Find Academic Supervisors

• Applied Artificial Intelligence
• Digital Twins
• Hybrid Modeling
• +4 more

Idelfonso Nogueira is an Associate Professor at the Department of Chemical Process Technology, Norwegian University of Science and Technology (NTNU). His research focuses on integrating artificial intelligence, advanced control, and system optimization to address challenges in Industry 4.0/5.0. Key research areas include AI-driven product/process development, interpretable machine learning for process systems, multi-scale system integration, and adaptive digital twins. He collaborates extensively with global universities, institutes, and industries to advance sustainable industrial solutions. His work emphasizes digitalization, hybrid modeling, and optimization frameworks, as seen in publications on pressure swing adsorption, fragrance engineering, and CO2 capture systems. He also promotes innovative teaching methods, including augmented reality for chemical engineering education. Recent publications highlight advancements in digital twin frameworks, machine learning for crystallization processes, and optimal control strategies. His research bridges theoretical models with practical applications, ensuring robustness and reliability in industrial contexts.

Pinar HeggernesView profile

Professor

• Algorithms
• Graph Theory
• Combinatorics
• +3 more

Pinar Heggernes is a Professor at the Department of Informatics at the University of Bergen , specializing in algorithms and graph theory . Her research focuses on solving NP-hard graph problems through polynomial-time and fixed parameter tractable algorithms , particularly on chordal , split , and outerplanar graphs . She served as the elected deputy rector of the University of Bergen (2021–2025) with responsibility for education and digital knowledge, and previously as head of the Department of Informatics until 2021. Her career spans leadership in externally funded projects, editorial roles in international journals, and active participation in national research policy, including co-founding the Norwegian Artificial Intelligence Research Consortium (NORA). Education: PhD in Informatics from the University of Bergen. Research Interests: Algorithms for graph classes, computational complexity, parameterized algorithms, and combinatorial optimization. Collaborations: Extensive partnerships with academia, businesses, and public administration, including advisory roles in the Bergen Chamber of Commerce and the Norwegian Cognitive Center . Grants: Project leader for four Research Council of Norway FRIPRO grants (CLASSIS, SCOPE, MIST, and earlier projects) and participant in EU, Trond Mohn Foundation, and National Security Authority grants. Students: Supervised Paloma T. Lima (PhD, 2019) and co-supervised numerous advisees through collaborative projects. Labs/Teams: Coordinated interdisciplinary ICT research and education initiatives at UiB, with international collaborations in the USA, France, and Turkey.

Morten BrunView profile

Associate Professor

• Topological Data Analysis
• Computational Topology
• Persistent Homology
• +3 more

Morten Brun is an Associate Professor at the Department of Mathematics, University of Bergen. His research spans computational topology, persistent homology, and applications in biology and data science. Email: morten.brun@uib.no Research Interests: He specializes in topological data analysis, focusing on sparse nerves, relative persistent homology, and computational geometry. His work applies topological methods to biological problems, including drug resistance modeling in tuberculosis and immune profiling in multiple sclerosis. Recent Publications: His 2025 work includes hypercubic modeling of tuberculosis drug resistance and high-dimensional immune profiling post-stem cell transplantation. Earlier articles explore computational topology techniques (2017-2024) and interdisciplinary applications in toxicology and systems biology.

Martin Wulf GerdesView profile

Associate Professor

• eHealth
• Artificial Intelligence
• Machine Learning
• +9 more

Martin Wulf Gerdes is an Associate Professor at the Department of Information and Communication Technology, University of Agder, Norway. With a Ph.D. in eHealth and over two decades of experience in telecommunications and healthcare technology research, he focuses on developing user-friendly, interoperable eHealth solutions using ICT and IoT. His career spans roles at Ericsson Eurolab and academic leadership in Norway. Education: M.Sc. equivalent in General Electrical Engineering (1998) from RWTH Aachen, emphasizing ICT. Ph.D.: Information and Communication Technologies with eHealth specialization (2019) from University of Agder. Research Interests: Gerdes explores AI-driven decision support systems, distributed eHealth infrastructure, telemedicine, and assistive technologies for chronic disease management. His work bridges IoT, data privacy, and user-centered design in healthcare robotics. Teaching: Since 2014, he has taught at bachelor's and master's levels, currently leading courses in Communication & Cooperation, Technology Understanding, and seminar projects in eHealth. Scientific Publications: His 15 most recent articles (2020-2024) span AI in dental radiology, blood glucose prediction, digital twins, and ethical considerations in healthcare algorithms. Topics include IoT integration, data privacy, and pandemic analytics.

Bjørnar TessemView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Journalism
• +3 more

Bjørnar Tessem is a Professor at the Department of Information and Media Studies, University of Bergen. His research focuses on artificial intelligence (AI) and machine learning applications in journalism and sustainable resource management, particularly fisheries. He leads projects like MediaFutures and collaborates with researchers on anomaly detection in fisheries data and ethical AI in automated journalism systems. His work bridges computational methods with societal challenges, emphasizing fairness, transparency, and sustainability. Research interests include AI-driven journalism frameworks, knowledge graphs for news angles, sustainable fisheries monitoring via machine learning, and ethical implications of AI in media. Key projects involve developing tools for detecting violations in fishing activities and enhancing news production through automated systems. He also contributes to public understanding through popular science articles on technology and AI. Recent articles highlight advancements in collective anomaly detection for fisheries surveillance, future technologies in journalism, and fairness in automated data journalism systems. Tessem’s research is supported by grants from the Research Council of Norway and has implications for both academic and industry applications in media and environmental sectors.

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

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

Kirill SimonovView profile

Associate Professor

• Parameterized Complexity
• Algorithm Design
• Graph Theory
• +3 more

Kirill Simonov is an Associate Professor in the Department of Informatics at the University of Bergen. His research focuses on parameterized complexity, algorithm design, and graph theory, with particular emphasis on clustering algorithms, graph modification problems, and algorithmic graph theory. He has contributed to foundational work in fair clustering, approximate algorithms for graph cycles, and structural analysis of sparse graphs. His notable contributions include studies on coresets for fair clustering, algorithmic extensions of Dirac's theorem, and techniques for building large k-cores from sparse graphs. His work is supported by the Research Council of Norway (Project 314528). He frequently collaborates with leading researchers like Fedor Fomin and Petr Golovach on topics such as parameterized algorithms and combinatorial optimization. Simonov's publications span venues like the Journal of Computer and System Sciences and Leibniz International Proceedings in Informatics. His research bridges theoretical computer science with practical algorithmic solutions for graph problems and data clustering challenges.

• 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.

• 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 .

Irina GribkovskaiaView profile

Professor

• Offshore Logistics
• Vehicle Routing and Scheduling
• Mathematical Modelling and Optimization
• +4 more

Irina Gribkovskaia is a Professor at the Faculty of Logistics, Molde University College (Norway). Her research focuses on offshore energy logistics, vehicle routing and scheduling, mathematical optimization for logistics planning, and decision support systems under uncertainty. She leads the Energy Logistics Research Group (EneLog) and has collaborated on projects such as the Norwegian-Russian Arctic Logistics initiative. Her work addresses challenges in offshore oil/gas and renewable energy sectors, including supply vessel planning, helicopter transportation safety, and emissions reduction. Notable contributions include robust scheduling methods, fleet sizing strategies, and simulation-based optimization tools. Key projects include analyzing transit shipping via the Northeast Passage and tactical helicopter planning for offshore personnel. She has authored over 50 peer-reviewed articles in journals like Transportation Research and Omega , focusing on logistics optimization under uncertainty. Irina is a core member of multidisciplinary programs, including a Norwegian-Belarusian MSc in Logistics Analytics. Her research integrates operations research with real-world maritime and energy logistics systems.

• Parameterized Complexity
• Algorithms
• Graph Theory
• +3 more

Saket Saurabh is a Professor at the Department of Informatics, University of Bergen. His research focuses on parameterized complexity, algorithms, graph theory, and combinatorial optimization. He has contributed extensively to theoretical computer science, with a strong emphasis on algorithm design and analysis for NP-hard problems. His work includes studies on graph algorithms, approximation schemes, and fairness in computational problems. Recent publications address topics such as minimum membership dominating sets, hybrid clustering, and fair hitting set problems. Saurabh has collaborated widely, with co-authors like Fedor Fomin, Petr Golovach, and others. Key research interests include parameterized algorithms for graph problems, exponential-time approximation methods, and structural graph theory. His contributions have advanced the understanding of computational complexity and practical algorithmic solutions for challenging problems.

Petr GolovachView profile

Research Professor

• Discrete Mathematics
• Theoretical Computer Science
• Graph Theory
• +6 more

Petr Golovach is a Research Professor at the Department of Informatics, University of Bergen. His research focuses on Discrete Mathematics and Theoretical Computer Science, particularly graph theory, algorithms, parameterized complexity, and clustering. He has held academic positions at Syktyvkar State University (1991–2007), Durham University (2009–2011), and currently teaches advanced courses like Advanced Algorithms Techniques and Enumeration Algorithms at the University of Bergen. His research interests include graph algorithms, parameterized complexity, matroid theory, and algorithmic enumeration. He has organized notable events like the Dagstuhl Seminar 2018 and serves on program committees for STACS, IPEC, and SWAT. Notable contributions include work on hybrid clustering algorithms, graph cuts under matroid constraints, and parameterized tractability of path and cycle problems. He has supervised PhD students like Nidhi Purohit and master’s students including Øyving Stette Haarberg and Andreas Steinvik. His publications span over 200 peer-reviewed papers in top venues such as Journal of the ACM and conferences like SODA and ICALP. His research bridges extremal combinatorics with algorithm design, emphasizing practical and theoretical advancements in graph-based problems.

Fredrik ManneView profile

Professor

• Parallel Computing
• Distributed Computing
• Graph Algorithms
• +3 more

Fredrik Manne is a Professor at the Department of Informatics, University of Bergen, Norway. His work focuses on parallel and distributed computing, particularly in combinatorial scientific computing and self-stabilizing algorithms. He has contributed extensively to graph algorithms, including matching, coloring, and clustering, with applications in high-performance computing, numerical optimization, and wireless networks. His research involves designing algorithms for parallel architectures, including multi-core and GPU-based systems. He has co-authored numerous publications on topics such as spanning forests, vertex cover heuristics, and b-matching. His work often integrates theoretical and applied approaches, addressing challenges in sparse matrix computations and wireless mesh network communication. The recent articles highlight his exploration of graph neural networks for parallel coloring, efficient multithreaded matching algorithms, and GPU-accelerated clustering methods. These studies emphasize scalability, optimization, and real-world applications in computational science.

• Control Systems
• Robotics
• Power Systems
• +2 more

Professor Damiano Varagnolo is affiliated with the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU), where he conducts research spanning control systems, robotics, power systems, and data-driven modeling. His work bridges theoretical advances with practical applications in underwater vehicles, medical rehabilitation, and energy infrastructure. His research focuses on: Development of formation control algorithms for autonomous underwater vehicles (AUVs) and snake robots Transient stability enhancement for grid-forming converters in renewable energy systems Data-driven modeling of physiological signals for wheelchair propulsion and industrial processes Distributed optimization for underwater communication networks Recent publications (2024-2025) demonstrate strong emphasis on: Underactuated vehicle control using hand position concepts Energy expenditure estimation through physiological signal processing Consensus protocols for JANUS-based underwater networks Educational innovation through control-themed outreach initiatives like the Advent Calendar project He actively collaborates with NTNU colleagues across engineering disciplines and contributes to curriculum development through graph-theoretic approaches for educational coherence.