جستجوی استاد راهنمای دانشگاهی

• 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.

Lars Moberg Salbuمشاهده پروفایل

پژوهشگر ارشد

• Computational Geometry
• Algebraic Topology
• Parameterized Complexity
• +۳ مورد دیگر

Lars Moberg Salbu is a Postdoctoral Fellow at the Department of Informatics, University of Bergen. His research focuses on computational geometry, algebraic topology, and parameterized complexity, with contributions to graph theory, metric analysis, and algorithm design. He has collaborated with institutions such as the Department of Mathematics and Department of Computer Technology at Western Norway University of Applied Sciences. His work bridges theoretical mathematics and computer science, addressing challenges in topological data analysis and combinatorial optimization. Key publications include studies on transition graph dynamics, minimum bounded chains, and homology determination from data samples. Salbu’s research emphasizes interdisciplinary approaches, leveraging discrete mathematics and geometric principles to solve complex computational problems. No scientific awards are listed, but his active participation in international journals like IEEE Access and Mediterranean Journal of Mathematics highlights his academic contributions. Collaborations with co-authors such as Morten Brun and Belen Garcia Pascual underscore his collaborative approach to advancing theoretical frameworks in his field.

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.

Pinar Heggernesمشاهده پروفایل

استاد

• Algorithms
• Graph Theory
• Combinatorics
• +۳ مورد دیگر

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 Brunمشاهده پروفایل

دانشیار

• Topological Data Analysis
• Computational Topology
• Persistent Homology
• +۳ مورد دیگر

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.

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.

Mostafa Bakhoday Paskyabiمشاهده پروفایل

دانشیار

• Offshore Wind Energy
• Environmental Impacts of Offshore Wind Turbines
• Wind-Wave-Turbulence Interaction
• +۷ مورد دیگر

Mostafa Bakhoday Paskyabi is an Associate Professor at the Geophysical Institute , University of Bergen. His research focuses on offshore wind energy , environmental impacts of turbines , wind-wave-turbulence interaction , and climate change effects on wind parks . He employs Large Eddy Simulation (LES) and data assimilation for digital twin development. Teaching: Course responsible for ENERGI322 and ENERGI101; lecturer for ENERGI321 and ENERGI360. Supervision: Advises PhD candidates like Maria Krutova, Xu Ning, and Øystein Husevåg Døskeland, alongside master’s students including Fahim Masud Ahmed and Tord Nylan Malmei. His research explores physical oceanography (ocean mixing, Lagrangian particle tracking), air-sea interaction , and reduced-order modeling for offshore energy systems. Recent work examines swell wave impacts on turbulence and LiDAR-based data assimilation for transient event forecasting. Projects include LESWIND , WindSys , HIPERWIND , and Wind3D , addressing offshore wind design, ecosystem impacts, and climate operability. Collaborations span institutions like NTNU, NORCE, and IMR.

Balint Laczkoمشاهده پروفایل

پژوهشگر ارشد

• Music Technology
• Sonification
• 3D Audio
• +۶ مورد دیگر

Balint Laczko is a Doctoral Research Fellow at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, affiliated with the Faculty of Humanities at the University of Oslo. His research focuses on image sonification and interdisciplinary studies in autophagy, combining expertise in music technology, machine learning, and computer vision. Education: Master's degrees in Classical Composition (Liszt Academy of Music, Budapest, 2017) and Performance Technology & Electroacoustic Composition (Norwegian Academy of Music, Oslo, 2021). Research interests include 3D audio, audiovisual analysis, and the development of tools for musical gesture analysis. His work bridges artistic and technical domains, with publications in conferences like ICAD and ACM. Key projects: AUTORHYTHM (biological-music tech collaboration), Sonification Pilot (bioRITMO).

Petr Golovachمشاهده پروفایل

استاد پژوهش

• 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.

• Parameterized Complexity
• Algorithms
• Graph Theory
• +۳ مورد دیگر

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.

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.

Odd Kolbjørnsenمشاهده پروفایل

دانشیار

• Geophysics
• Seismic Inversion
• Bayesian Statistics
• +۳ مورد دیگر

Odd Kolbjørnsen is an Associate Professor at the University of Oslo's Department of Mathematics, affiliated with the Faculty of Mathematics and Natural Sciences. His primary research focuses on geophysics, seismic inversion, Bayesian statistics, and data-driven reservoir modeling. He holds a strong academic background in mathematical geosciences, with contributions to methodologies like Bayesian inversion, Markov mesh modeling, and deep learning applications in geoscience. His work bridges traditional geophysical analysis with modern computational techniques, emphasizing uncertainty quantification and high-resolution imaging in reservoir characterization. Key research areas include seismic data reconstruction, multi-task neural networks for flow metering, and 4D seismic inversion for lithology-fluid prediction. His publications span journals like Geophysics , Mathematical Geosciences , and Neural Networks , reflecting interdisciplinary collaborations in geostatistics and energy engineering. No scientific awards are explicitly listed, though his extensive publication record underscores his research impact. His advisory role and involvement in research groups like 'Statistics and Data Science' at UiO highlight his academic leadership. Ongoing work focuses on integrating machine learning with geophysical data analysis and CO2 sequestration optimization.

Nello Blaserمشاهده پروفایل

استاد

• Topological Data Analysis
• Machine Learning
• Statistics
• +۳ مورد دیگر

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.

Mateus De Oliveira Oliveiraمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Automated Theorem Proving
• Parameterized Complexity
• +۳ مورد دیگر

Mateus De Oliveira Oliveira is an Associate Professor in the Department of Informatics at the University of Bergen. His research focuses on theoretical computer science, automated theorem proving, parameterized complexity, formal verification, graph theory, and computational logic. His work spans algorithm design for constraint satisfaction problems, automated reasoning systems, and formal verification techniques. Notable contributions include advancements in width-based automated theorem proving, Petri net synthesis, and parameterized complexity analysis for graph algorithms. He leads the Autoproving project funded by the Research Council of Norway, exploring automated theorem proving through parameterized complexity theory. Recent publications highlight developments in symbolic functional decomposition, state canonization for theorem proving, and optimal dynamic programming algorithms. His research bridges foundational theory with practical applications in automated systems and formal methods.

Martin Vatshelleمشاهده پروفایل

دانشیار

• Graph Algorithms
• Parameterized Algorithms
• Algorithm Engineering
• +۹ مورد دیگر

Martin Vatshelle is an Associate Professor in the Department of Informatics at the University of Bergen (UiB) , Norway. He obtained his PhD from UiB in 2012 with a thesis titled New Width Parameters of Graphs . His research is centered on theoretical computer science, particularly in the design and analysis of graph algorithms. His main research interests include: Graph Algorithms Parameterized and Fixed-Parameter Tractable (FPT) Algorithms Algorithm Engineering Graph Theory, especially width parameters like boolean-width, clique-width, and branch decompositions Dynamic programming on structured graphs The recent publications of Martin Vatshelle reflect a strong focus on structural graph theory and algorithmic efficiency. His work frequently explores the computational complexity of graph problems, leveraging width parameters to design faster exact or parameterized algorithms. Themes such as boolean-width bounds, vertex partitioning, and satisfiability problems (#SAT, MaxSAT) are recurrent, indicating a deep engagement with both theoretical foundations and practical algorithmic improvements. There are no scientific awards mentioned in the provided texts. Martin Vatshelle has taught a variety of courses in algorithms, discrete mathematics, and complexity theory since 2004, including Algorithms, Data Structures and Programming , Complexity Theory , and Algorithm Engineering . There is no mention of research grants or supervised students in the provided materials. He collaborates extensively with researchers such as Jan Arne Telle and Sigve Hortemo Sæther. He is affiliated with the research group on Didactics within the Department of Informatics. No separate lab or research center is mentioned.