جست‌وجوی استاد و پژوهشگر

Dieu Tien Buiمشاهده پروفایل

استاد

• Geospatial Artificial Intelligence
• Machine Learning
• GIS and Geomatics
• +۶ مورد دیگر

Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.

• Algorithms
• Combinatorics
• Parameterized Algorithms
• +۱۲ مورد دیگر

Fedor V. Fomin is a Professor in the Department of Informatics at the University of Bergen, Norway, where he leads the Algorithms Research Group. His work is central to theoretical computer science and combinatorics, with significant contributions to algorithm design and analysis. His primary research interests include: Parameterized Algorithms and Kernelization Exact (Exponential Time) Algorithms Graph Algorithms and Graph Minors Approximation Algorithms and Treewidth Matroid Algorithms and Metric Embedding Algorithmic Fairness and Pursuit-Evasion Problems The selected publications reflect a strong trend in foundational algorithmic techniques, particularly in parameterized complexity, kernelization, and exact algorithms. His work often bridges theoretical depth with practical applicability, especially in graph-theoretic problems and preprocessing methods. His scientific recognition includes: EATCS Nerode Prize 2015 EATCS Nerode Prize 2017 Fedor V. Fomin has made substantial contributions through major textbooks such as Parameterized Algorithms (2015) and Kernelization (2019), which have become essential resources in the field. He has collaborated with leading researchers including Daniel Lokshtanov, Saket Saurabh, and Dieter Kratsch. While specific advising roles are not listed, his publications and books suggest extensive mentorship and collaboration. He is actively involved in organizing academic events like FPT Fest and GRASTA, indicating leadership in the research community. He is affiliated with the Algorithms Research Group at the University of Bergen, contributing to a vibrant research environment focused on discrete algorithms and complexity.

• Computer Architecture
• Microarchitecture
• Memory Systems
• +۴ مورد دیگر

Rakesh Kumar is an Associate Professor in the Department of Computer Science (IDI) at the Norwegian University of Science and Technology (NTNU) , affiliated with the Computer Architecture Lab (CAL) within the Faculty of Information Technology and Electrical Engineering . Prior to joining NTNU, he held postdoctoral and research associate positions at Uppsala University and the University of Edinburgh, and interned at Intel Barcelona Research Center. Research Interests include improving large-scale datacenter efficiency through microarchitecture and memory system optimizations, hardware/software co-designed processors, dynamic code translation, vectorization, and serverless function execution. His work explores ready-aware instruction scheduling, branch prediction organization, and address translation mechanisms. Scientific Contributions span publications at top-tier conferences like MICRO (2024, 2023, 2018, 2016) HPCA (2020, 2019, 2023, 2022) ASPLOS (2018) DATE (2019, 2021) Journal articles appear in ACM Transactions on Computer Systems and IEEE Computer Architecture Letters . Awards include Intel Spontaneous Level II/Excellence Award (2014) Best Presentation Award at HiPC-SS08 (2008) Best Paper Award at National Conference on High Computing Technologies (2008) Distinguished Artifact Award at MICRO 2023 PhD Supervision involves advising students like Roman Kaspar Brunner, Elias Orrem, and Truls Asheim on topics spanning microarchitecture, vector units, and runahead execution policies.

• Control Systems
• Petroleum Engineering
• Machine Learning
• +۳ مورد دیگر

Alexey Pavlov is a Professor of Petroleum Cybernetics at the Department of Geosciences and Petroleum, Norwegian University of Science and Technology (NTNU). He holds an MSc in Applied Mathematics from St. Petersburg State University, a PhD in Mechanical Engineering from Eindhoven University of Technology, and has industrial R&D experience from Statoil and Ford Motor Co. Education: MSc (Applied Mathematics, St. Petersburg State University), PhD (Mechanical Engineering, Eindhoven University) His research focuses on control systems for petroleum engineering applications, including nonlinear control theory, iterative learning control, and data-driven optimization methods. Publications reveal a strong emphasis on real-time drilling optimization, well integrity monitoring, and synchronization in networked systems. Recent work trends include machine learning integration for oil well monitoring, moment matching in model reduction, and extremum seeking control for multi-agent systems. Collaborations span institutions like Ford Motor Co., Statoil, and Eindhoven University of Technology. Current affiliations include the Department of Geosciences and Petroleum at NTNU. No scientific awards or advisee information is explicitly mentioned in the provided texts.

Sergej Stoppelمشاهده پروفایل

پژوهشگر

• Visual Data Science
• Data Visualization
• Human-Computer Interaction
• +۳ مورد دیگر

Sergej Stoppel is currently a researcher (PostDoc) in the visualization group at the University of Bergen. His academic background includes a Mathematics master's degree (2014) and a PhD in 2018, both from the University of Bergen. His research focuses on visual data science, with an emphasis on interactive visualization techniques, human-computer interaction, and physical visualization systems. He has authored/co-authored numerous papers on topics such as hexagonal map enhancements, spatio-temporal data interaction, and low-cost physical art generation. Education: M.Sc. Mathematics (2014), University of Bergen PhD in Visualization (2018), University of Bergen Research Interests: His work spans data visualization, scientific visualization, and interactive techniques. Notable contributions include Vol²velle (printable interactive volume visualization widgets), Firefly (virtual illumination drones), and LinesLab (low-cost art generation systems). He explores methods to bridge the gap between virtual and physical visual representations, emphasizing user-centric design. Publications: His recent work includes advancements in hexagonal map visualization (2022), spatio-temporal selection techniques (2020), and illumination automation (2019). These contributions highlight his focus on enhancing data interpretation through novel interaction paradigms. Labs/Teams: Active member of the VisGroup at the University of Bergen, collaborating on projects like MetaVis and VIDI. His work is supported by grants involving visualization and medical imaging applications.

Fedor Fominمشاهده پروفایل

استاد

• Theoretical Computer Science
• Graph Algorithms
• Parameterized Complexity
• +۳ مورد دیگر

Fedor Fomin is a Professor in the Department of Informatics at the University of Bergen. His research focuses on Theoretical Computer Science, including Graph Algorithms, Parameterized Complexity, Combinatorics, and Combinatorial Games. He is affiliated with the Norwegian Academy of Science and Letters, the Norwegian Academy of Technological Sciences, and the Academia Europaea, and holds fellowships from ACM and EATCS. His work has been recognized with the EATCS Nerode Prize in 2015 and 2017. Dr. Fomin has authored influential books such as Kernelization: Theory of Parameterized Preprocessing and Parameterized Algorithms , which are foundational in the field of algorithm design. His recent publications explore cutting-edge topics in parameterized complexity, graph theory, and distributed computing, with contributions to approximation algorithms, kernelization, and combinatorial optimization. His awards and honors reflect his significant impact on theoretical computer science. Fomin has been awarded an ERC Advanced Grant and has mentored numerous researchers, contributing to the advancement of algorithmic techniques and their applications.

• Geometry
• Topology
• Quantum Field Theory
• +۲ مورد دیگر

Ryan Grady is an Associate Professor in the Department of Mathematical Sciences at Montana State University. His research and teaching focus span advanced mathematical disciplines. Education: Ph.D. and M.S. (2012, 2009) from University of Notre Dame B.S. (2007) from Colorado School of Mines Ryan's research lies at the intersection of geometry, topology , and quantum field theory (QFT) , with a focus on rigorous mathematical frameworks. He explores connections between QFT, derived geometry , and higher Lie theory , aiming to bridge physical intuition with formal mathematical structures. His recent publications highlight trends in topological data analysis , homotopy theory , and quantization . Key areas include factorization algebras , K-theory , and non-linear sigma models , reflecting a synthesis of abstract mathematics and physical applications. Ryan has advised multiple graduate students, including Eric Berry, Adam Howard, Garrett Oren, and Bryce Morrow, whose work spans cohomology of Grassmanians , surface immersions , algebraic structures , and infinite-dimensional linear algebra .

Kirill Simonovمشاهده پروفایل

دانشیار

• Parameterized Complexity
• Algorithm Design
• Graph Theory
• +۳ مورد دیگر

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.

• Telecommunications
• Network Resilience
• Optical Fiber Sensing
• +۵ مورد دیگر

Haakon Bryhni is a Research Professor at Simula Research Laboratory's Center for Resilient Networks and Applications, focusing on telecommunications infrastructure and network resilience. His work spans optical fiber sensing, Cloud-RAN architecture, 5G networks, and network security. Expertise in telecommunications and network infrastructure Contributions to optical fiber sensing and AI-driven network outage classification Active in public outreach and policy analysis for ICT resilience Recent research explores Sagnac loop sensing systems , Cloud-RAN integration , and cybersecurity preparedness in Norway. Collaborations include international conferences and publications in Optics & Laser Technology , Sensors , and IEEE journals. Contact: haakonbryhni@simula.no

Petr Golovachمشاهده پروفایل

استاد پژوهشی

• Discrete Mathematics
• Theoretical Computer Science
• Graph Theory
• +۶ مورد دیگر

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.

Guillaume Aulanierمشاهده پروفایل

استاد

• Solar Physics
• Space Weather
• MHD Modeling
• +۷ مورد دیگر

Guillaume Aulanier is a Professor II at the Rosseland Centre for Solar Physics (RoCS) at the University of Oslo, Norway, and simultaneously holds a position as Astronomer (equivalent to Professor) at Laboratoire de Physique des Plasmas (LPP) in Paris, France. His academic career spans over two decades, beginning as an Assistant Astronomer at the Paris Observatory in 2001. He currently serves as Chair of the national terrestrial program (PNST) of CNRS/INSU and Deputy-director of the astrophysics graduate program (AStroParis) at Université Paris Sciences & Lettres. Professor Aulanier specializes in solar physics, with research focusing on the solar origins of space weather. His expertise includes developing 3D MHD models coupled with multi-wavelength observations for studying eruptive flares, prominences, and magnetic reconnection in complex topologies. He is the original developer of the Observationally-driven High-order Magneto-hydrodynamics code (OHM) and uses the TOPOlogy and fieldline TRacing code (TOPOTR) for analysis. His work consistently bridges theoretical modeling with observational data from cutting-edge solar missions. His recent publications show a strong trend toward high-resolution observational analysis of solar phenomena, particularly focusing on coronal mass ejections, flare ribbons, and magnetic reconnection processes. His research leverages data from multiple spacecraft including NASA's Solar Dynamics Observatory (SDO) and ESA's Solar Orbiter, often combining these observations with sophisticated numerical simulations. The work demonstrates increasing attention to fine-scale structures in solar eruptions and their implications for space weather forecasting. Academic palms (France, rank knight, 2017) National order of merit (France, rank knight, 2015) Professor Aulanier actively contributes to major international solar physics initiatives as a member of science teams for NASA's Solar Dynamics Observatory (SDO) and ESA's Solar Orbiter spacecraft. He currently teaches Hydrodynamics at the Master M1 space sciences & technologies program at Université PSL and Solar numerical projects at the Master M2 physics of plasmas and fusion program across several French institutions. His work with the OHM code and TOPOTR framework represents a significant contribution to the methodology of solar physics research, enabling more precise modeling of complex magnetic structures in the solar atmosphere.

• Dynamics of long-span suspension bridges
• Floating bridges
• Stochastic environmental wind and wave loading
• +۷ مورد دیگر

Øyvind Wiig Petersen is an Associate Professor at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on bridge dynamics, wind and wave loading, inverse force identification, structural monitoring, and machine learning applications in structural mechanics. He works extensively with long-span suspension bridges and floating bridge systems. Current research areas include vortex-induced vibrations, Kalman filter applications, wind tunnel testing, and finite element model updating. He has published in leading journals like Journal of Wind Engineering, Mechanical Systems and Signal Processing, and Engineering Structures. His work integrates experimental data with computational models for structural condition assessment and load estimation.

Sukalpa Chandaمشاهده پروفایل

دانشیار

• Machine Learning
• Zero-Shot Learning
• One-Shot Learning
• +۷ مورد دیگر

Sukalpa Chanda is an Associate Professor at the Department of Computer Science and Communication, Halden University College. His research focuses on Machine Learning with applications to Document Image Analysis, Computer Vision, and Video Image Analysis, including advanced methods like Zero-Shot Learning, Deep Learning, and Transformer Networks. PhD in Computer Science from NTNU Appointments: Postdoctoral Researcher at Uppsala University (2018-2019) and Groningen University (2016-2018) Research Interests: Chanda specializes in Zero-Shot and One-Shot Learning for document and image analysis, with applications in handwriting recognition, face generation, and biomedical imaging. His work bridges theoretical machine learning with practical implementations in cultural heritage preservation and healthcare diagnostics. Scientific Collaboration: He collaborates with institutions like Indian Institute of Technology (Pallakad/Patna) and leads the Hugin Munin Project under The Digital Society research priority area. His team includes Master’s students and research assistants working on Transformer Networks and generative models. Key Publications: Recent works include frameworks for zero-shot action recognition (T2L, 2025), Nordic manuscript writer identification (2023), and advanced medical image segmentation networks (PAANet, 2021). His research spans document analysis, deep metric learning, and biomedical applications.

• Cloud Computing
• Cybersecurity
• Software Engineering
• +۶ مورد دیگر

Kyrre Begnum is an Associate Professor at OsloMet – Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design through the Department of Computer Science. His research spans cloud computing, cybersecurity, and software engineering, with a focus on virtual machine management, intrusion detection, and autonomic systems. Cloud Computing Cybersecurity Software Engineering Machine Learning Data Center Management His recent publications emphasize adversarial machine learning in security, affinity-aware scheduling for cloud efficiency, and network optimization in virtualized environments. While no formal awards are documented in the provided texts, his work demonstrates technical innovation in distributed systems and educational applications of virtualization technology.

Kseniia Marcqمشاهده پروفایل

پژوهشگر ارشد

• Educational Assessment
• Item Response Theory
• Large-Scale Educational Studies
• +۳ مورد دیگر

Kseniia Marcq is a Research Fellow at the Centre for Educational Measurement (CEMO) at the University of Oslo. Her primary role involves postdoctoral research focusing on educational assessment methodologies. She holds a PhD in Educational Sciences (expected 2025) and an MSc in Measurement, Assessment, and Evaluation from the University of Oslo. Her academic background includes roles as a research assistant and teaching assistant in various educational measurement courses. Her research interests center on large-scale educational assessments, particularly the Programme for International Student Assessment (PISA), with a focus on item format analysis, nonresponse patterns, and psychometric methodologies. She explores cross-country comparisons, gender disparities in educational metrics, and improving the reliability of assessment instruments. Her publications analyze PISA 2018 data, examining item nonresponse, cognitive domain measurement, and methodological advancements in kernel equating and IRT models. She contributes to projects like the 'Early and Adapted Assessment for Language Skills' (SEALS) and collaborates with the Frontier Research in Educational Measurement (FREMO) group. Teaching responsibilities include courses on data science, measurement models, linear models, and survey methodology. She supervised a Master’s thesis on PISA’s mathematical literacy scores and actively participates in academic discourse through peer-reviewed journals. Her work bridges theoretical psychometrics with practical applications in international educational evaluations.