Jørgen Røysland Aarnes is a Research Fellow at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). His research spans fluid dynamics, computational methods, and philosophy of science, with particular focus on turbulence, multiphase flows, and structural realism in Ernst Cassirer's philosophy. He completed his PhD in 2018 with a thesis on particle-laden flows impinging on cylinders. Research Interests: Computational fluid dynamics (CFD), including high-order methods and overset grids Turbulent flows and free-surface vortex structures Particle-laden flow dynamics and impaction mechanisms Philosophy of science, structural realism, and symbolic forms Recent Contributions: His 2025 work on free-surface vortex patterns and 2024 philosophical papers on Cassirer's symbolic forms demonstrate interdisciplinary expertise. He has developed computational tools like the Pencil Code for fluid simulations. Awards: No scientific awards explicitly listed, though his work has been presented at major conferences including the European Turbulence Conference and European Geosciences Union meetings. Advising & Grants: No explicit grants or advisees listed, though his PhD supervision experience is implied through his postdoctoral role. Labs/Teams: Collaborates with the Pencil Code Collaboration and NTNU's energy engineering research groups.
Xing Cai is a Professor at the Department of Informatics, University of Oslo, specializing in Scientific Computing and Machine Learning. His academic career spans several decades with a consistent focus on high-performance computing and its applications to complex scientific problems. He maintains an active research profile with numerous publications in top-tier journals and conferences. Professor Cai's research interests encompass parallel programming and high-performance computing, performance modeling and optimization, automated code generation, heterogeneous computing, and numerical methods for solving partial differential equations. His work extends to specialized applications in computational cardiology, computational geoscience, and biomedical computing. His research bridges theoretical computer science with practical applications in medicine and earth sciences, demonstrating exceptional interdisciplinary reach. An analysis of his recent publications (2019-2024) reveals a strong trend toward leveraging novel hardware architectures (GPUs, AI processors, specialized accelerators) for scientific computing, with particular emphasis on cardiac modeling applications. His work shows increasing sophistication in hardware-aware algorithm design, with publications spanning from fundamental performance modeling to domain-specific applications. The interdisciplinary nature of his work is evident in the diverse range of journals and conferences where he publishes, from computer science venues to specialized medical and geoscience publications. Professor Cai leads or participates in several significant research projects including the EuroHPC Centre of Excellence: Numerical Modeling of Cardiac Electrophysiology at the Cellular Scale (MICROCARD-2), High resolution simulation of cardiac electrophysiology on realistic whole-heart geometries, Maelstrom Associate Team, ODISSEE, Simula-Berkeley Education and Research collaboration (SIMBER), and aCG eX3: Experimental Infrastructure for Exploration of Exascale Computing. These projects reflect his leadership in both computational methodology development and domain-specific applications. His research group maintains strong collaborations with medical researchers, particularly in cardiac electrophysiology, and with geoscientists working on reservoir simulation. The publications list demonstrates consistent mentorship of junior researchers, with frequent co-authorship patterns suggesting an active supervision of PhD and postdoctoral researchers. His work on the EMI model for cardiac tissue represents a significant contribution to computational cardiology with potential clinical applications. The laboratory environment surrounding Professor Cai's work appears to be well-equipped for high-performance computing research, with access to advanced hardware platforms including GPU clusters, AI processors, and specialized accelerators. His publications on the use of Graphcore IPUs, Xeon Phi processors, and NVIDIA architectures indicate a well-resourced research environment capable of experimenting with cutting-edge hardware.
Roles and Affiliations: Diana Sousa Marques Santos is a Professor of Portuguese Linguistics at the University of Oslo (UiO), affiliated with the Institute for European Languages, Literature, and Area Studies (ILOS) within the Faculty of Humanities. Since 2011, she has held positions as Associate Professor (until 2012) and subsequently Full Professor. Her research focuses on computational linguistics, corpus-based studies, and cultural analysis, particularly in the context of Portuguese and Lusophone literature. Education: She earned a 5-year engineering degree in Electronics and Computers from the Technical University of Lisbon (IST), followed by an MSc in Machine Translation and a PhD in contrastive corpus-based semantics, both from IST. Research Interests: Diana’s work spans computational processing of Portuguese, natural language processing, semantics, translation, and distant reading. She leads the Linguateca project, fostering computational resources for Portuguese and collaborates internationally with institutions like the University of Southern Denmark. Key projects include PANTERA (parallel corpus for Portuguese-Norwegian translation studies) and contributions to the European Literary Text Collection (ELTeC) for distant reading. Teaching: She teaches Portuguese grammar, linguistics, and cultural studies at undergraduate and graduate levels, including courses like Introduction to Statistical Thinking for PhD students. Her pedagogical efforts include co-authoring textbooks such as Viva! for Portuguese language education. Projects and Activism: Diana advocates for open-source software in academia, notably opposing the University of Oslo’s shift to Microsoft Exchange. She promotes Lusophone cultural studies and participates in initiatives like the BILLIG project (GIS and NLP in literary geography). Her work bridges computational methods and humanities, emphasizing interdisciplinary approaches. Labs and Teams: She contributes to research groups such as Digital Humanities, Grammar and Meaning Representations, and the Ibero-American Research Hub (IberoARH), fostering collaborative projects in language technology and cultural analysis.
Carsten Griwodz is a Professor in the Department of Informatics (IFI) at the University of Oslo, specializing in digital infrastructure and security. He leads the Distributed Infrastructure and Security (DIS) research group and contributes to several specialized labs including the Sustainable Immersive Networking Lab (SINLAB), Imagine Beyond 5G Blockchain Lab, and the AliceVision Association. Professor at Department of Informatics, University of Oslo Section leader for DIS: Distributed Infrastructure and Security Group member in Networks and Distributed Systems (ND) Active in sustainable immersive networking and blockchain labs Co-founder of AliceVision open-source photogrammetric framework His research focuses on network performance, edge computing, and immersive technologies. He has pioneered work in cloud gaming QoE, real-time 3D reconstruction, and low-latency systems. Current projects explore redirected walking in VR, GPU programming for tracking, and sustainable networking solutions. Recent publications (2021-2024) demonstrate his expertise in network delay analysis, point cloud compression, virtual reality environments, and GPU-accelerated systems. His work bridges computer science fundamentals with cutting-edge applications in multimedia, security, and health informatics. He supervises numerous master's theses covering diverse topics from wireless streaming challenges to medical imaging advancements, consistently mentoring on topics related to network optimization, immersive technologies, and GPU computing. Supervised 40+ master's theses (2004-2024) Thesis topics span network performance, GPU programming, VR systems, and multimedia processing Active in both theoretical and applied research domains Mentoring interests include HCI, distributed systems, and real-time processing Contributes to education through research-led supervision As a permanent faculty member with extensive publication records and active research groups, Griwodz maintains significant influence in academic circles through both his technical contributions and educational mentorship.
Per Kristen Jakobsen is a Professor at the Department of Mathematics and Statistics , UiT The Arctic University of Norway. His work spans interdisciplinary research in nonlinear optics , quantum field theory , and climate modeling , with a focus on mathematical and computational approaches to complex systems. Teaches graduate courses in partial differential equations (Mat-3200), nonlinear waves (Mat-3202), and climate dynamics (Mat-3213). A member of the Complex Systems Modeling (CoSMo) research group, addressing phenomena from optical pulse propagation to Arctic sea ice loss. Recent research highlights include: Climate modeling predictions for Arctic summer sea ice loss (Environmental Research Letters, 2024). Boundary integral methods for nonlinear scattering and extreme nonlinear optics (Physical Review A, 2019–2020). Studies on gain media and superoscillatory functions in electromagnetic theory (Optical Review, 2021). His methodological expertise includes quantum resonant-state expansions , leaky-mode analysis , and stochastic entropy models . He is based at Realfagbygget A217, Tromso.
Dr. Sijing Shen is an Associate Professor at the Institute of Theoretical Astrophysics, University of Oslo . She specializes in numerical simulations of galaxy formation and evolution, focusing on interactions between galaxies and the intergalactic medium, galactic winds, and the physics of the interstellar medium. PhD in Astrophysics (McMaster University, 2010) Postdoctoral Fellow (University of California, Santa Cruz, 2010-2014) Research Associate (University of Cambridge, 2014-2016) Associate Professor (University of Oslo, 2017-present) Her research employs high-performance computing and numerical magnetohydrodynamics to model the co-evolution of supermassive black holes and galaxies, the origins of galactic magnetic fields, and observational signatures of high-redshift galaxies. She develops and utilizes massively parallel magneto-hydrodynamic codes to create virtual universes for comparison with observational data. Recent publications highlight her work on dwarf galaxies, circumgalactic medium physics, and spectral diagnostics for next-generation telescopes like AtLAST. Key themes include feedback mechanisms, dark matter interactions, and computational methods in astrophysics. Young Research Talents Grant, Research Council of Norway Shen supervises 2 PhD students and 1 postdoctoral fellow. Her work bridges theoretical modeling with observational predictions, particularly in galaxy formation and evolution processes.
Tor Flå is a Professor at the Department of Mathematics and Statistics, UiT The Arctic University of Norway. His research spans mathematical modeling in biology and physics, with a focus on quantum chemistry, cancer dynamics, and bioinformatics. He leads projects in computational biology, plasma physics, and protein adaptation studies. Key contributions include developing the DeltaProt software for comparative genomics and advancing multiwavelet-based methods for electronic structure calculations. His work on leukemia stem cell dynamics and cold-adapted enzymes bridges mathematics and life sciences. Flå collaborates with international teams on nonlinear systems and has been affiliated with research groups like CoSMo (Complex Systems Modeling). Flå’s publications span journals like Journal of Mathematical Chemistry , PLOS ONE , and BMC Bioinformatics . He has advised interdisciplinary projects but no specific student names are listed. His research emphasizes computational methods, statistical analysis of biological sequences, and theoretical frameworks for complex systems.
Rakesh Kumar is an Associate Professor in the Department of Computer Science at NTNU, affiliated with the Computer Architecture Lab (CAL). He received his PhD from UPC Barcelona in 2014 and previously worked at Uppsala University, the University of Edinburgh, and Intel Barcelona Research Center. His research focuses on improving datacenter efficiency through microarchitecture and memory systems, hardware/software co-design, and dynamic code translation. Education: PhD in Computer Architecture, UPC Barcelona (2014) MEng in Microelectronics, BITS Pilani (2008) BTech in Electronics and Communications, Kurukshetra University (2005) Research Interests: His work emphasizes processor microarchitecture, memory systems, and energy-efficient designs. Key areas include hardware/software co-design (e.g., Nvidia Denver alternatives), dynamic vectorization, and server optimization. Recent projects target server front-end bottlenecks, address translation efficiency, and BTB organization for data centers. Publications: Recent work includes contributions to IEEE/ACM MICRO, HPCA, and ISCA, focusing on topics like uneven block size instruction caches, address translation optimizations, and server architecture improvements. Awards: Intel Spontaneous Level II/Excellence Award (2014) Distinguished Artifact Award at IEEE/ACM MICRO 2023 Teaching & Advising: Teaches courses like TDT4258 Low Level Programming and advises students on projects such as vector unit design and microarchitecture optimization. Active in mentoring PhD candidates and leading the Computer Architecture Lab. Labs/Teams: Affiliated with the Computer Architecture Lab (CAL) and collaborates on projects like DARCO, an infrastructure for HW/SW co-designed virtual machines.
Kjell Magne Mathisen is a Professor in the Department of Structural Engineering at the Norwegian University of Science and Technology (NTNU), specializing in computational mechanics and numerical methods. His work bridges advanced engineering computation with practical applications in structural analysis, contact mechanics, and fluid-structure interaction. Research Focus: Computational mechanics, Isogeometric analysis, Finite Element Method, Nonlinear structural analysis, Fluid-structure interaction, Error estimation. Affiliation: NTNU, Department of Structural Engineering. His recent publications (2019-2024) emphasize wind-resistant bridge design using ALE-VMS and Isogeometric methods, machine learning applications in additive manufacturing, and adaptive refinement techniques for thin plates. Collaborations with leading researchers like Yuri Bazilevs and Trond Kvamsdal highlight cross-institutional efforts. Professor Mathisen's technical competencies include software architecture for engineering computations, parallel processing, and multiscale modeling, with historical contributions to shell analysis, sparse matrix methods, and contact problem simulations.
Giles Reger is a Senior Lecturer in the School of Computer Science at the University of Manchester , affiliated with the Formal Methods Group . His academic journey includes a BA in Computer Science from the University of Cambridge (2009), an MSc in Advanced Computer Science (University of Manchester, 2010) with the Highest Achiever of the Year Award , and a PhD (University of Manchester, 2014) on runtime verification. Research Interests: Theorem Proving (via Vampire system) and Runtime Verification (via MarQ and VyPR tools). Collaborations: Projects with University of Oxford, ARM, AWS, CERN, and SnT Luxembourg. Recent Work: Giles' publications span 2019-2016, focusing on Vampire's higher-order reasoning, symmetry avoidance in finite model finding, neural guidance in theorem proving, and runtime verification for Python web services (VyPR2). Trends include integrating machine learning with formal methods and advancing logic-based verification tools. Scientific Awards: Highest Achiever of the Year Award (MSc, University of Manchester, 2010) Vampire's multiple trophies at CASC and SMT-COMP competitions Advising: Supervises PhD students Michael Rawson, Ahmed Bhayat, and Joshua Dawes. Labs/Teams: Contributes to the Vampire team and the VyPR project.
Lars Ailo Bongo is a Professor at the Department of Computer Science, UiT The Arctic University of Norway. His research interests span operating systems, concurrent programming, data-intensive computing, and applications of machine learning in healthcare and bioinformatics. He has been actively involved in teaching courses such as Operating Systems, Concurrent Programming, and Algorithms, emphasizing open-access educational materials. Notably, three of his master’s students received awards for their theses: Nina Angelvik (2018), Bjørn Fjukstad (2014), and Martin Ernstsen (2013). He also advises students like Johan Ravn, whose master’s project led to a startup. Bongo’s work includes developing scalable bioinformatics pipelines (e.g., NeLS infrastructure), optimizing genomics workflows, and advancing tools for medical image analysis. He pioneered open-access course repositories on GitHub and contributed to visualization frameworks like GeneNet VR. His research bridges computer science with healthcare, focusing on tumor infiltration lymphocyte analysis, AI-driven diagnostics, and ethical AI applications in mental health. He has held roles as a postdoc at Princeton and TA at the University of Tromsø. His scientific contributions span over 50 peer-reviewed articles, covering topics from parallel computing to synthetic health data interoperability. He emphasizes reproducibility and transparency in data management, exemplified by projects like Occode for historical data transcription and MORTAL language for cross-paradigm computing. In education, Bongo advocates for accessible teaching materials and has designed courses like INF-2202 Concurrent Programming, which utilize modern tools (e.g., Go language) and GitHub repositories. His teaching philosophy integrates practical coding with theoretical foundations, fostering student innovation and open-source collaboration.
Håvard Arnestad is a Research Fellow at the University of Oslo's Department of Informatics, affiliated with the Faculty of Mathematics and Natural Sciences. He is part of the Digital Signal Processing and Image Analysis (DSB) research group and serves as a Group Teacher and Lecturer for the course 'Ultrasound imaging' (IN3015/IN4015). He organizes a bi-weekly journal club focusing on acoustic imaging advancements in sonar and medical ultrasound. Education: Håvard holds a Master's in Engineering Physics from NTNU (Trondheim), specializing in acoustics. His work experience includes roles at NEO (spectroscopy), CERN (superconducting magnet testing), and Cisco (acoustics/audio engineering). Research interests span three core areas: classical/adaptive beamforming, interval arithmetic for beampattern analysis, and subsonic radiation from leaky Lamb waves. His work challenges conventional acoustics assumptions and explores applications in medical ultrasound and sonar systems. Key collaborators include Ole Marius Rindal, Andreas Austeng, Sven Peter Näsholm, and Gabor Gereb. Upcoming conferences include the 2025 Scandinavian Symposium on Physical Acoustics (Geilo), Northern Lights Deep Learning Conference (Tromsø), and the Acoustical Society of America meeting (New Orleans). Past engagements include international symposiums in Germany, Canada, Italy, and the UK. His advising includes MSc students Helene Wold (now at Sonitor) and Chaoran Han (now a PhD candidate). He maintains active profiles on Google Scholar, ResearchGate, ORCID, and LinkedIn, contributing to interdisciplinary research at the intersection of acoustics, signal processing, and numerical simulations.
Fatih Kizilaslan is a Research Fellow in the Department of Biostatistics at the University of Oslo, Norway, specializing in statistical models for high-dimensional and functional data. He holds a PhD in Mathematics from Gebze Technical University (2015) and has extensive academic experience, including roles as Associate Professor (2019-2023) and Assistant Professor (2016-2019) at Marmara University, Turkey. His research focuses on cure models, frailty modeling, and reliability analysis. He has conducted research at McMaster University (Canada) and the Gebze Institute of Technology (Turkey). His academic interests include survival analysis, statistical inference for extreme-value distributions, and applications of biostatistics in medical research. He is affiliated with the Statistical models for high-dimensional and functional data research group. His work emphasizes methodological advancements in stress-strength reliability, multicomponent systems, and record-based statistical models. Notable contributions include publications on cure models, frailty analysis, and reliability estimation in complex systems. His research bridges theoretical statistics with practical applications in biomedical and engineering contexts. He actively collaborates on projects involving high-dimensional covariates and predictive modeling for clinical outcomes.
Johannes Langguth is an Associate Professor at the Department of Informatics, University of Bergen. His work focuses on high-performance computing, graph algorithms, and social network analysis. He has contributed to GPU acceleration techniques for cardiac simulations and graph processing on manycore architectures. Langguth's research intersects computational science with societal challenges, including misinformation detection in social media and conspiracy theory analysis. He leads projects on temporal interaction networks and has developed datasets like COCO and GECO to study pandemic-related disinformation. His interdisciplinary approach combines computer science with psychology and public health, addressing both technical and human aspects of information dissemination. Notable contributions include optimizing breadth-first search on Graphcore IPUs and applying graph neural networks for combinatorial problems. Langguth collaborates widely, with affiliations at Simula Research Laboratory and BI Norwegian School of Business. His work is funded by institutions like the Research Council of Norway and the EU, reflecting its applied and impactful nature in both academia and industry.
Bjørn Atle Johan Angelsen is a Professor affiliated with the Norwegian University of Science and Technology (NTNU), based at AHL-senteret (Prinsesse Kristinas gate 3, Øya). His research focuses on medical ultrasound imaging, nonlinear acoustics, and biomedical engineering, with emphasis on applications in drug delivery, tissue characterization, and contrast agent detection. He has supervised multiple PhD students, including Ola Finneng Myhre, Jochen Rau, and Rune Hansen. His work frequently appears in journals like the Journal of the Acoustical Society of America and IEEE Transactions on Ultrasonics . Angelsen's research interests include dual-frequency ultrasound technologies, acoustic radiation force mechanisms, and the development of advanced imaging techniques for clinical diagnostics. His studies often involve collaborations on nanoparticle transport in biological tissues and the optimization of transducer designs to reduce scattering and enhance image clarity. His recent articles highlight advancements in nonlinear elasticity imaging, ultrasound-enhanced drug delivery, and the exploitation of acoustic properties for tissue classification. These contributions bridge fundamental physics with translational biomedical applications. While no specific awards are noted in the text, his extensive publication record and role as a supervisor at NTNU underscore his significant contributions to the field.
