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.

• Artificial Intelligence
• Machine Learning
• AI Alignment
• +2 more

Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.

Swati AggarwalView profile

Professor

• Artificial Intelligence
• AI in Health and Medicine
• AI in Society
• +6 more

Swati Aggarwal is a Professor in Artificial Intelligence at the Faculty of Logistics, Molde University College (HiMolde). Her research focuses on AI applications in healthcare, ethics, cognitive development, and neural networks. She holds a PhD in Neutrosophic Neural Networks, a Master's in Information Technology, and a Bachelor's in Computer Science and Engineering. Previously, she was a Marie Curie Postdoc Fellow at NTNU, working on AI models for cognitive assessment in infants (AIM_COACH project). Research Interests - AI in Health/Medicine - Ethics in AI and Societal Impact - EEG/BCI for Cognitive Assessment - Machine Learning and Deep Learning Publications Her recent work spans AI ethics, BCI applications, adversarial attacks, and healthcare diagnostics. Notable contributions include EEG-based infant perceptual monitoring (2025) and malaria detection via EfficientNet (2023). She also explores cross-lingual adversarial robustness and blockchain in hospitality systems. Labs/Teams - ABC-AI: Applied, Basic, and Conscientious AI Group - Virtual Technologies and Learning Research Group

Anne H Schistad SolbergView profile

Professor

• Machine Learning
• Deep Learning
• Image Analysis
• +6 more

Anne H Schistad Solberg is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. She leads research in digital signal processing and image analysis, with a focus on machine learning applications across multiple domains. As co-director of SFI Visual Intelligence, she oversees research on interpretable deep learning models, uncertainty quantification, contextual learning, and self-supervised learning approaches. Her research spans medical imaging (particularly cardiovascular ultrasound), environmental monitoring using satellite imagery, and seabed mapping with sonar technology. Professor Solberg's work demonstrates a consistent trajectory from foundational signal processing techniques to cutting-edge deep learning applications. Her recent publications show increasing specialization in medical image analysis, particularly in echocardiography enhancement and cardiac structure segmentation, while maintaining strong contributions to remote sensing and geophysical applications. She teaches several popular courses including IN2070, IN3310, and IN5400 (Machine Learning for Image Analysis), which is noted as the most popular master's/PhD course on deep learning at the University of Oslo. Professor Solberg serves as principal investigator for the Intelligent Cardiovascular Ultrasound Scanner (INCUS) project, collaborating with GE Vingmed Ultrasound to develop AI-enhanced cardiac imaging systems that improve diagnostic accuracy and productivity in echocardiography. Co-director of SFI Visual Intelligence research center Principal Investigator for the INCUS project (Intelligent Cardiovascular Ultrasound Scanner) Member of the Digital Signal Processing and Image Analysis (DSB) research group Member of the Strategic Research Initiative: Multimodal Medical Imaging and Image Analysis (MEDIMA) Her research group develops algorithms that address real-world challenges in medical diagnostics and environmental monitoring, with a particular emphasis on making deep learning models more interpretable and reliable for critical applications. The INCUS project, funded through User-driven Research-based Innovation (BIA), aims to reduce the time wasted during cardiac ultrasound examinations by implementing intelligent algorithms that learn from expert users and historical data.

• Biomechanics
• Human Kinetics
• Biomedical Engineering
• +3 more

Dr. Ryan B Graham is an Associate Professor in the School of Human Kinetics at the University of Ottawa , with cross-appointments to the Ottawa-Carleton Institute for Biomedical Engineering . He holds Adjunct Assistant Professor positions at Queen's University and University of Waterloo , reflecting his interdisciplinary collaborations in biomechanics and biomedical engineering. His research focuses on spine movement analysis , motion capture validation , and sensor technology applications in clinical and military contexts. Recent work includes developing markerless motion capture systems, assessing movement reliability, and modeling spine dynamics for injury prevention. Editorial Contributions: Associate Editor for Biomechanics and Control of Human Movement at Frontiers in Sports and Active Living Dr. Graham's academic network includes collaborations with institutions in Canada, the UK, and the Netherlands, with a focus on advancing biomechanical methodologies and their clinical translation.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +9 more

Jim Torresen is a Professor at the Norwegian University of Science and Technology (NTNU), specializing in Computer Science, Artificial Intelligence, and Robotics. He earned his M.Sc. and Dr.ing. (Ph.D.) in computer architecture and design from NTNU in 1991 and 1996 respectively, followed by industry experience in hardware design before transitioning to academia in 1999. Research Interests: His work spans Machine Learning, Evolvable Hardware, and Ethical AI, with notable contributions to music technology, facial expression recognition, and healthcare monitoring systems. He actively explores interdisciplinary applications of AI in creative domains and clinical environments. Publications & Editorial Roles: Torresen has published extensively in journals like Frontiers in Artificial Intelligence and Genetic Programming and Evolvable Machines . He serves as a Topic Editor for Frontiers in Explainable AI and has editorial roles in robotics and biomedical AI domains.

Gabriele de SetaView profile

Researcher

• Digital ethnography
• Chinese internet culture
• Algorithmic folklore
• +4 more

Gabriele de Seta is a researcher at the University of Bergen's Department of Linguistic, Literary and Aesthetic Studies, leading the ALGOFOLK project funded by the Trond Mohn Foundation (2024-2028). A sociologist by training with a PhD from Hong Kong Polytechnic University, he previously served as postdoctoral fellow at Academia Sinica (Taipei) and on the ERC-funded Machine Vision in Everyday Life project. Department of Ethnology, Academia Sinica (postdoctoral fellow) Department of Linguistic, Literary and Aesthetic Studies, University of Bergen (researcher) His research focuses on algorithmic folklore, synthetic ethnography, and Chinese digital culture. He examines how digital technologies and creative practices mutually shape each other, particularly in China's sociotechnical landscape. Key areas include platform governance, machine vision, algorithmic accountability, and digital vernaculars. Recent publications analyze TikTok/Douyin's infrastructural role, deepfakes, QR codes as globalized gateways, and Chinese internet vulgarity. His work combines theoretical frameworks from STS, media studies, and digital anthropology with fieldwork on Chinese digital practices. Trond Mohn Foundation Starting Grant ERC-funded research As educator, he designed the Machine Vision module for Bergen's Critical Approaches to Technology and Society course and taught Key Theories in Digital Culture. He supervises multiple MA/PhD students including co-supervision of Hanna Lauvli. Through his blog and podcast, he promotes public engagement with digital research.

Katrine EldegardView profile

Professor

• Conservation Biology
• Behavioural Ecology
• Population Ecology
• +7 more

Katrine Eldegard is a Professor at the Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Department of Ecology and Natural Resource Management (INA). She leads BatLab Norway and contributes extensively to national and international conservation science policy. Institution: Norwegian University of Life Sciences School: Faculty of Environmental Sciences and Natural Resource Management Department: Department of Ecology and Natural Resource Management Position: Professor Her research centers on understanding how human activities and land use affect natural ecosystems and species across taxa and spatial scales. She specializes in the behavioral, population, and community-level responses of mammals, birds, and insects to anthropogenic pressures such as energy infrastructure, transport networks, and forestry. A major focus is on bat ecology and conservation, pollination dynamics, and biodiversity monitoring in boreal and agricultural landscapes. Her recent publications reveal strong trends in climate change impacts on bat morphology and distribution, pollinator-plant interactions under environmental change, and the ecological consequences of infrastructure development. These works integrate field ecology with advanced modeling and policy-relevant assessments. She has played leading roles in key scientific committees: Chair, Mammal Committee, Norwegian Red List for Species (2021) Chair, Mammal Committee, Norwegian Alien Species List (2023) Member, Norwegian Scientific Committee for Food and Environment (VKM), CITES Expert Panel Norway’s representative, UNEP/Eurobats Advisory Committee Eldegard has supervised numerous research projects and collaborated with government agencies and private partners on applied ecology. She teaches courses including NATF200 Vern og forvaltning av norsk natur and the upcoming NATF300 Conservation Science. Her work is supported by extensive fieldwork, interdisciplinary collaboration, and integration of ecological theory with practical conservation. She leads BatLab Norway, a research group dedicated to advancing knowledge on bat ecology, behavior, and conservation through innovative methods including telemetry, acoustic monitoring, and landscape analysis.

• 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

• Verification and Validation
• Search-Based Software Engineering
• Autonomous Driving
• +3 more

Shaukat Ali serves as Research Professor and Head of the Department of Engineering Complex Software Systems at Simula Research Laboratory, concurrently holding the title of Chief Research Scientist. His academic leadership drives innovation at the critical nexus of quantum computing, artificial intelligence, and software engineering, with concentrated expertise in verification, validation, and testing methodologies for complex systems including cyber-physical infrastructures and autonomous robotics. His primary research domains encompass: Verification and Validation Search-Based Software Engineering Autonomous Driving Systems Cyber-Physical Systems Engineering Digital Twin Technologies Quantum Software Engineering Analysis of recent publications (2024-2025) reveals a decisive trend toward quantum-AI convergence in software engineering, particularly through quantum software testing frameworks and AI foundation models applied to cyber-physical systems. His work systematically addresses noise mitigation in quantum hardware, uncertainty quantification in adaptive robotics, and novel testing paradigms using vision-language models for industrial robotics—demonstrating both theoretical rigor and industrial applicability. As department head, Ali spearheads strategic research directions in complex software systems, fostering cross-disciplinary collaboration while actively shaping quantum software engineering through workshops like QAI2024 and Q-SANER 2024. His invited presentations at venues including JYU Quantum Electronics and EU-Korea Quantum Forums underscore his influence in defining emerging research landscapes.

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.

• Anthropology of Science and Technology
• Computer Supported Collaborative Work
• Digitalization
• +9 more

Vidar Hepsø is a Professor at the Department of Computer Technology and Informatics, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). His work bridges anthropology of science and technology with practical challenges in digitalization, energy transition, and remote operations. Research focuses on digital infrastructures, socio-technical systems, and human factors in oil and gas industries Active in NTNU Applied Information Technology and NTNU Energy Transition Initiative Publications emphasize open-source ecosystems, autonomous systems, and environmental monitoring His scholarly output spans computer-supported collaborative work, IT infrastructure governance, and risk-informed anomaly detection in subsea systems. He leads projects connecting digital innovation with offshore wind and petroleum geoscience.

• Numerical Analysis
• Differential Equations
• Geometric Numerical Integration
• +5 more

Elena Celledoni is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). She has been employed at NTNU since 2004 and has held the position of professor since 2009. She is a member of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and serves as its leader. Her educational background includes: Master's degree in Mathematics from the University of Trieste (1993) Ph.D. in Computational Mathematics from the University of Padua, Italy (1997) Elena Celledoni's research focuses on numerical analysis, particularly structure preserving algorithms for differential equations and geometric numerical integration. Her work bridges theoretical mathematics with practical computational methods, developing algorithms that maintain the geometric properties of the systems they approximate. She has made significant contributions to Lie group integrators, energy-preserving methods, and the application of these techniques to mechanical systems and shape analysis. In recent years, her research has expanded to include the intersection of numerical methods with machine learning, exploring how structure-preserving approaches can enhance neural networks and data-driven modeling. Her publications demonstrate a clear trend toward integrating traditional numerical analysis with modern machine learning techniques while maintaining a strong foundation in geometric integration and structure preservation. This interdisciplinary approach has led to innovations in neural ODEs, structure-preserving neural networks, and physics-informed machine learning models that respect the underlying mathematical structures of the systems they model. Elena Celledoni has received recognition for her work through the following honors: Member of the Royal Norwegian Society of Sciences and Letters Member of the European Consortium of Mathematics in Industry Council Member of the board of the International Council of Mathematics in Industry and Applications Editorial board member for SIAM Review, Journal of Computational Dynamics, Journal of Geometric Mechanics, Calcolo, and Networks and Heterogeneous Media As an advisor, she has mentored several students including Torbjørn Ringholm who completed his doctoral dissertation on 'Discrete gradient methods in image processing and partial differential equations on moving meshes.' Her research has been supported by various grants enabling her to lead projects on geometric numerical integration, collaborate internationally, and organize significant academic events such as the special semester at Isaac Newton Institute of MS in 2019 on 'Geometry, compatibility and structure preservation.' She leads the Differential Equations and Numerical Analysis Group at NTNU, which focuses on developing and analyzing numerical methods that preserve the geometric structure of differential equations. The group maintains active collaborations with researchers worldwide and has made substantial contributions to advancing the field of geometric numerical integration and its applications to real-world problems.

Mohan Lal KolheView profile

Professor

• Smart Grid
• Renewable Energy Systems
• Electric Vehicles
• +5 more

Professor Mohan Lal Kolhe is a distinguished academic at the University of Agder , serving as a Full Professor in Smart Grid and Renewable Energy within the Faculty of Engineering and Science and the Department of Engineering Sciences . With over three decades of international academic experience, he has held positions at prestigious institutions including University College London, University of Dundee, and Hydrogen Research Institute in Canada. His career spans technical innovation, policy development (e.g., as a member of South Australia’s Renewable Energy Board), and extensive research leadership in sustainable energy systems. Research Leadership : Focus on Smart Grid integration, Electric Vehicles, Hydrogen Energy, Solar/Wind Systems, and Techno-Economic Energy Analysis. Global Recognition : Listed in the top 2% of scientists worldwide (2020-2023) by Stanford University, with 10 publications averaging 200+ citations. Recent publications emphasize advanced optimization techniques for renewable integration, EV charging infrastructure, hydrogen production, and power system stability. His work has secured competitive funding from entities like the Norwegian Research Council and EU programs. Awards and Expert Roles : Top 2% Global Scientist (Stanford, 2020-2023) Highly Cited Researcher (Top 10 publications, 200+ avg. citations) Expert evaluator for European Commission, Royal Society London, EPSRC, and Cyprus Research Foundation He actively contributes to international conferences as keynote speaker and editorial board member, with leadership roles in research groups like Autonomous and Cyber-Physical Systems and Energy Systems .

• Machine Learning
• Information Theory
• Deep Learning
• +6 more

Zhiyuan Wu is a Doctoral Research Fellow at the University of Oslo , affiliated with the Digital Signal Processing and Image Analysis research group under the Faculty of Mathematics and Natural Sciences . Education: Bachelor’s degree in Communication Systems and Information Technology from Lanzhou University, China Master’s degree from the Technical University of Munich, School of CIT Research Focus: Zhiyuan Wu specializes in machine learning, with particular emphasis on probabilistic graphical models, information theory, and tackling real-world challenges such as distributional shifts and privacy concerns. His work explores entropy regularization techniques to address label shift in distributed learning systems, aiming to improve model robustness and data privacy across diverse domains like medical applications. Publications & Research Trends: His recent publication at the International Conference on Learning Representations highlights a novel approach to mitigating label shift through entropy regularization. This aligns with his broader research goals of enhancing the adaptability and interpretability of machine learning models in dynamic, privacy-sensitive environments. Labs & Teams: He is actively involved with the Digital Signal Processing and Image Analysis (DSB) research group, contributing to collaborative projects that bridge theoretical advancements with practical implementations in machine learning.