Hans Petter Hildre is Head of Department at the Department of Ocean Operations and Civil Engineering , part of the Faculty of Engineering at the Norwegian University of Science and Technology (NTNU). His work focuses on maritime engineering, digital twin technology, and marine operations. Research interests include: Digital Twin Applications in Maritime Industry Offshore Operations and Wind Turbine Installation Marine Robotics and Autonomous Systems Wave Field Estimation and Environmental Load Analysis Human-Machine Interaction in Maritime Contexts Co-simulation and Real-time Monitoring Recent publications highlight trends in: Wave shielding effects for offshore vessels Knowledge transfer from automotive/aviation to maritime Crane path planning using digital twins Visual attention zone recognition systems Hydrodynamic modeling and sensitivity analysis Smart city-maritime integration Scientific collaborations span institutions including: European Commission (Future Skills Reports) Royal Institution of Naval Architects The American Society of Mechanical Engineers (ASME) IEEE Transactions on multiple domains Springer Publishing
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.
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.
Halvard Buhaug is a Professor II in Political Science at the Norwegian University of Science and Technology (NTNU) and a Researcher I at the Peace Research Institute Oslo (PRIO). His work focuses on the security implications of climate change, including food security, economic shocks, forced migration, and natural disasters. Affiliations: NTNU (Political Science), PRIO (Researcher I) Research Areas: Climate-conflict nexus, environmental security, migration dynamics, peace research Expertise: Quantitative conflict analysis, climate risk modeling, policy-relevant research Buhaug's recent publications analyze climate-induced displacement, conflict risk projections, and the limitations of climate migration models. His interdisciplinary work bridges climate science, political geography, and security studies. Methodologically, Buhaug employs statistical modeling, geospatial analysis, and comparative case studies. He contributes to policy debates through accessible publications in outlets like The Conversation and Political Violence @ A Glance .
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.
Ida Scheel is an Associate Professor in Statistics and Data Science at the University of Oslo , Department of Mathematics. She specializes in Bayesian hierarchical modeling, recommendation systems, and stochastic processes on networks. Her research interests include: Bayesian statistics and model diagnostics Data science applications in environmental and health domains Network-based machine learning Uncertainty quantification in predictive modeling Recent publication trends show a focus on Bayesian model validation, machine learning for product adoption prediction, and real-estate analytics. She contributes to interdisciplinary projects like BigInsight and CELS . Scientific awards : Sverdrup Prize for Young Researchers (2011) Advising : Supervised 8 PhD students (main/co-supervisor) in areas spanning Bayesian causal effects, neural network survival analysis, and model conflict detection. Key grants include participation in the Data Science@UiO and Integreat projects. Labs/teams : Active member of the Center for Computational Inference in Evolutionary Life Science (CELS) and the BigInsight center.
Are Oust is a Professor of Financial Economics at the Norwegian University of Science and Technology (NTNU School of Economics) and a Professor II at the Norwegian School of Economics. He specializes in housing market dynamics, real estate economics, and tax policy. His research focuses on housing bubbles, property valuation, and the impact of regulation on real estate markets. Affiliations: NTNU School of Economics (Professor) Norwegian School of Economics (Professor II) Deputy Head of Research at NTNU School of Economics (2021–present) Deputy Director of NTNU Center for Housing and Environmental Economics (2016–present) Education: PhD in Economics, NTNU (2013) Master’s in Accounting and Auditing, Economics, and Business Administration from NHH (Norwegian School of Economics) Bachelor of Science in Economics, NTNU Research Interests: Dr. Oust’s work emphasizes automated valuation models, housing market regulation, energy labeling in real estate, and the interplay between taxation and home ownership. His research has been published in journals such as Quantitative Finance , Journal of Real Estate Research , and Energy Policy . Key Contributions: His studies on housing bubbles, rental market dynamics, and the application of AI in real estate valuation have shaped policy debates. Recent work explores the role of adverse selection in iBuyer models and the predictive power of dwelling conditions in automated valuations. Grants & Leadership: He advises on real estate policy and serves on multiple boards, including the NTNU Center for Housing and Environmental Economics, and private real estate firms like Strinda Eiendom AS. His teaching focuses on personal finance, investment strategies, and tax planning.
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)
Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.
Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.
Daumantas Bloznelis is an Associate Professor of Business Analytics at the Norwegian University of Life Sciences (Ås, Norway) and an Adjunct Associate Professor at the University of Inland Norway (Rena, Norway). He holds a PhD in Economics from the Norwegian University of Life Sciences, with visiting scholar experience at Cornell University (USA). His research focuses on financial econometrics, commodity markets, and statistical price modeling, with particular emphasis on risk management and forecasting in aquaculture sectors. Bloznelis has extensive experience in academia, including teaching courses on machine learning, econometrics, and quantitative methods across multiple universities. He has supervised numerous PhD and Master’s theses, contributing to the development of future scholars in finance and management. His work also extends to applied research, such as cross-hedging carbon risk and portfolio optimization in electric vehicle sectors. Bloznelis has received several accolades, including scholarships from the Norwegian Research Council and Vilnius University, and awards for academic excellence in Lithuania. Education: PhD in Economics/Finance (2011–2016), Norwegian University of Life Sciences MSc in Statistics/Econometrics (2009–2011), Vilnius University BSc in Statistics/Econometrics (2005–2009), Vilnius University Research Interests: Bloznelis specializes in statistical price modeling, forecasting methodologies, and risk management in financial and commodity markets. His work integrates machine learning and econometric techniques to address practical challenges in sectors like salmon farming and electric vehicles. He also explores the application of copula models and factor analysis to portfolio optimization and market dynamics. Key Awards: 3rd prize in International Econometric Team Competition (2010) PRESIDENT OF LITHUANIA AWARD for dictation contest (2007) Prime Minister of Lithuania Award for matriculation excellence (2005) Professional Contributions: Bloznelis has presented at over 30 international conferences, including NCCC commodity price analysis meetings and CEMA annual conferences. He serves on the Board of Advisors for Vilnius University’s Faculty of Mathematics and Informatics. His research outputs include influential papers on futures market biases, hedging strategies, and factor models in commodity pricing.
**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.
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.
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.
Bjarte Hannisdal is an Associate Professor in the Department of Geosciences at the University of Bergen, Norway, and affiliated with the Bjerknes Centre for Climate Research. He plays a key leadership role in iEarth, a national Centre of Excellence in geoscience education, serving as the iEarth Education Chair and Head of Focus Area 1, which aims to develop innovative frameworks for higher education in geosciences. Department of Geosciences, University of Bergen Bjerknes Centre for Climate Research iEarth – Centre of Excellence in Geoscience Education His research lies at the intersection of geobiology, paleontology, and Earth system science, with a strong focus on quantitative methods. He investigates Earth system evolution, causality in dynamical systems, and the co-evolution of life and the planet using advanced statistical and information-theoretic approaches applied to geological and fossil records. His work spans microbial ecology in deep-sea sediments, paleoclimatology through isotope analysis, and the detection of causal interactions in deep time. Hannisdal has made significant contributions through a high-impact publication record, with articles in top journals such as Science , Nature Geoscience , PNAS , and Physical Review E . His recent publications highlight trends in applying machine learning and information theory to geoscience problems, including microbial responses to oxygen, causality detection in incomplete records, and the calibration of geochemical proxies. These works reflect a strong interdisciplinary trend, integrating biology, physics, and computational methods into Earth sciences. He is actively involved in higher education, having developed and taught courses such as GEOV114 (Introduction to Geobiology) and GEOV302 (Data Analysis in Geosciences), and contributes to others like GEOV344 and BIO318. His educational research explores student-centered learning and computational skill development. He has supervised doctoral research, including Dario Blumenschein’s project on educational change. His work is supported by funding from the Research Council of Norway, Trond Mohn Foundation, and EU Horizon 2020. Hannisdal collaborates widely across institutions and disciplines, as evidenced by his co-authorship with researchers from Norway, the US, Germany, and others.
