Parameterized Algorithms Professors and Supervisors in Norway

Pål Grønås Drangeمشاهده پروفایل

دانشیار

University of BergenNorway

Pål Grønås Drange is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on parameterized complexity of graph algorithms, experimental algorithmics, and applications in machine learning and network interdiction. He leads projects on trustworthy AI and energy transition optimization. Dr. Drange holds a diploma in electronics, bachelor's in cognitive science, master's in logic for AI, and a PhD in algorithms. His work bridges computer science, mathematics, and engineering. Education: PhD in Algorithms (University of Bergen) Master's in Logic for AI Bachelor's in Cognitive Science Electronics Diploma His research interests include parameterized complexity, sparse network algorithms, and applied machine learning. Recent projects involve correlation clustering with overlapping data and autonomous drone inspection algorithms. He is affiliated with the Center for Data Science (CEDAS) and leads the Algorithmic Foundations for Trustworthy AI project funded by the Trond Mohn Foundation. Dr. Drange has received the 2024 outreach award for science communication. His lectures on AI ethics and societal impacts have reached diverse audiences, including policymakers and industry leaders. He is active in program committees for major conferences like ESA, COCOON, and SWAT. Key Achievements: PACE 2024: 1st in parameterized track, 2nd in heuristics track Lead software advisor for Equinor's energy transition projects Open-source contributions to algorithmic toolkits Teaching includes courses on algorithms, machine learning, and software engineering. He advises industry workshops and has conducted over 30 outreach lectures on AI ethics and technology governance in 2023–2025.

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Daniel Lokshtanovمشاهده پروفایل

استاد

University of BergenNorway

Daniel Lokshtanov is a Professor at the Department of Informatics, University of Bergen. His research focuses on parameterized algorithms, graph theory, and computational complexity. He has collaborated extensively with leading researchers such as Fedor Fomin and Saket Saurabh. Notable contributions include foundational work on kernelization techniques, fixed-parameter tractability, and algorithm design for graph problems. His work often addresses structural graph properties and combinatorial optimization challenges. Lokshtanov has authored influential papers on topics like Odd Cycle Transversal, cograph completions, and algorithms for planar graphs. He co-authored the textbook *Kernelization: Theory of Parameterized Preprocessing* (2019), a key reference in the field. His research spans theoretical breakthroughs and practical algorithmic advancements, with over 150 publications in top venues like ACM Transactions on Algorithms and SIAM Journal on Computing.

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Henrik Andersen Sveinssonمشاهده پروفایل

پژوهشگر

University of OsloNorway

Henrik Andersen Sveinsson is a Researcher and Postdoctoral Fellow at the University of Oslo's Department of Physics, affiliated with the Center for Computing in Science Education. His research focuses on molecular simulations of fractures in geologically relevant materials like silica, ice, and gas hydrates, alongside fundamental studies of friction dynamics. He teaches courses including HON1000 (Cross-Disciplinary Thematic Focus for Honours Students) and HON2200 (Data-driven Projects), alongside prior instruction in electromagnetism and wave physics. His work bridges computational physics and geoscience, with recent papers exploring machine learning applications in atomic force modeling, methane hydrate mechanics under varying conditions, and diffusion-driven frictional aging. Key contributions include studies on nanoscale damage production in quartz and the role of water ice in nanocrystalline materials. Publications highlight interdisciplinary approaches, combining molecular dynamics with AI tools for material property prediction and environmental impact assessments. Sveinsson’s research has implications for understanding earthquake mechanics, climate-related material stability, and energy storage systems. His academic background includes a PhD in computational physics (2019) and master’s/bachelor’s degrees from UiO (2015/2013). Current projects include the ChemFrac initiative on reaction-induced fracturing and CO2Basalt studies. No awards are explicitly listed, though his work is broadly collaborative with institutions like the Norwegian Broadcasting Corporation and international journals.

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Einar Malvin Rønquistمشاهده پروفایل

استاد

Norwegian University of Science and TechnologyNorway

Einar Malvin Rønquist is a Professor and has served as Head of the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) since August 2013. He has been a faculty member at the same department since 1999 and is affiliated with the differential equations and numerical analysis (DNA) research group, which he led from 2010-2011. His extensive publication record demonstrates a sustained research career spanning over three decades in computational mathematics. Professor Rønquist's research focuses primarily on numerical methods for partial differential equations, with special emphasis on spectral methods, reduced basis methods, and computational fluid dynamics. His work addresses challenges in high-order approximation, model reduction, and efficient computation for complex physical systems. He has made significant contributions to the development of tensor-product solvers for deformed domains, interface tracking algorithms, and the theoretical foundations of reduced basis methods. His research bridges theoretical mathematics with practical computational applications across engineering and physical sciences, with particular relevance to fluid dynamics, geometric problems, and multi-physics systems. Analysis of Rønquist's publication trajectory reveals a consistent evolution from foundational spectral element methods to advanced model reduction techniques. His work shows increasing sophistication in handling parametric problems and uncertainty quantification while maintaining rigorous mathematical foundations. The publications demonstrate strong contributions to high-order numerical methods, with applications spanning fluid dynamics, geometric problems, and multi-physics systems. Recent work shows particular emphasis on practical computational implementation, including parallel algorithms and efficient solvers for real-world applications that require solving problems on complex or deformed domains. Rønquist has maintained active international collaborations throughout his career, particularly with researchers at MIT (notably Anthony Patera), Université Pierre et Marie Curie (Yvon Maday), and other leading institutions across Europe and North America. His work has appeared consistently in top journals in computational mathematics and scientific computing, including Journal of Computational Physics, SIAM Journal on Scientific Computing, and Computer Methods in Applied Mechanics and Engineering. As department head since 2013, Rønquist has played a significant administrative role in shaping mathematics education and research at NTNU. His leadership extends beyond pure administration, as evidenced by his 2016 commentary in Universitetsavisa regarding the organization of technical cybernetics and electrical power engineering at NTNU, and his 2021 opinion piece in Aftenposten about mathematics examinations. He continues to actively contribute to both the academic and public discourse on mathematics education and research.

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Henrique Musseli Cezarمشاهده پروفایل

پژوهشگر

University of OsloNorway

Henrique Musseli Cezar is a Researcher at the Hylleraas Centre for Quantum Molecular Sciences within the Department of Chemistry at the University of Oslo. His research focuses on developing molecular simulation methods, particularly applying machine learning potentials to study phenomena such as ionic conductivity in metal-organic frameworks. He holds a Ph.D. in Physics from the University of São Paulo, with postdoctoral experience at both the University of Oslo and the University of São Paulo, where he investigated computational nanoﬂuidics and Monte Carlo methods. Research Interests : His work spans Computational physics and theoretical chemistry, Machine learning-enhanced molecular dynamics, Nanostructured materials and their applications in environmental science, Monte Carlo and Metainference methods, Phase behavior and adsorption properties in nanomaterials. Awards : He has received notable recognition including the Best PhD thesis in Atomic/Molecular Physics (Brazilian Physical Society), Top Downloaded Paper awards, and multiple poster/flash talk accolades for his contributions to molecular simulation and computational methods. Grants & Fellowships : His postdoctoral research was supported by FAPESP (Brazil) for nanoﬂuidics studies, CAPES and CNPq fellowships for his Ph.D. and Master’s research, Current affiliation with the Hylleraas Centre funded by Norwegian research grants. Labs & Collaborations : Active in the Hylleraas Centre, he collaborates internationally on projects blending computational and experimental approaches, such as the H2O-MOF initiative and studies on surfactant structures and greenhouse gas adsorption.

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Jan Arne Telleمشاهده پروفایل

استاد

University of BergenNorway

Jan Arne Telle is a Professor in the Department of Informatics at the University of Bergen. His research focuses on algorithms, computational complexity, and graph theory, with recent contributions to explainable AI (XAI) and machine teaching. He leads the Norwegian Research Council-funded project 'Machine Teaching for Explainable AI' and has taught courses such as INF339 on Algorithmics of Causality. His work spans graph algorithms, parameterized complexity, and combinatorial optimization, with notable contributions to graph decomposition techniques like mim-width, cliquewidth, and boolean-width. Collaborations include researchers from institutions like the University of Rostock and the University of Montpellier. Key research trends include analyzing time series classification interpretability, optimizing robust simplifications for machine learning models, and solving combinatorial problems in machine teaching. Telle's algorithms for problems like Feedback Vertex Set and Perfect Matching Cut have advanced the field of parameterized complexity. Despite no listed awards, his extensive publication record reflects significant academic impact. He is involved in the Algorithms Research Group at UiB and contributes to computational theory education and outreach, including participation in programming competitions and academic leadership roles.

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Anders Malthe-Sørenssenمشاهده پروفایل

استاد

University of OsloNorway

Anders Malthe-Sørenssen is a Professor at the University of Oslo’s Center for Computing in Science Education within the Faculty of Mathematics and Natural Sciences. His research integrates artificial intelligence (AI) with neuroscience to develop advanced machine learning techniques and study brain-inspired AI systems. He also focuses on computational methods in materials science, particularly in frictional dynamics and nanoscale processes, alongside computational education research to enhance science teaching. Malthe-Sørenssen teaches courses including Fys1120 (Electromagnetics), HON1000 (Interdisciplinary Honors), and Fys4460 (Disordered Systems and Percolation), emphasizing active learning and technology integration. His past industrial experience includes tech startups and large-scale industry-funded projects, leading to patented technologies. His research interests span computational physics, machine learning, neuroscience, and education innovation. Notable projects include studies on neural circuitry, AI robustness, and material fracture mechanics. He has received awards such as the HMK Gold Medal (1999), Nansen Prize (2001), and multiple teaching excellence prizes (2011–2015). Malthe-Sørenssen leads initiatives like the CO2Basalt project for carbon storage and contributes to international collaborations. His work bridges AI, materials science, and education, fostering interdisciplinary innovation.

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Øyvind Fretteمشاهده پروفایل

استاد

University of BergenNorway

Øyvind Frette is a Professor and Vice-Dean for Research and Innovation at the Faculty of Science and Technology, University of Bergen. He holds a PhD in Physics (2002) and specializes in optical remote sensing, biophysics, and environmental science. His research integrates optics, marine biology, and atmospheric studies, with a focus on aerosol retrieval, phytoplankton dynamics, and solar radiation modeling. Education: Doctoral Dissertation: Light and Life in Norwegian Coastal Waters: Aspects of Optical Remote Sensing (2002) Research Interests: Optical properties of water and atmospheric systems Remote sensing algorithms for marine and terrestrial environments Fluorescence lifetime imaging in biological systems Climate impacts on UV radiation and aquatic ecosystems Publications: Recent work includes advancements in aerosol retrieval algorithms, luminescent solar concentrators, and high-altitude lake studies. His articles highlight interdisciplinary approaches to environmental and optical challenges. Advising & Grants: Supervised over 15 doctoral and master’s students, with notable advisees including Dennis Muyimbwa (atmospheric aerosols) and Nicolausi Ssebiyonga (phytoplankton fluorescence). Active in securing grants for climate and optics research. Labs/Teams: Leads the Optics and Atomic Physics research group at the Department of Physics and Technology.

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Brage Isak Bakkane Keiseråsمشاهده پروفایل

پژوهشگر ارشد

University of OsloNorway

Brage Isak Bakkane Keiserås is a Doctoral Research Fellow in the Department of Informatics at the University of Oslo, Faculty of Mathematics and Natural Sciences. His research is centered on theoretical aspects of computer science, particularly algorithmic graph theory and parameterized complexity. His research interests include: Theoretical Computer Science Parameterized Complexity Graph Algorithms Discrete Mathematics Algorithm Design Computational Complexity The available publication indicates a strong focus on hardness results for domination problems under structural graph parameters, particularly using Mim-Width as a complexity measure. This aligns with current trends in fine-grained complexity and structural parameterizations in algorithm design. He is actively involved in the research project Leveraging Energy-Aware Programming (LEAP) , which suggests an interest in broader programming technology applications, possibly connecting theoretical foundations with energy-efficient computing paradigms. There are no scientific awards listed in the provided text. There is no information available about student advising or research grants beyond project affiliation. No lab or team name is explicitly mentioned, though his work is likely conducted within the Programming Technology research group at UiO.

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Stefan Grunerمشاهده پروفایل

استاد

Western Norway University of Applied SciencesNorway

Stefan Gruner is a Professor in the Department of Computer Science, Faculty of Engineering, Built Environment and Information Technology, at the University of Pretoria, South Africa. He holds advanced degrees in Informatics (PhD, MSc) and Philosophy (MA), reflecting his interdisciplinary research that bridges formal software engineering, digital forensics, and the philosophy of technology. His work is widely published in international journals and conferences, and he is deeply involved in academic service, including supervision, examination, and program committees. Education: MA in Philosophy, Germany, 2010 PhD in Informatics, RWTH Aachen, Germany, 1999 MSc (Dipl.-Inform.) in Informatics, Germany, 1996 His research interests center on Formal Methods , Software Engineering , and the Philosophy of Computer Science . He has made significant contributions to parameterized and three-valued model checking, formal methods for railway systems, and the scientific foundations of digital forensics. His recent work explores historical semantics in software architecture and the philosophical underpinnings of informatics. His publications from the last few years reveal a strong trend towards formal verification, safety-critical systems, and the epistemological aspects of digital investigation and software engineering. Scientific Recognition: Biographic entry in Who's Who in the World (2010 edition) Member of the selection committee for the Annual Best Dissertation Award (EAPLS) Stefan Gruner is an active advisor and supervisor, having guided numerous MSc and PhD students to completion. He has secured research funding from the South African National Research Foundation (NRF) and international partners for projects on model-driven development, CSP protocol verification, and visualization of distributed algorithms. His academic service is extensive, including roles as program chair for workshops like FM+AM and SEFM, editor for conference proceedings, and external examiner for multiple universities. He has also been a visiting researcher at institutions in Bordeaux and Swansea. He was a central organizer of the Distributed Systems Research Group at the University of Pretoria and has fostered international collaborations, particularly within the formal methods and software engineering communities.

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Sushmit Dharمشاهده پروفایل

دانشیار

UiT The Arctic University of NorwayNorway

Sushmit Dhar serves as an Associate Professor in the Department of Automation and Process Technology at UiT The Arctic University of Norway, focusing on control engineering and automation solutions for Arctic maritime challenges. His work bridges theoretical control methods with practical applications in extreme environments. His research spans: Classical and modern control methods (PID, LQR, MPC) State estimation and Kalman filtering Physics-Informed Neural Networks and Reinforcement Learning for decision support IoT-based automation for maritime operations Marine icing phenomena and sea spray measurement Recent publications demonstrate cutting-edge work in marine icing prediction using LiDAR and capacitive sensors, Arctic road safety systems, and optimization of maritime operations. His research integrates advanced sensing technologies with data-driven modeling to address safety challenges in cold climates, particularly through sea spray measurement innovations and route planning algorithms for fishing vessels and search-and-rescue operations. Dr. Dhar actively contributes to collaborative initiatives: Member of the Sustainable Technology and Safety (STS) research group Member of the IR, Spectroscopy, and Numerical Modelling Research Group Key participant in the SPRICE project (Multidisciplinary approach for spray icing modelling and decision support) His teaching portfolio includes foundational courses in Linear Systems (AUT-2604) and Control Engineering (AUT-2801) for Automation Engineering undergraduates, preparing students for technical challenges in Arctic engineering contexts.

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