Find Academic Supervisors

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.

Inge HoffView profile

Professor

• Materials for road construction
• Frost protection
• Laboratory testing
• +5 more

Professor Inge Hoff is affiliated with the Norwegian University of Science and Technology (NTNU) in the Department of Civil and Environmental Engineering, where he has served since 2009. Prior to this, he held roles as senior researcher and research leader at SINTEF. Research Interests : Materials for road construction, frost protection, laboratory testing, pavement dimensioning, road rehabilitation, state development modeling, ground-penetrating radar surveys, and concrete/natural stone coverings. Students : Mentors active PhD fellows Lisa Hannasvik, Arman Hamidi, Clara Weber, and Shoiab Ahmad. Teaching : Coordinates courses like TBA4204/BYGT1102 Transport Infrastructure , BYGT2204 Road and Railway Construction , and BA8600 Pavement Structure Dimensioning . Recent publications highlight his expertise in granular material behavior, asphalt durability under climate stressors, and advanced structural assessment techniques. Collaborations with international researchers and presentations at major conferences (TRB, International Conference on Bituminous Mixtures) demonstrate his ongoing contributions to road engineering.

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.

• 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.

Ole-Christoffer GranmoView profile

Professor

• Machine Learning
• Artificial Intelligence
• Tsetlin Machines
• +7 more

Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.

• 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.

Elisabeth FevangView profile

Research Fellow

• Labour Market Economics
• Health Economics
• Welfare System Analysis
• +3 more

Elisabeth Fevang is a Senior Research Fellow at the Frisch Centre, affiliated with the University of Oslo. She completed her PhD at the University of Bergen in 2019 with a dissertation titled "Economic Incentives, Employment and Health." Her research focuses primarily on labor market economics and health economics, with particular attention to welfare systems, disability insurance, sick leave patterns, and elderly care economics. Dr. Fevang's research interests center on the intersection of health and labor economics. She investigates how economic incentives affect work behavior, particularly regarding sick leave, disability, and care responsibilities. Her work often examines the Norwegian welfare system's impact on labor market participation, with specific focus on vulnerable groups such as elderly caregivers and healthcare workers. She frequently employs register-based data analysis to study policy impacts on labor supply decisions. Her publication record shows consistent output with multiple high-impact articles in 2024, indicating active research in current labor market issues. Her recent work has increasingly focused on healthcare sector employment patterns, particularly shift work schedules in municipal care services, and their relationship to sick leave. Earlier publications established her expertise in disability insurance systems and the "sick pay trap" phenomenon where benefit structures may inadvertently encourage prolonged absence from work. Dr. Fevang is actively involved in numerous research projects at the Frisch Centre, including those examining labor market exclusion, social insurance systems, long-term care effects on elderly workers' participation, and effects of welfare system reforms. Her collaborative work with researchers like Knut Røed, Simen Markussen, and Heidi Gautun demonstrates her integration within Norway's economic research community.

Milica OrlandicView profile

Associate Professor

• Hyperspectral Imaging
• Remote Sensing
• Digital Design
• +7 more

Milica Orlandic is an Associate Professor in the Department of Electronic Systems at NTNU. She holds an MSc from the University of Montenegro (2009) and a PhD from NTNU (2015). Her research focuses on hyperspectral imaging, remote sensing, FPGA-based systems, and embedded computing for aerospace applications. She is actively involved in the HYPSO CubeSat mission, developing onboard processing systems for Earth observation. Education: MSc in Electrical Engineering, University of Montenegro (2009) PhD in Electronics, NTNU (2015) Research Interests: Her work spans hyperspectral data processing , including compression, anomaly detection, and onboard computing for satellites. She also explores reconfigurable hardware (FPGAs) for real-time signal processing, cyber-physical systems, and spaceborne sensor systems. Publications Trends: Recent work emphasizes lightweight machine learning for anomaly detection, FPGA acceleration of hyperspectral compression (CCSDS 123), and algorithm co-design for CubeSat missions. Key contributions include robust onboard processing frameworks for HYPSO-1 and adaptive hardware-software systems. Advising & Teams: She supervises a dynamic team of over 40 PhD and MSc students working on FPGA implementations, satellite systems, and hyperspectral algorithms. Notable collaborations include the HYPSO CubeSat project, which aims to deliver high-resolution Earth observation data with low latency. Labs & Infrastructure: Her research leverages NTNU’s facilities for embedded systems prototyping, FPGA development, and CubeSat payload testing. The HYPSO mission integrates her team’s hardware-software co-design innovations for space applications.

• Anesthesiology
• Emergency Medicine
• Cardiopulmonary Resuscitation (CPR)
• +3 more

Eirik Skogvoll is a Professor at NTNU (Norwegian University of Science and Technology) and a pediatric anesthesiologist at St. Olav's University Hospital. He holds a leadership role as Vice Head for Research in the ISB Research group within the Faculty of Medicine and Health Sciences. His primary affiliation is with the Department of Circulation and Medical Imaging. Skogvoll's academic background includes an MD (1985), PhD (2000), and certifications in anesthesiology (1993) and pediatrics (2005). He has been actively involved in clinical work, teaching, and research at NTNU since 1994. Notably, he served as Head of the Resuscitation Committee at St. Olav's Hospital from 2008 to 2019 and was a visiting professor at the University of Pennsylvania in 2017. His research focuses on anesthesiology, emergency medicine, and cardiopulmonary resuscitation (CPR), with a particular emphasis on clinical circulatory physiology and medical statistics. He leads the Norwegian Cardiac Arrest Registry and has contributed to courses like Pediatric Emergencies and Medical Statistics in Clinical Analysis. Recent articles highlight advancements in AI-driven diagnostics (e.g., deep learning for ECG analysis), optimization of CPR techniques, and pediatric cardiac arrest epidemiology. His work bridges clinical practice and technological innovation, aiming to improve patient outcomes in critical care scenarios.

Riccardo SimionatoView profile

Research Fellow

• Deep Learning
• Audio Modeling
• Sound Synthesis
• +3 more

Riccardo Simionato is a Research Fellow in the Department of Musicology at the University of Oslo (UiO), affiliated with the Faculty of Humanities. He holds a MSc and BSc in Computer Science and Information Engineering from the University of Padova, Italy, and conducted research at Aalto University, Finland (2017–2018). His research focuses on nonlinear audio modeling using deep learning, particularly addressing low-latency interactive solutions for acoustic and electronic musical instruments/devices. Education: 2018: MSc in Computer Science Engineering, University of Padova 2015: BSc in Information Engineering, University of Padova Research Interests: Deep Learning , Audio Modeling , and Sound Synthesis . He explores how deep learning can approximate complex nonlinear phenomena in audio systems, balancing computational efficiency with interpretability. Recent work emphasizes hybrid neural-audio effects, time-variant systems, and physics-informed methods for piano modeling. Publications highlight advancements in optical compressor modeling, piano analysis, and tools for generating audio effect datasets. His work bridges machine learning and music technology, aiming for practical applications in real-time audio processing and electronic instrument emulation. Grants and advising: No specific grants or student advisees listed in the provided text. Collaborations include projects with Prof. Stefano Fasciani and teams at UiO’s Department of Musicology. Labs/Teams: Active within UiO’s Sound and Music Computing research group, focusing on interdisciplinary projects combining computer science and music technology.

Sabrina SartoriView profile

Professor

• Energy Storage
• Renewable Energy
• Energy Systems
• +3 more

Sabrina Sartori is a Professor and Section Head at the University of Oslo's Energy Systems Section, Institute of Theoretical Astrophysics. Her research bridges energy technology and astrophysics, focusing on sustainable power solutions for remote astronomical facilities. Energy Systems Section, University of Oslo AtLAST Telescope Project Consortium Her work centers on energy storage , renewable energy integration , and materials science for hydrogen technologies, combining experimental characterization with life cycle assessments. 2025 : Dual-phase alloys for room temperature hydrogen storage 2024 : Sustainable off-grid telescope power systems Recent publications analyze offshore wind-hydrogen systems , photochromic materials , and metal hydride scalability , reflecting her multidisciplinary approach to decarbonization.

• Signal Processing
• Engineering Education
• Frequency Domain Radars
• +3 more

Lars Magne Lundheim is a Professor at the Department of Electronic Systems at Norwegian University of Science and Technology (NTNU). He holds an MSc and PhD in electrical engineering from NTNU. His career includes roles as Associate Professor (2002–2010), Research Scientist at SINTEF, and part-time academic positions. He belongs to the Signal Processing Group and focuses on signal processing for radar systems, power consumption optimization, and engineering education. His research spans technical domains like multicarrier communication systems and pedagogical innovations such as project-based learning and curriculum design. Notable contributions include work on sustainability integration in STEM education and competency development through active learning strategies. Lundheim is a Senior Member of IEEE and has reviewed for major journals/conferences including IEEE Transactions on Signal Processing. Recent publications emphasize educational frameworks linking mathematics with engineering practice, while earlier works address technical challenges in radar distortion correction and power-efficient multiplier design. He has contributed to 16 PhD examination committees and teaches courses like TTT4270 (Electronic System Design) and TTT4260 (Electronic System Design and Analysis). Lundheim’s outreach includes presenting at events like the Læringsfestivalen and collaborating on national initiatives like the 'Fremtidens teknologistudier' educational strategy report. His interdisciplinary work bridges technical innovation with pedagogical excellence in electrical engineering education.

Clinton Phillips ConradView profile

Professor

• Geodynamics
• Geophysics
• Tectonics
• +4 more

Clinton Phillips Conrad is a Professor of Mantle Dynamics at the University of Oslo's Department of Geosciences and Centre for Planetary Habitability. His research focuses on understanding Earth's dynamic interior through 3D modeling of mantle deformation, constrained by seismic/geodetic observations and geological indicators. He studies interactions between Earth's interior and surface processes including topography, plate motions, volcanism, and climate change. Research Interests Conrad's group develops advanced models examining how mantle dynamics influence surface environments. Key research areas include: Mantle geodynamics and deformation mechanisms Interactions between deep Earth processes and surface topography/volcanism Impacts of ice mass changes on solid Earth deformation Seismic and magnetotelluric constraints on mantle properties Evolution of plate tectonics and continental stability His work integrates geophysical observations with computational modeling to understand planetary habitability. Publication Analysis Conrad's recent publications (2021-2025) demonstrate a strong focus on: Mantle water distribution and its role in volcanism Glacial-isostatic adjustment and ice sheet interactions Anisotropic viscosity and mantle rheology Plume-lithosphere interactions in continental settings Cratonic stability mechanisms Sea level and paleogeographic reconstructions Methodologically, his work combines numerical modeling with seismic, geodetic, and magnetotelluric data analysis. Projects and Leadership Conrad leads several major research initiatives: ANIMA: ANIsotropic Viscosity in MAntle Dynamics ERC Starting Grant: DYNAMICE (ice-mantle dynamics interplay) MAGPIE: Magnetotelluric Analysis for Greenland and Postglacial Isostatic Evolution POLARIS: Evolution of the Arctic in deep time He maintains the MAGPIE blog documenting field research and maintains a research group developing mantle convection models.

• 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.

• Image Processing
• Machine Learning
• Artificial Intelligence
• +7 more

Dilip K. Prasad is a Professor at the Department of Informatics, UiT The Arctic University of Norway. His work bridges Artificial Intelligence and Medical Imaging , with a focus on Interpretable AI , Scalable AI , and Life Science Applications . He has contributed to Maritime Technology and Biomedical Engineering . Ph.D. and B.Tech from Nanyang Technological University and IIT Dhanbad Senior Research Fellow at NTU (2015-2019), Research Fellow at NUS (2012-2015) Industry experience at IBM, Infosys, Mediatek, Philips His research explores Image Processing , Machine Learning , and AI Applications in Biomedicine . Recent work includes Dense Video Captioning , 3D Mitochondrial Modeling , and Physics-Guided Loss Functions . Articles span Neurocomputing , Optics Express , and top AI conferences like CVPR and NeurIPS . Prasad has received the Rolls-Royce Inventor Award (2016) and Best Paper Award (IJCIE 2017) . He has reviewed for 50+ journals and 30+ conferences, serving as Area Chair for NeurIPS 2022-23 and Organizer Chair for ICCV Workshop 2023 .