جست‌وجوی استاد و پژوهشگر

Claudio Meggioمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Statistics
• +۴ مورد دیگر

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 Mironovمشاهده پروفایل

پژوهشگر

• Semantic Web Technologies
• Systems Biology
• Bioinformatics
• +۳ مورد دیگر

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).

• Cloud Computing
• Big Data
• Data Storage Optimization
• +۲ مورد دیگر

Akif Quddus Khan is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. His office is located at Pilestredet 35, 0166 Oslo (Room PS240) with contact details including email akif.quddus.khan@oslomet.no and office phone +47 672 38 763. His research centers on cloud computing infrastructure with specialized focus on cost modeling, storage optimization, and big data pipeline efficiency. Key contributions include developing graph-based cost optimization frameworks, creating comprehensive taxonomies for cloud storage economics, and pioneering storage-as-a-service integration methods for data-intensive workflows. His work consistently bridges theoretical computer science with industrial applications in digital transformation and sustainable IT infrastructure design. Analysis of his 2021-2024 publications reveals a methodological evolution from container-based workflow orchestration toward sophisticated graph modeling and rule-based classification systems for cloud resource management. His research demonstrates increasing emphasis on economic aspects of cloud computing, with 70% of recent work addressing cost optimization challenges through novel classification techniques and tiered storage architectures. Scientific Awards: No major scientific awards, fellowships, or medals are documented in the provided information. Advising and Grants: No details regarding student supervision, research grants, or funding sources appear in the available materials. His collaborative network includes international researchers from Norwegian and European institutions, particularly Mihhail Matskin, Dumitru Roman, and Ahmet Soylu. Laboratory Affiliations: While specific lab assignments aren't specified, his research aligns with OsloMet's Innovation, Digital Transformation and Sustainability group within the Department of Computer Science, focusing on practical implementations of cloud resource management frameworks.

• Cybersecurity
• Information Security
• Security Assurance
• +۷ مورد دیگر

Basel Cat is a Professor and Head of the Department of Information Security and Communication Technology at NTNU's Faculty of Information Technology and Electrical Engineering. His research focuses on cybersecurity, information security assurance, and innovative approaches to cyber range technologies. Key areas include vulnerability analysis, privacy protection, and AI-driven security solutions. Research Interests Cybersecurity incident triage and attack chain analysis Quantitative security evaluation models for web and cloud services Automated vulnerability injection in source code IoT security and cognitive digital twin architectures Adversarial AI applications in cybersecurity exercises Publications highlight advancements in cybersecurity education, attack-defense scenario modeling, and the integration of AI/LLMs in security frameworks. Recent work explores frameworks for evaluating cloud services and OWASP-aligned web application security standards. Collaborations involve development of hybrid IoT cyber ranges and frameworks for security assurance metrics. His research has been featured in top-tier journals like Computers & Security and Journal of Information Security and Applications .

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Anis Yazidi is an Associate Professor at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, where he leads research in the Research Group for Digital Signal Processing and Image Analysis. His academic profile demonstrates significant contributions to artificial intelligence, machine learning, and signal processing with over 50 publications between 2021-2025 in high-impact venues including IEEE Transactions, Frontiers journals, and AAAI proceedings. Professor Yazidi's research spans multiple interconnected domains with particular emphasis on Tsetlin Machines, deep learning for medical applications, and signal processing theory. His work bridges theoretical foundations with practical implementations, developing novel frameworks like DREAMS for EEG analysis with model card reporting and interpretable methods for ECG classification. His contributions to learning automata theory, particularly in convergence analysis of Tsetlin-based algorithms, represent significant theoretical advances. The research portfolio also extends to cybersecurity applications of AI for IoT protection, agricultural technology for plant disease detection, and renewable energy modeling for wind-speed statistics. Yazidi's publication record reveals a strong interdisciplinary approach, with collaborations across computer science, neuroscience, medical diagnostics, and engineering disciplines. His recent work shows increasing focus on trustworthy AI systems, ethical considerations in medical applications, and specialized neural architectures tailored for specific data modalities including EEG, ECG, and eye-tracking data. The research demonstrates both theoretical rigor in algorithm development and practical implementation for real-world problems. Professor Yazidi maintains an active collaboration network with researchers across the Department of Informatics at UiO, particularly with Pedro Lind, Hugo Lewi Hammer, and Paal Engelstad, while also engaging in international collaborations. His work contributes significantly to both the theoretical foundations of learning systems and their practical implementation in diverse application domains from healthcare to renewable energy.

Tom Luk R Michoelمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Machine Learning
• +۳ مورد دیگر

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.

• Medical Ultrasound Imaging
• Cardiac Imaging
• Blood Flow Dynamics
• +۳ مورد دیگر

Lasse Løvstakken is a Professor at the Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU). His research focuses on medical ultrasound imaging, particularly in developing advanced techniques for blood flow analysis and cardiac imaging. Key projects include 3D ultrasound imaging of blood flow in pediatric and adult hearts, leveraging artificial intelligence and deep learning for automated measurements and diagnostic improvements. His work emphasizes the integration of AI into echocardiography, such as real-time guidance systems and automated strain analysis, to enhance reproducibility and reduce variability. Collaborative efforts span clinical validation of new imaging modalities and interdisciplinary applications, such as seabed classification using deep learning. He leads projects funded by institutions like NTNU and collaborates with international teams on innovations in cardiac mechanics, valve timing, and hemodynamic modeling. Løvstakken’s research also addresses translational challenges, including telemedicine applications of handheld ultrasound devices and automated quantification tools for clinical use. His contributions bridge biomedical engineering and clinical cardiology, aiming to improve diagnostic accuracy and patient care through cutting-edge imaging technologies.

Johan Pensarمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Probabilistic Graphical Models
• Causal Learning
• +۳ مورد دیگر

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 Parviainenمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Networks
• Graphical Models
• +۷ مورد دیگر

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.

• High Performance Computing
• Parallel Computing
• Heterogeneous Computing
• +۱۲ مورد دیگر

Anne C. Elster is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She is the founder and director of the HPC-Lab, a leading research group in heterogeneous and parallel computing. She also maintains a long-standing affiliation with the Oden Institute at the University of Texas at Austin as a Senior Visiting Scientist until Summer 2025. Research Interests: Her work spans high-performance computing (HPC), GPU computing, parallel algorithms, auto-tuning, performance optimization, and machine learning applications in scientific computing. She leads research in heterogeneous architectures and has contributed significantly to compiler and runtime systems for GPUs and accelerators. Publications Trends: Her recent publications (2021–2024) focus on GPU acceleration, auto-tuning frameworks (e.g., BAT, LS-CAT), performance modeling (Roofline), machine learning integration in HPC, and applications in geophysical and scientific computing. There is a strong emphasis on empirical evaluation, benchmarking, and practical optimization techniques. Scientific Awards and Recognition: IEEE Senior Member (2000) IEEE Computer Society Distinguished Contributor Charter member, NTNU's Board (2021) Distinguished Speaker, IEEE Computer Society (2019–2022) Advising and Grants: She has advised over 100 master’s students and several PhD students. She has led major funded projects including the RCN SFI Centre for Geophysical Forecasting, EU H2020 CloudLightning and TICOH, and NFR FRINATEK on Computational Microscopy. She has served on numerous international program committees and evaluation boards. Labs and Teams: She leads the HPC-Lab at NTNU, which includes postdocs, PhDs, and master’s students, and collaborates with international researchers. The lab is a hub for innovation in GPU computing, auto-tuning, and HPC applications.

• Statistical Machine Learning
• Signal Processing
• Data Fusion
• +۱۷ مورد دیگر

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.

• Programming models
• Formal methods
• Distributed systems
• +۳ مورد دیگر

Einar Broch Johnsen is a Professor at the Department of Informatics, University of Oslo. His research focuses on programming models, formal methods, and distributed systems, with significant contributions to languages like ABS and SMOL. He has led major projects including the Sirius Center (2015-2023) and EU initiatives Envisage and HyVar. His work spans asynchronous systems, cloud computing, and digital twins, emphasizing rigorous verification techniques. Recent publications show a trend toward AI integration, robotics, and knowledge-based systems, blending formal methods with practical applications in autonomous decision-making and semantic validation. Johnsen holds editorial and committee roles for journals including Formal Aspects of Computing and conferences like FASE and iFM. He teaches courses on computability and concurrency models, advancing both theoretical and applied aspects of software engineering.

Ahmet Soyluمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Semantic Web
• Data Integration
• +۳ مورد دیگر

Ahmet Soylu is Associate Professor in Data and Knowledge Management at the University of Oslo. His research develops semantic technologies and knowledge systems for applications in healthcare, cloud computing, and industrial domains. Research Focus Creates knowledge graph systems for data integration and analysis, with applications in medical diagnostics (cancer/brain tumor classification), cloud cost optimization, and industrial monitoring systems. Develops transformer-based models for SQL recommendation and data linking. Publication Trends Recent work shows strong emphasis on healthcare AI (particularly cancer diagnostics), cloud storage optimization, knowledge graph embeddings, and industrial applications at Bosch. Increasing focus on imbalanced learning techniques for medical data. Project Leadership Developed Koala-UI for tabular data linking Semantic systems for Bosch welding quality monitoring Open energy management systems for EV charging

Evgeny Kharlamovمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Representation
• Machine Learning
• +۳ مورد دیگر

Evgeny Kharlamov is an Associate Professor at the University of Oslo and Senior Research Expert at Bosch AI. His research focuses on Neuro-Symbolic AI, Knowledge Representation (knowledge graphs, rules, ontologies), data integration, and applications in manufacturing, energy, and corporate intelligence. He has published over 190 papers in venues like IJCAI, AAAI, WWW, and holds an h-index of 41 with ~6K citations. Key awards include ranking 7th in the AI 2000 Knowledge Engineering list (2024) and top 111 German AI researchers (2023). **Education & Previous Roles**: Previously held positions at the Universities of Oxford and Bozen-Bolzano, INRIA Saclay, and Telecom Paris. His expertise spans individual research to managing teams at Bosch AI and coordinating a research center at the University of Oslo. He raised ~6.7M EUR in EU/UK research funding and led multiple large-scale EU projects. **Research Interests**: Combines theoretical advances in AI with industrial applications, emphasizing explainable and scalable solutions. Key areas include knowledge graphs, semantic web technologies, and LLM-centered systems like RAG. **Awards**: Notable recognitions include the 2020 Best Research Paper Award at ESWC, multiple ISWC awards (2015–2017), and IBM’s Best Master’s Thesis Award (2008). His PhD thesis was honored by AI*IA (2012). **Grants & Projects**: Co-secured ~6.7M EUR in funding. Led projects in semantic data integration (e.g., Siemens streaming data), digital twins, and predictive quality monitoring in manufacturing. **Teaching**: Taught courses on Digital Ecosystems (2024), Semantic Web (2023), Knowledge Graphs (2023), and Foundations of Databases (2007). **Labs/Teams**: Active in Bosch AI’s research initiatives and the University of Oslo’s data science teams, focusing on industrial AI applications.

Geir Olve Storvikمشاهده پروفایل

استاد

• Data Science
• Computational Statistics
• Bayesian Hierarchical Modelling
• +۵ مورد دیگر

Geir Olve Storvik is a Professor in Statistics and Data Science at the University of Oslo. His research focuses on computational statistics, Bayesian hierarchical modeling, Monte Carlo methods, spatio-temporal modeling, and dynamical processes. He has made significant contributions to Bayesian machine learning and its applications in biological and environmental domains. University: University of Oslo Department: Statistics and Data Science Academic Rank: Professor Storvik's research spans interdisciplinary applications of Bayesian methods in fields such as neural networks, DNA methylation, and infectious disease modeling. His recent work includes sparse Bayesian neural networks and sequential Monte Carlo approaches for epidemiology. Key projects include Bayesian methods in machine learning and BigInsight Statistical and machine learning methods for sensor data . He serves as an advisor to PhD students like Aliaksandr Hubin and Ivar Grytten, with completed theses in graph-based genomics and Bayesian variable selection. His work appears in journals like Transactions on Machine Learning Research , The Journal of Artificial Intelligence Research , and Journal of the Royal Statistical Society , covering topics from model sparsity to ecological forecasting.