Find Academic Supervisors

• 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.

• 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

• Wireless Communications
• Machine Learning
• Digital Twins
• +5 more

Pierluigi Salvo Rossi is a Professor at the Department of Electronic Systems , Norwegian University of Science and Technology ( NTNU ), with additional roles as Deputy Head of Department (since 2021) and Deputy Manager at the Center for Green Shift in the Built Environment (since 2022). He also serves as a part-time Research Scientist at SINTEF Energy's Gas Technology department. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests span Wireless Communications , Digital Twins , Machine Learning , and Statistical Signal Processing , focusing on applications like Industrial IoT , Fault Detection , and Energy Systems . His recent Publications highlight trends in Federated Learning , Graph Signal Processing , and Multi-Sensor Anomaly Detection across domains from Natural Gas Pipelines to Subsea Leakages . Scientific Awards include: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Professional Roles encompass editorial leadership (e.g., IEEE Sensors Journal) and conference organization (e.g., General Chair for IEEE Sensor Array and Multichannel Signal Processing Workshop, 2022). He leads major funded research projects like PREFERENCE (RCN, 2023-2027) and AUTOSHIP (RCN, 2020-2028).

Alvaro Fernandez QuilezView profile

Associate Professor

• Artificial Intelligence
• Responsible AI
• AI Ethics
• +13 more

Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.

Adín Ramírez RiveraView profile

Professor

• Computer Vision
• Machine Learning
• Representation Learning
• +6 more

Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.

• Space Physics
• Cosmic Dust
• Dusty Plasma Phenomena
• +6 more

Ingrid Mann is a Professor in Space Physics at the UiT The Arctic University of Norway , Department of Physics and Technology. She leads and participates in multiple externally funded research initiatives including the Cosmic dust injection into the upper Earth atmosphere , MXD 2 rocket project to study the mesosphere , and EISCAT Research infrastructure project . ORCID: 0000-0002-2805-3265 Member of research group Space Physics Member of projects: Intermittent fluctuations in physical systems , Maxidusty-2 , CASCADE , Codia , Boosting Space Business , and Forskningsparken 1 A216 Her research spans space and atmospheric physics , focusing on dusty plasmas , cosmic dust dynamics , and polar atmosphere interactions . She employs spacecraft observations , EISCAT radar , rocket experiments , and machine learning for data analysis. Recent publications highlight cosmic dust detection with Parker Solar Probe and Solar Orbiter , PMSE multilayer properties , and dust impact signal modeling . Her work integrates radar , optical , and spacecraft data to understand polar atmospheric systems. She teaches FYS-2000 Kvantemekanikk , FYS-2019 Sun, Planets, and Space , and supervises G-Chaser student rocket projects . Her research group contributes to EISCAT_3D infrastructure and interplanetary dust modeling . Co-edited books: Nanodust in the Solar System (2012) Small Bodies in Planetary Systems (2008) Modern Meteor Science (2005)

Daniel RomeroView profile

Professor

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +5 more

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

• Data fusion
• Machine learning
• Statistical signal processing
• +6 more

Pierluigi Salvo Rossi is a Full Professor and Deputy Director at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), Norway. He leads the Ph.D. Program in Electronics and Telecommunication and serves as Deputy Manager of the Centre for Green Shift in the Built Environment. His roles include leadership in SPIN (Signal Processing for Industry N.0), a research group focusing on industrial digitalization through statistical signal processing and machine learning. Education: Ph.D. in Computer Engineering, University of Naples “Federico II”, Italy (2005) Dr.Eng. (cum laude) in Telecommunications Engineering, University of Naples “Federico II”, Italy (2002) Research Interests: His work centers on data fusion, machine learning, sensor networks, and wireless communication systems. He applies these to industrial challenges such as digital twins, IoT, and sensor validation for safety-critical systems. Recent projects include leadership in initiatives like SIGNIFY (sensor validation) and ML4ITS (machine learning for irregular time series). Awards & Roles: Recipient of the Exemplary Senior Editor Award (IEEE Communications Letters, 2018) Editorial roles include Topical Editor for IEEE Sensors Journal and Area Editor for IEEE Open Journal of the Communications Society Coordinator of major projects funded by RCN, ESA, and ERC Grants & Teams: Leads or co-leads projects such as PREFERENCE (CCS primary reference), DARIO (ESA/PRODEX), and PERSEUS (ERC MSCA-COFUND). The SPIN group collaborates across construction, energy, and ocean industries, anchored at NTNU’s Gløshaugen campus.

• Structural Dynamics
• Bridge Engineering
• Wind Engineering
• +6 more

Ole Andre Øiseth is a Professor at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), specializing in structural dynamics with focus on bridges and marine structures. He leads the structural mechanics research group. Research Interests: Wind Engineering Bridge Aerodynamics Structural Health Monitoring Operational Modal Analysis Fluid-Structure Interaction Extreme Load Analysis Key Contributions: Developed nonlinear force models for bridge aerodynamics Advanced Kalman filter techniques for wind load identification Environmental contour methods for bridge design Automated monitoring systems for long-span bridges

Ole Marius Hoel RindalView profile

Senior Lecturer

• Medical Ultrasound Imaging
• Sensor Technology
• Signal Processing
• +3 more

Ole Marius Hoel Rindal is a Senior Lecturer at the University of Oslo (UiO), affiliated with the Department of Informatics (IFI) and the Digital Signal Processing and Image Analysis research group. His research focuses on medical ultrasound imaging, sensor technology, and sports science applications. He holds a PhD in medical ultrasound beamforming from UiO and co-founded Sonair, a company developing 3D ultrasonic sensors for industrial use. Education: PhD in Medical Ultrasound Imaging, University of Oslo (2014) Research Interests: His work spans adaptive beamforming in medical ultrasound, real-time blood pressure monitoring, and biomechanical analysis of cross-country skiing techniques using microsensors and machine learning. Key contributions include advancements in software beamforming, coherence-based aberration correction, and the development of open-source ultrasound tools like the UltraSound Toolbox (USTB). Publications & Trends: Recent work emphasizes clinical validation of ultrasound technologies (e.g., fetal imaging, echocardiography) and algorithmic innovations in beamforming. Collaborations with SINTEF MiNaLab and industry partners highlight practical applications in robotics and athlete performance optimization. Awards: No specific honors listed. Advising & Grants: Guidance on projects involving wearable sensors for blood pressure monitoring and sensor-based athlete analysis. Active in open-source software development for medical imaging. Teams & Labs: Leads the Digital Signal Processing and Image Analysis group at UiO, collaborating with industry (Sonair) and academic partners on sensor innovation and imaging systems.

• Artificial Intelligence
• Machine Learning
• Bayesian Statistics
• +6 more

Hugo Lewi Hammer er professor ved Oslo Metropolitan University, tilhørende Faculty of Technology, Art and Design og Department of Information Technology – Mathematical Modeling . Hans forskning fokuserer på forbedring av pålitelighet og transparens i maskinlæring, forsterkende læring og dyb læringsmodeller gjennom metodikk innen modelltolkning, usikkerhetskvantifisering, robust statistikk og kausal inferens. Hans nylige arbeid inkluderer: AI-drevet optimering i assistert reproduksjonsteknologi (embryoutvalg og sædcelleanalyse) Medisinsk bildebehandling (polypdeteksjon, meibomkertutgang) Neural nettverkstolkning og usikkerhetsmodellering i EEG-analyse Biomekanisk prediksjon av muskelutmatting Hans publikasjoner viser mangfoldige anvendelser av AI i medisin og teknologi, med spesialvekt på: Explainable AI (XAI) i diagnostikk og behandling Usikkerhetskvantifisering i dyb læring Automatisering av medisinske prosedyrer (ICSI, embryoanalyse) Stokastisk simulering og kausal inferens Hammer er engasjert i forskningsgruppene Applied Artificial Intelligence og Mathematical Modeling og har publisert over 130 vitenskapelige artikler og 7 forskningsrapporter.

• Neural Data Science
• Systems Neuroscience
• Computational Neuroscience
• +3 more

Benjamin Adric Dunn is an Associate Professor in (Neural) Data Science at the Department of Mathematical Sciences, NTNU. His research focuses on computational neuroscience, systems neuroscience, and topological data analysis. He holds a PhD and postdoctoral experience from NTNU's Kavli Institute for Systems Neuroscience, an MS in Computational Engineering from Purdue University, and a BS in Applied Mathematics from the University of Connecticut. Previously, he worked as a Computational Methods Engineer at Pratt & Whitney, UTC. His work explores neural mechanisms underlying spatial navigation, grid cell dynamics, and neural coding in rodents. Key contributions include studies on toroidal representations in grid cells and cortical coding of 3D posture. Dunn has published extensively in journals like Nature , Science , and Neuron , with a focus on interdisciplinary methods combining topology, statistics, and neuroscience. He has collaborated on projects involving neural ensemble activity analysis and hidden variable modeling in biological data. Dunn’s research also addresses computational challenges in neuroscience, such as removing experimental variability in functional data.

• Reliability Engineering
• Maintenance Optimization
• Risk and Safety Analysis
• +8 more

Jørn Vatn is a Professor in reliability, availability, maintainability, and safety at the Norwegian University of Science and Technology (NTNU) , specifically within the Faculty of Engineering and the Department of Mechanical and Industrial Engineering . His work focuses on integrating risk assessment and maintenance strategies for critical infrastructure. MSc courses: TPK4120 Safety and Reliability Analysis , RAMS optimization PhD course: PK8200 Risk Influence Modelling and Risk Indicators Continuing Education: Maintenance Optimization , Safety and Reliability in Railway Applications , Project Risk Management , Risk Reducing Measures Research Interests span maintenance management, reliability modeling, condition-based maintenance, risk influence analysis, and digital twin applications in infrastructure. His methods emphasize statistical analysis and Markov processes to model degradation and optimize maintenance scheduling. The 15 most recent articles (2025-2022) demonstrate trends in applying Markov models , digital twins , and data-driven optimization to offshore wind turbines, railways, hydrogen pipelines, and cyber-physical systems. Subfields include predictive maintenance, stochastic degradation modeling, and infrastructure resilience. He leads the ROSS Gemini Centre and the RAMS group , advising on risk modeling and maintenance strategies. Collaborations involve SINTEF, industry partners, and international researchers.

• Artificial Intelligence
• Physics
• Computer Technology
• +4 more

Pedro Lind is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, where he serves in the Department of Information Technology with a focus on Artificial Intelligence. His academic appointments include active participation in research groups for Applied Artificial Intelligence and Mathematical Modeling. Dr. Lind's research spans interdisciplinary domains including: Biomedical AI applications (EEG classification, ECG analysis, eye tracking) Stochastic processes and complex systems modeling Trustworthy machine learning for security/privacy Physics-inspired computational methods Renewable energy statistics and modeling His recent publications demonstrate strong focus on developing novel AI methodologies for medical diagnostics (2024-2025), particularly using generative models and interpretable AI approaches for physiological data analysis. He leads significant research initiatives including the AI-Mind project developing diagnostic tools for dementia. Additional projects include international technology transfer collaborations with Czech Republic institutions. Dr. Lind maintains active research teams and labs focused on computational neuroscience and applied AI.

Pardhasaradhi BethiView profile

Research Fellow

• Machine Learning
• Deep Learning
• Signal Processing
• +7 more

Pardhasaradhi Bethi is a Research Fellow at the Department of Information and Communication Technology , University of Agder , specializing in lightweight deep learning models for edge computing and radar signal processing. Focus areas include object classification, sound recognition, and drone surveillance Collaborates with colleagues like Linga Reddy Cenkeramaddi and Srihari Pathipati Research Interests encompass edge AI , radar-communication convergence , and real-time IoT integration . His work emphasizes optimizing neural networks for low-power devices and improving tracking frameworks. Publication Trends show a strong focus on automotive radar , drone detection , and human activity analysis using deep learning techniques.