Find Academic Supervisors

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.

Claudio MeggioView profile

Researcher

• Machine Learning
• Data Science
• Statistics
• +4 more

Claudio Meggio is a researcher affiliated with the University of Oslo's Faculty of Mathematics and Natural Sciences. He collaborates with the Hylleraas Centre and CompSci group, focusing on machine learning applications in chemistry and explainability of models. His PhD research involves boosting algorithms on graph-structured data. Education: BSc in Mathematics from the University of Trento, MSc in Mathematics (Probability/Statistics) from University of Torino, with study abroad at University of Waterloo. His master's thesis on the COVID-19 outbreak was supervised by Prof. Andrea Pugliese. Research Interests: Supervised/unsupervised learning, boosting algorithms, graph-structured data analysis, and metal compound studies. He is part of the SALSA team at University of Bolzano and works under supervisors Riccardo De Bin, Johan Pensar, and David Balcells. Publications include work on Path Boosting for chemistry applications. Active in interdisciplinary projects at the intersection of computational science and chemistry.

Vladimir MironovView profile

Researcher

• Semantic Web Technologies
• Systems Biology
• Bioinformatics
• +3 more

Vladimir Mironov is a Senior Scientist at the Norwegian University of Science and Technology (NTNU), specializing in Semantic Web technologies and their application in systems biology and bioinformatics. His work focuses on knowledge graph integration, biological data modeling, and semantic network construction. Email: vladimir.mironov@ntnu.no Email: vladimir.n.moronov@gmail.com His research interests include: Development of Semantic Web tools for gene regulatory network analysis Ontology engineering for biological knowledge management Integration of chromosome location data with functional genomics Triple store evaluation for biological datasets Recent publications highlight his contributions to: Knowledge graph frameworks for gene regulation Network analysis tools in bioinformatics Ammonia-tolerant microbial communities in biotechnology Orthology prediction algorithms Semantic data modeling for biological datasets He has actively contributed to projects like DrugLogics and Crossover Research since 2008, developing semantic tools for biomedical research, including the Cytoscape BioGateway App and Cell Cycle Ontology (CCO).

Tom Luk R MichoelView profile

Professor

• Bioinformatics
• Computational Biology
• Machine Learning
• +3 more

Tom Luk R Michoel is a Professor at the Computational Biology Unit within the Department of Informatics at the University of Bergen. His research focuses on bioinformatics, computational biology, and machine learning applied to understanding gene regulation and causal relationships in biological systems. He teaches in both the Bachelor and Master programs in Informatics, including courses like BINF301 and MNF130. Research Interests: Michoel’s work explores how genetic variation influences gene expression and disease mechanisms. He develops machine learning algorithms to infer causal gene regulatory networks from large-scale genomic data, emphasizing causal inference over mere correlations. His recent projects include analyzing plasma protein networks linked to cardiovascular disease and applying Bayesian networks to understand gene-disease associations. Publications: His most recent work (2025) focuses on causal protein networks in myocardial infarction risk and network-driven frameworks for coronary artery disease studies. He also contributes to methodological advancements like integrating graph neural networks with metabolic models. Current Activities: Michoel leads the development of tools like Findr.jl for network inference and teaches short courses on causal inference in drug discovery. His lab collaborates on projects involving single-cell analysis, multi-tissue genomics, and systems pharmacology.

Johan PensarView profile

Associate Professor

• Statistical Machine Learning
• Probabilistic Graphical Models
• Causal Learning
• +3 more

Johan Pensar is an Associate Professor of Statistics and Data Science at the University of Oslo's Department of Mathematics. He holds a PhD from Åbo Akademi University (2016) and was a postdoc at the University of Helsinki (2016–2020). His research focuses on statistical machine learning, probabilistic graphical models, causal inference, and applications in genomics. He has supervised multiple PhD students and co-supervised others in interdisciplinary projects, including causal modeling in healthcare and machine learning for microbiology. Education: PhD in Statistics, Åbo Akademi University, 2016 Postdoctoral Researcher, University of Helsinki, 2016–2020 Research Interests: Pensar's work integrates statistical theory with practical applications. Key areas include developing methods for causal discovery, probabilistic graphical models (e.g., Bayesian networks), and their use in genomics and healthcare. He emphasizes interpretable machine learning and robust statistical frameworks for complex data. Publications: Recent work spans causal inference, microbial genome analysis, and housing market prediction. His methods address challenges like confounding bias, generalization in ML, and uncertainty quantification in valuation models. Awards: Finnish Statistical Society Doctoral Thesis Award (2013–2016) Teaching & Advising: Pensar teaches advanced courses in statistical learning and probabilistic graphical models. He advises PhD students on causal modeling, ML in healthcare, and data science applications. He collaborates with industry partners like Integreat and Eiendomsverdi AS. Lab/Teams: He is affiliated with the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) and leads research on Bayesian methods in ML.

Pekka ParviainenView profile

Associate Professor

• Machine Learning
• Bayesian Networks
• Graphical Models
• +7 more

Pekka Parviainen is an Associate Professor in the Department of Informatics at the University of Bergen, within the Faculty of Mathematics and Natural Sciences. His research spans machine learning, probabilistic modeling, and AI theory, with a focus on Bayesian and Markov networks, adversarial robustness, fairness, and energy forecasting. He is affiliated with the Center for Data Science (CEDAS), an active research center at the university. His research interests include: Structure learning in graphical models Probabilistic forecasting using graph neural networks Adversarial robustness and defense mechanisms Fairness in clustering and machine learning Optimization and approximation in learning algorithms Applications in renewable energy and quantum sensing His recent publications (2020–2025) reflect a strong theoretical grounding combined with real-world applications, particularly in energy systems and AI safety. The works trend toward scalable and interpretable models, with increasing focus on fairness and robustness. Key themes include Bayesian network learning, metric learning, and causal graph modeling. Scientific contributions include: Development of novel adversaries (e.g., Voronoi-epsilon) for measuring robustness Scalable algorithms for learning large DAGs and Bayesian networks Integration of continuous optimization with combinatorial heuristics Applications in electricity demand forecasting and gas sensing Parviainen advises PhD students, including Hyeongji Kim (2023 thesis on distance in machine learning), and collaborates extensively with researchers in Norway and internationally. He has received computational support via Sigma2 (NN9884K) and is part of the CEDAS project, which fosters interdisciplinary data science research. While no specific grants are detailed, his involvement in funded projects and high-impact publications indicates active grant engagement. He is associated with the Center for Data Science (CEDAS), where he contributes to advancing data-driven methodologies across domains. The team emphasizes scalable, robust, and fair AI systems, aligning with national and international research priorities in trustworthy machine learning.

• Statistical Machine Learning
• Signal Processing
• Data Fusion
• +17 more

Salvo Rossi is a Full Professor of Statistical Machine Learning for Signal Processing at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He holds leadership roles as Deputy Director for Research and Deputy Manager of the Centre for Green Shift in the Built Environment. Additionally, he serves as a part-time Senior Research Scientist at SINTEF Energy Research. His work bridges academia and industry, with significant contributions to signal processing, machine learning, and IoT systems. Research Interests: Statistical Machine Learning Data Fusion in Wireless Sensor Networks Digital Twins and Industrial IoT Anomaly Detection and Fault Diagnosis Federated and Distributed Learning Graph Signal Processing His recent publications reflect a strong trend toward intelligent, distributed systems for industrial monitoring, particularly in energy and safety-critical environments. The integration of machine learning with signal processing for decision fusion in sensor networks is a central theme. Scientific Awards and Recognition: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Editorial and Professional Service: Senior Area Editor, IEEE Transactions on Signal and Information Processing over Networks (since 2025) Topical Editor, IEEE Sensors Journal (since 2022) Area Editor, IEEE Open Journal of the Communications Society (2019–2023) Executive Editor, IEEE Communications Letters (2019–2021) General Chair, IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Trondheim, 2022 He has advised numerous PhD and master’s students (not explicitly listed), led major research projects in IoT and green energy, and contributed to national and international research initiatives. His lab, SPIN (Signal Processing Group), focuses on intelligent signal processing for real-world applications in smart environments and Industry 4.0.

• Cloud computing
• Cybersecurity
• Software Engineering
• +7 more

Paal Engelstad is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, Department of Computer Science. His research focuses on Cloud computing , Cybersecurity , and Software Engineering , with significant contributions to Graph Neural Networks (GNNs) and their applications in network analysis, renewable energy forecasting, and telecom systems. Research interests span Applied Artificial Intelligence and Autonomous Systems and Networks . Recent publications address spatio-temporal wind forecasting , GNN propagation schemes , and 5G network security . He contributes to advancements in renewable energy integration and telecom protocol analysis .

Anamaria CostacheView profile

Associate Professor

• Fully Homomorphic Encryption
• Privacy-Preserving Machine Learning
• Lattice-Based Cryptography
• +3 more

Anamaria Costache is an Associate Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Information Security and Communication Technology. Previously, she held roles including a postdoc at Royal Holloway, University of London (RHUL), a Research Scientist at Intel AI Research, and a part-time position at Intel. She earned her PhD from the University of Bristol and undergraduate/master's degrees from the University of Warwick, focusing on number theory and algebraic structures like Brauer Groups. Her research interests center on fully homomorphic encryption (FHE), privacy-preserving machine learning, lattice-based cryptography, and post-quantum security protocols. Her work is supported by Intel, addressing challenges in secure computation on encrypted data. She has authored numerous publications in top venues like CRYPTO, PKC, and IEEE conferences, focusing on FHE optimizations, biometric systems, and cryptographic security analyses. Teaching includes courses on applied cryptography, network security, and quantum-safe encryption at NTNU. She co-chairs NIKT 2019 and the WAHC workshops (2022–2024), and serves on editorial boards for journals like IACR Communications in Cryptology. She actively participates in program committees for conferences such as MathCrypt and FHE.org. Professional service includes the Lattigo Advisory Committee and contributions to the Bristol Crypto Blog. Her current projects emphasize verifiable computation over encrypted data and resilient authentication mechanisms in biometric systems.

Luca CibinelView profile

Research Fellow

• Statistics and Data Science
• Machine Learning
• Graph-Structured Data
• +3 more

Luca Cibinel is a Doctoral Research Fellow at the University of Oslo, affiliated with the Department of Mathematics and the Statistics and Data Science group. His PhD project, supervised by Basil Ell, Johan Pensar, and Riccardo De Bin, focuses on developing statistical learning techniques for assessing and generating transition metal complexes based on observational data and theoretical knowledge. Prior to this, he earned a master's degree in mathematics from the University of Trento (2023), with a thesis on penalized likelihood inference for Gaussian covariance graph models. He also worked as an early-stage researcher at the University of Padua, investigating probabilistic graphical models for count data in high-dimensional scenarios. His research interests include statistical relational learning, probabilistic logic, and machine learning applications for graph-structured data. Luca is based at the Niels Henrik Abels hus in Oslo. Education: Master's in Mathematics, University of Trento (2023) Early-stage Researcher role at University of Padua (2024) Research Focus: Luca's work bridges statistical methodology and computational modeling, particularly in contexts requiring integration of theoretical and empirical data. His current project aims to create frameworks for evaluating and generating transition metal complexes, with potential applications in materials science and chemistry. Professional Affiliations: Faculty of Mathematics and Natural Sciences (student status), Statistics and Data Science research group. Links: LinkedIn Profile

• Machine Learning
• Graph Neural Networks
• Vertex Clustering
• +3 more

Jonas Berg Hansen is a Research Fellow at the Department of Mathematics and Statistics , UiT The Arctic University of Norway. His work focuses on advanced machine learning techniques for graph-structured data and energy system modeling. Primary affiliation: UiT The Arctic University of Norway Department: Mathematics and Statistics Research Interests: Hansen develops novel graph neural network architectures combining principles from spectral clustering and optimization theory. His recent work emphasizes: Graph total variation for improved vertex clustering Decentralized energy systems balancing Integration of photovoltaics with electric vehicle networks Publication Trends: His 2021-2023 publications demonstrate cross-disciplinary research at the intersection of: Machine learning and graph theory Power systems optimization Sustainable energy infrastructure

• Signal Processing
• Machine Learning
• Graph Theory
• +5 more

Rohan Thekkemarickal Money is a Postdoctoral Fellow at Simula, affiliated with the Department of Signal and Information Processing for Intelligent Systems within the Simula Metropolitan Centre. His research focuses on advanced signal processing, graph theory, and machine learning applications to dynamic networks and topological data analysis. Recent research trends include developing simplicial vector autoregressive models for network dynamics, online topology identification for higher-order systems, and privacy-aware learning frameworks . His work addresses challenges in time-series forecasting, edge flow backcasting, and capacity estimation for lithium-ion batteries using graph-based methodologies. Rohan collaborates with researchers like Dr. B. Beferull-Lozano and Dr. E. Isufi, contributing to interdisciplinary projects at the intersection of signal processing , network science , and topological data analysis . His publications demonstrate expertise in handling nonlinear systems with sparse data and designing scalable algorithms for real-time applications.

Nello BlaserView profile

Professor

• Topological Data Analysis
• Machine Learning
• Statistics
• +3 more

Nello Blaser is a Professor at the Department of Informatics at the University of Bergen. His research focuses on topological data analysis (TDA), particularly in developing methods for encoding data into topological structures, validating unordered persistent homology, and applying these techniques in biomedicine and geophysics. He has contributed to theoretical advancements in TDA, including works on relative persistent homology and divisive cover algorithms. His interdisciplinary work bridges computer science, mathematics, and environmental science, addressing challenges in CO2 leak detection, marine pollution, and medical diagnostics. Blaser's research portfolio includes collaborations on machine learning models for flow reconstruction, receptor occupancy studies in neurology, and topological methods for analyzing gene interactions. He has been funded by the Research Council of Norway and other institutions. Notable publications include contributions to Discrete & Computational Geometry , Physics of Fluids , and Neurology: Neuroimmunology and Neuroinflammation . His work emphasizes practical applications of topological methods, such as improving environmental monitoring systems and advancing diagnostic tools for diseases like rheumatoid arthritis and multiple sclerosis.

Jeremy BarnesView profile

Assistant Professor

• Cross-lingual Methods
• Transfer Learning
• Sentiment Analysis
• +5 more

Jeremy Barnes is an Assistant Professor in the IXA Group at the University of the Basque Country (UPV/EHU), part of the HiTZ Centre. He holds a PhD in Computational Linguistics and has extensive postdoctoral experience at the University of Oslo. His research focuses on cross-lingual methods, sentiment/emotion analysis, weak supervision, and under-resourced language resources. He has contributed to projects like NorDial and NorLM, developing tools like skweak. Barnes teaches courses in computational syntax, text mining, and NLP. Education: PhD (Pompeu Fabra University), M.A. and B.A. in Linguistics Key Projects: LUMINOUS (multimodal LLMs), SANT (Norwegian sentiment analysis) His work spans structured sentiment analysis, cross-lingual transfer learning, and dialectal feature identification. He has received awards for his book Sonic Ethnography (2021) and photography (2020). Barnes advises numerous Master's theses and collaborates internationally on NLP challenges.

• Digital Forensics
• Cybercrime Analysis
• Social Network Analysis
• +3 more

Jan William Johnsen is a Researcher at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. He specializes in digital forensics, cybercrime analysis, and social network analysis, with a focus on applying big data techniques to criminal investigations. His research interests include identifying cybercriminals through text and network analysis, preprocessing natural language for open-source intelligence, and investigating technological factors affecting child sexual abuse material distribution. He has contributed to interdisciplinary studies on criminal network dynamics and has co-authored reports on digital forensics for major institutions. Recent publications highlight trends in criminal network analysis, automated forensic frameworks, and the application of big data in cybercrime investigations. His work bridges technical cybersecurity methods with criminal justice applications. Johnsen teaches courses such as IMT4114 (Introduction to Digital Forensics) and IMT4130 (Investigation of Cybercrime), and has participated in international workshops like the IEEE Big Data conference. His contributions include developing tools like Reccoon for network footprinting and analyzing organized crime groups' communication patterns.