Research Interests: His work spans data visualization, scientific visualization, and interactive techniques. Notable contributions include Vol²velle (printable interactive volume visualization widgets), Firefly (virtual illumination drones), and LinesLab (low-cost art generation systems). He explores methods to bridge the gap between virtual and physical visual representations, emphasizing user-centric design. Publications: His recent work includes advancements in hexagonal map visualization (2022), spatio-temporal selection techniques (2020), and illumination automation (2019). These contributions highlight his focus on enhancing data interpretation through novel interaction paradigms. Labs/Teams: Active member of the VisGroup at the University of Bergen, collaborating on projects like MetaVis and VIDI. His work is supported by grants involving visualization and medical imaging applications.

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.

• Earth Observation
• Remote Sensing
• Sea Ice Dynamics
• +5 more

Jack Christopher Landy is an Associate Professor in the Department of Physics and Technology at UiT The Arctic University of Norway , specializing in Earth Observation and Arctic sea ice dynamics . His work integrates satellite altimetry , microwave backscatter , and data assimilation to study climate impacts on polar ice. Research Focus : Arctic sea ice thickness and freeboard estimation Snow depth modeling via CryoSat-2 and ICESat-2 Multi-decadal SAR analysis of ice types Climate change effects on ice roughness and transport Collaborations : Torgny Vinje Ice and Climate Laboratory INTERAAC (Norway-China air-snow-ice-ocean interactions) Multi-institutional Arctic climate projects

• Artificial Intelligence
• Process Control
• Systems Optimization
• +4 more

Idelfonso Nogueira is an Associate Professor in the Department of Chemical Engineering at NTNU. His research focuses on integrating artificial intelligence, advanced control systems, and digitalization to address industrial challenges, particularly in chemical processes and product development. He leads the AiP²S² framework, emphasizing synergies between AI and traditional engineering methods. Education: PhD in Chemical and Biological Engineering (University of Porto, 2018), Visiting Researcher at Tampere University of Technology (2016–2018), and multiple master’s degrees from Brazilian and Portuguese institutions. Research Interests: Process optimization using AI, hybrid modeling, digital twins, and Industry 4.0/5.0 applications. Collaborations span universities, institutes, and industries globally. Teaching focuses on preparing engineers for modern industrial demands. Publications: Over 60 manuscripts, with 70% in Q1 journals (e.g., Chemical Engineering Science , Computers and Chemical Engineering ). Key areas include pressure swing adsorption, machine learning for process control, and fragrance molecule design. Grants/Advising: Active in parameter estimation, process modeling, and renewable energy projects. No explicit student advising list provided. Labs/Teams: Engages in AI-driven process systems engineering, digital twin development, and sustainable technology initiatives.

Ingrid Hobæk HaffView profile

Associate Professor

• Multivariate statistics
• Copulae
• Skew distributions
• +3 more

Ingrid Hobæk Haff is an Associate Professor in insurance mathematics and statistics at the Department of Mathematics, University of Oslo since 2015. She holds a master's degree in Industrial Mathematics from NTNU (2002) and a PhD from the Statistics for Innovation center (2008–2012), with a 20% position as a research scientist at the Norwegian Computing Centre. Previously, she worked there as a research scientist and senior scientist. Her research interests focus on multivariate statistics, copulae, skew and heavy-tailed distributions, and applications in insurance and finance. She has contributed to advancements in statistical modeling, particularly in copula constructions and their applications to risk assessment and extreme value analysis. Key awards include the Sverdrup award for young scientists and the mathematical award of Hanna og John Olav Stubban . She is affiliated with the Statistics and Data Science research group and the completed Stochastics of Renewable Energy Markets (STORE) project. Her work spans interdisciplinary collaborations, including applications in immunology and bioinformatics, leveraging machine learning for antibody-antigen interaction studies and synthetic data generation.

Kana BannoView profile

Research Fellow

• Salmon Aquaculture
• Fish Behaviour
• Fish Welfare
• +4 more

Kana Banno is a PhD Research Fellow at the Department of Biological Sciences Ålesund, Faculty of Natural Sciences at the Norwegian University of Science and Technology (NTNU). Her research focuses on salmon aquaculture, with particular emphasis on fish behavior and welfare in commercial sea cages. She utilizes advanced technologies including underwater cameras, ROVs, echosounders, and sonars for aquaculture monitoring. Banno earned her PhD in Biology from NTNU in 2025, following an MSc in Biology with a focus on aquaculture and genetics from UiT The Arctic University of Norway in 2019. Her educational background includes: PhD in Biology, NTNU (2025) MSc in Biology (Aquaculture & Genetics), UiT The Arctic University of Norway (2019) Banno's research interests center around understanding fish behavior as an indicator of welfare and health in aquaculture settings. She investigates how environmental factors affect the distribution and behavior of Atlantic salmon in commercial sea cages. Her work bridges biology and engineering through the integration of technology for monitoring purposes. She has particular expertise in using computer vision and artificial intelligence to identify growth-stunted salmon and monitor wild fish interactions around aquaculture sites. Her research contributes to developing more sustainable and welfare-conscious aquaculture practices. Analysis of her publication record reveals a strong focus on technological applications in aquaculture monitoring, with particular emphasis on computer vision, sonar technology, and multi-sensor integration. Her work spans both practical applications for the aquaculture industry and fundamental research on fish behavior. The publications show a progression from molecular genetics (her master's work on salmon lice) to more applied technological solutions for monitoring fish welfare in commercial settings. Her notable achievements include: Featured in 'Women in aquaculture' series by The Fish Site (2021) Research on AI for wild fish monitoring highlighted by Norwegian SciTech News (2021) Banno teaches BIA3003 Introduction to scientific method (2024 Autumn) and has been actively involved in the InnoSEA project (Innovations for Sustainable sEabased Aquaculture). Her fieldwork involves regular visits to salmon farms approximately 1.5 hours from her base, where she monitors salmon behavior using underwater cameras and remotely operated vehicles. She collaborates extensively with researchers from multiple countries including Argentina, China, Germany, Japan, and Norway. She maintains strong connections with the aquaculture industry, having previously worked at FRD Japan, a land-based RAS facility producing rainbow trout, where she gained practical experience in fish farming operations before transitioning to research.

• 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.

John Faulkner BurkhartView profile

Professor

• Hydrology
• Cryosphere
• Water resources
• +3 more

John Faulkner Burkhart is a Professor II (20% position) in the Section of Physical Geography and Hydrology at the Department of Geosciences, University of Oslo. His primary professional appointment is at Statkraft, Norway's largest renewable energy company, indicating a significant industry-academia connection. Based at the Geology Building on Sem Sælandsvei 1 in Oslo, he maintains dual institutional affiliations that bridge theoretical research with practical applications in water resources and energy. Dr. Burkhart's research expertise spans multiple interconnected environmental domains. His primary focus areas include Hydrology, Cryosphere dynamics, and Water resources management, with particular emphasis on mountainous and polar regions. His work investigates the complex interactions between atmospheric processes, snow and ice dynamics, and hydrological systems, especially in the context of climate change. Geographic regions of particular focus include the Himalayas, European mountain systems, and polar environments. Analysis of Dr. Burkhart's recent publication record reveals a clear trajectory toward integrating advanced computational methods with environmental observations. His research increasingly incorporates machine learning techniques for hydrological modeling and parameter identification, while maintaining strong foundations in traditional physical modeling approaches. A significant portion of his work focuses on improving hydrological predictions through innovative data assimilation techniques, particularly using remote sensing data for snow cover monitoring. This research has direct applications for water resource management, especially in the context of hydropower operations in mountainous regions. Dr. Burkhart maintains extensive international collaborations, as evidenced by his numerous multi-author publications involving researchers from institutions across Europe, Asia, and North America. His work consistently appears in high-impact journals across multiple environmental disciplines, reflecting the interdisciplinary nature of his research. Current research directions appear to focus on enhancing the predictive capabilities of hydrological models through the integration of diverse data sources and advanced computational methods, with particular relevance for climate change adaptation in water-stressed regions.