جستجوی استاد راهنمای دانشگاهی

• Game Theory
• Economic Theory
• Sport Management
• +۱۲ مورد دیگر

Kjetil Kåre Haugen is a Professor in logistics and sport management at Molde University College, Faculty of Business Administration and Social Sciences. His research spans game theory, economic theory, and algorithmic development with applications in sports—particularly football—focusing on doping, gambling, corruption, and uncertainty of outcome. He is an active member of the Sport Management Research Group (SpM). Research Interests: Haugen’s work integrates economic modeling and game theory to analyze sports phenomena such as doping behavior, tournament design, referee decisions (e.g., VAR), and fan engagement. He also explores philosophical and satirical dimensions of economics and academia, often publishing critiques of higher education systems and performance metrics. The recent articles (2020–2025) demonstrate a strong trend in applying mathematical and game-theoretic models to real-world sports issues, especially in football. Topics include cheating detection in chess, doping incentives, penalty kick dynamics, and the economic implications of tournament structures. His interdisciplinary approach bridges economics, computer science, and sports science, with a recurring emphasis on fairness, integrity, and strategic behavior. Scientific Contributions: Extensive publication record in journals like Mathematics for Applications , Scientometrics , and Sports Economics Review . Author of books on event logistics and game theory in soccer. Regular contributor to public discourse via Panorama , Subjekt.no , and The Guardian . Advising and Public Engagement: While no formal students are listed, Haugen frequently collaborates with researchers such as Knut Peder Heen, Harry Arne Solberg, and Alex Krumer. He actively engages in academic and public debates on sports policy, higher education reform, and ethical issues in research. His work has not been linked to specific grants in the provided text, but his consistent output suggests ongoing research activity. Laboratories and Teams: He is affiliated with the Sport Management Research Group (SpM) at Molde University College, which serves as his primary research team for interdisciplinary projects in sports economics and management.

• Software Engineering
• Cloud Computing
• Cybersecurity
• +۵ مورد دیگر

Andrea Arcuri is a Professor at the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. His research focuses on software engineering, particularly in automated testing, search-based techniques, and API security. Current research areas include cloud computing, cybersecurity, and software engineering. Key subfields: REST API testing, GraphQL fuzzing, evolutionary computation, and search-based test generation. Recent work emphasizes industrial applications of automated testing tools and frameworks like EvoMaster. His publications analyze trends in LLM integration with software testing, performance evaluation of industrial API testing, and tool development for search-based fuzzing. While no specific awards are listed, his contributions to software engineering are well-documented through 12 recent publications. Andrea collaborates extensively on empirical studies and tool development for API testing.

Eivind Valenمشاهده پروفایل

استاد

• Translation
• RNA biology
• Genome editing
• +۶ مورد دیگر

Eivind Valen is a Professor at the University of Oslo and holds multiple associate leadership roles at the Center for Molecular Medicine Norway (NCMM) , Center for Myeloid Blood Cancer (C-MYC) , Computational Biology Unit (CBU) , and Michael Sars Center . His research spans RNA biology, genome editing, and translation regulation. Research Focus: Investigates pervasive translation in non-coding regions, CRISPR applications, and RNA structure using nanopore sequencing. Key Tools: Developed CHOPCHOP platforms and ORFik for genome editing and translation analysis. Recent Article Trends: Emphasize ribosome profiling, poly(A) tail dynamics, and CRISPR precision. Awards: Includes ERC Consolidator Grant, EIC Pathfinder award, and KG Jebsen Center recognition. Collaborations: Engaged with institutions across Norway and Europe, including Technische Universität Dresden and EMBL nodes.

• Geriatrics
• Palliative Care
• Dementia Care
• +۳ مورد دیگر

Bettina Elisabeth Franziska Husebø serves as Professor at the Department of Global Health and Community Medicine, University of Bergen, with research focused on enhancing care for aging populations through community-based interventions and digital health innovations. Her expertise spans geriatrics, palliative care, and dementia management, emphasizing dignity-preserving approaches for elderly individuals in community settings and end-of-life care. Current work integrates sensor technology to monitor behavioral changes in neurodegenerative conditions while addressing relocation challenges and volunteer support systems for home-dwelling seniors with cognitive impairments. Recent publications demonstrate a strong trajectory in digital phenotyping applications, particularly using wearable devices to capture physiological and behavioral markers in Parkinson's and dementia patients. This includes methodological frameworks for clinical trial implementation and comprehensive reviews of activity recognition technologies. Her scientific contributions have earned prestigious recognition: Award for outstanding research dissemination (2012) Egget Research Prize - 2nd place (2010) Annual Award Organization Protection of Human Dignity (2010) Poster Award – 1st prize at European Association for Palliative Care Congress (2009) Research Award from Norwegian Pain Society (2009) Torstein Dale award from Norwegian Red Cross (2008) Professor Husebø leads multiple active research initiatives including 'Digital phenotyping of activity changes in people with dementia at the end of life' and 'FRIDE – Volunteering in care for people living at home with dementia', supported by University of Bergen and Vestre Viken HF grants. She previously completed the 'dignified death' project focused on cancer care for elderly women. Her collaborative network spans Norwegian healthcare institutions (VID, NR, OUS, HVL, HAUKELAND) and international partners in Romania, facilitating multi-center studies on community health solutions and technological interventions for aging populations.

• Bio-Inspired Artificial Intelligence
• Evolutionary Algorithms
• Artificial Neural Networks
• +۵ مورد دیگر

Keith L. Downing is a Professor in the Department of Computer Science at NTNU, affiliated with the Faculty of Information Technology and Electrical Engineering. His research focuses on bio-inspired artificial intelligence, including evolutionary algorithms, artificial neural networks, and computational neuroscience. He has authored two MIT Press books: *Intelligence Emerging* (2015) and *Gradient Expectations* (2023). His work bridges AI, artificial life, and cognitive science, emphasizing the emergence of intelligence in both natural and artificial systems. Teaching responsibilities include Introductory AI, AI Programming, Cognitive Architectures, and Deep Learning courses at NTNU. He also engages in public outreach, such as a 2015 TV appearance on *Schrødingers katt: Kunstig Intelligens*. His research spans predictive neural mechanisms, evolutionary computation, and the societal implications of AI, as seen in his 2014 feature article in *Aftenposten*. Key publications include foundational work on the Baldwin Effect (2004), neural predictive models (2013), and computational neuroscience (2009). He actively promotes interdisciplinary approaches to AI, blending technical innovation with biological plausibility.

Ines Heilandمشاهده پروفایل

استاد

• Systems Biology
• Metabolic Modeling
• Bioinformatics
• +۹ مورد دیگر

Ines Heiland is a Professor of Molecular Biology and Bioinformatics at the Department of Arctic and Marine Biology, UiT The Arctic University of Norway. She leads research in systems biology with a focus on metabolic modeling, particularly of NAD and tryptophan pathways, and their implications in aging and disease. She is Chair of the board of the National Research School for Bioinformatics, Biostatistics and Systems Biology (NORBIS), and teaches courses in bioinformatics, systems biology, and scientific programming. Research Interests: Her work lies at the intersection of computational biology and biochemistry. She develops dynamic models to simulate metabolic networks, especially those involving NAD homeostasis, tryptophan metabolism, circadian rhythms, and metabolic signaling. Her research aims to uncover mechanisms underlying aging and disease, guiding therapeutic development through predictive modeling. She emphasizes close collaboration with experimentalists to validate model predictions. Publication Trends: Her recent publications reflect a strong focus on NAD metabolism, systems modeling, and bioinformatics tool development. Key themes include genome-scale metabolic modeling, stable isotope labeling, epigenetic-metabolic crosstalk, and evolutionary analysis of metabolic pathways. Her work appears in high-impact journals such as Nature Metabolism , Cell , and PNAS . Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While specific advisees are not listed, she has mentored junior researchers and collaborated widely. She leads or contributes to multiple research projects, including those on metabolic biomarkers, protein-protein interactions, and stable isotope approaches. She has led modeling components in collaborative grants, particularly those involving experimental validation of metabolic hypotheses. Labs and Teams: She is a member of the Bioinformatics and Systems Biology research group at UiT and leads modeling efforts within collaborative projects. She is actively involved in NORBIS, supporting national training in bioinformatics and systems biology.

• Digital Twins
• Complex System Simulation
• Virtual Reality
• +۸ مورد دیگر

Saleh Abdel-Afou Alaliyat is an Associate Professor in Computer Science at the Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), Faculty of Information Technology and Electrical Engineering. He leads the Sustainable Digital Transformation (SDT) R&D Lab. Research Interests: Dr. Alaliyat's work focuses on Digital twin architecture, simulation, and visualization in offshore energy and urban sustainability Artificial intelligence, particularly deep learning, swarm intelligence, and optimization algorithms Virtual worlds, 3D environments, and human-computer interaction Agent-based modeling for environmental and aquaculture systems Computer vision and information security applications Publications: His recent work (2022-2024) emphasizes predictive digital twins for wind energy, smart city applications, computer vision for litter detection, and AI-driven curriculum development. Earlier works (2013-2019) explore agent-based modeling for disease dynamics in aquaculture and swarm optimization algorithms. Teaching: He teaches courses in Game Development, Information Security, Computer Graphics, and Swarm Intelligence at both bachelor's and master's levels. Outreach: He has delivered lectures at international conferences like ECMS 2022 and IEEE ICMSAO 2019.

• Quantum Computing
• Statistical Physics
• Stochastic Processes
• +۳ مورد دیگر

Sergiy Denysov serves as Professor in the Department of Information Technology at Oslo Metropolitan University's Faculty of Technology, Art and Design. He is an active member of the Mathematical Modeling research group, focusing on the intersection of computational physics and quantum information systems. His research spans: Quantum Computing and Noisy Intermediate-Scale Quantum (NISQ) devices Statistical mechanics of complex systems Stochastic processes including Lévy flights and Markovian dynamics Spectral theory of random operators Machine learning applications in physical modeling Mathematical foundations of quantum information Analysis of his 2021-2025 publications reveals strong emphasis on quantum-classical algorithm synergies, spectral properties of quantum operations, and anomalous diffusion phenomena. His work consistently bridges theoretical physics with computational implementations, particularly through Python-based tools for stochastic process modeling. Denysov actively contributes to the Mathematical Modeling research group, which develops analytical and computational frameworks for physical systems across quantum information, statistical mechanics, and complex dynamics domains.

Hege Elisabeth Giercksky Russnesمشاهده پروفایل

استاد

• Breast Cancer
• Molecular Oncology
• Precision Medicine
• +۵ مورد دیگر

Hege Elisabeth Giercksky Russnes is a Professor at the Department of Pathology, Faculty of Medicine, University of Oslo . Her research focuses on breast cancer , genomic instability , immune microenvironment characterization , and precision oncology implementation . She leads translational studies integrating molecular profiling with clinical outcomes , particularly in early-stage and metastatic breast cancer . Key research themes: tumor heterogeneity, immune checkpoint dynamics, and computational pathology Active in precision medicine initiatives including European collaborations (PCM4EU, PRIME-ROSE) Developed clinical guidelines for genomic test reporting (ESMO 2024) Her recent work emphasizes multi-omics subtyping for prognostication, circulating tumor cell analysis in immunotherapy trials, and automated digital pathology using deep learning. She collaborates extensively across immuno-oncology , computational modeling , and health economics frameworks.

Vegard Heimly Brunمشاهده پروفایل

دانشیار

• Endocrine Surgery
• Thyroid Surgery
• Parathyroid Disorders
• +۵ مورد دیگر

Vegard Heimly Brun serves as an Associate Professor at UiT The Arctic University of Norway, actively engaged with the Breast and endocrine surgery research group. He concurrently participates in the Gastrosurgery and Neurobiology research groups while contributing to the LevDOSE project focused on optimizing thyroid hormone replacement therapy. His research spans two distinct domains: contemporary endocrine surgery and foundational neuroscience. In surgical research, he pioneers advanced imaging techniques for parathyroid localization, develops patient-specific levothyroxine dosing algorithms, and investigates decision-making protocols for thyroidectomies. His neuroscience work explores growth hormone's modulation of hippocampal spatial memory, dendritic spine plasticity, and fear conditioning mechanisms. This dual expertise bridges clinical innovation with fundamental neurobiological inquiry. Publication analysis (2015-2025) reveals an evolutionary trajectory from neuroscience-focused investigations (2015-2019) toward clinically applied endocrine surgery research (2021-2025). Recent work emphasizes surgical innovation in thyroid/parathyroid disorders through pharmacokinetic modeling and multimodal imaging, while earlier contributions established critical links between hippocampal growth hormone signaling and memory processes. This progression demonstrates translational research spanning basic science to surgical practice. No scientific awards were documented in the source materials. While no student advising relationships or specific grant funding details were disclosed, his collaborative publications across medical and neuroscience disciplines indicate extensive interdisciplinary engagement. The LevDOSE project represents a significant clinical research initiative in personalized endocrine therapy. Dr. Brun operates within UiT's multidisciplinary research ecosystem through three primary groups: the Breast and endocrine surgery research group (focusing on surgical oncology and endocrine disorders), the Gastrosurgery group (addressing gastrointestinal malignancies), and the Neurobiology group (investigating fundamental memory mechanisms). His laboratory work integrates clinical surgical practice with basic neuroscience methodologies, particularly in hippocampal function and hormone-brain interactions.

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۵ مورد دیگر

Gustavo Borges Moreno e Mello is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. Based in office SG209 at Stensberggata 29, Oslo, he actively contributes to the Applied Artificial Intelligence research group with focus on cutting-edge computational methodologies. His research spans artificial intelligence with emphases on graph neural networks, multilingual NLP, reinforcement learning, and medical imaging. Key interests include developing propagation schemes for GNNs, cross-lingual BERT adaptations, rodent fMRI analysis, and empathy assessment in human-computer interaction systems. His work bridges theoretical AI with practical healthcare and communication applications. Analysis of his 2020-2024 publications reveals strong thematic continuity in neural architecture design, particularly HITS-GNN variants for graph analytics and time-representation models. Recent work increasingly integrates neuroscience principles with reinforcement learning while maintaining Nordic language processing applications, notably in English-Norwegian BERT alignment. No information regarding graduate student supervision or research grants was found in the provided materials. He maintains active collaboration within OsloMet's Applied Artificial Intelligence research group, focusing on interdisciplinary projects that connect computer vision, natural language processing, and neuro-inspired computing with real-world healthcare and communication challenges.

• Robotics
• Artificial Intelligence
• Evolutionary Computing
• +۱۲ مورد دیگر

Kyrre Harald Glette is a Professor in the Department for Informatics at the University of Oslo , affiliated with the Robotics and Intelligent Systems Research Group and the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion . His research focuses on the co-design of robot morphology and control using evolutionary and bio-inspired artificial intelligence methods. Research Interests: Robotics and Intelligent Systems Evolutionary Robotics and Co-Design of Morphology and Control Artificial Intelligence and Machine Learning Evolvable Hardware and Embodied Computation Computational Creativity and Music-AI Interaction Human-Robot Interaction and Swarm Robotics His recent publications (2023–2025) reflect a strong interdisciplinary trend, combining robotics, evolutionary computation, and music technology. Key themes include evolutionary design of modular robots , real-time adaptation in bio-inspired systems , interactive sonification , and AI-driven music generation . His work often involves the DyRET robotic platform, emphasizing real-world evolution and embodied learning. Scientific Contributions: Extensive publications in top venues like Frontiers in Robotics and AI , IEEE ICRA , GECCO , and Nature Machine Intelligence . Development of open-source robotic platforms and frameworks for evolutionary robotics. Interdisciplinary work bridging AI, neuroscience, and music. Advising and Projects: He supervises MSc students in evolutionary robotics, robot locomotion, and musical robot swarms. He is involved in several research projects including Predictive and Intuitive Robot Companion (PIRC) , Modeling and Robots , Multimodal Elderly Care Systems (MECS) , Musical Human-Machine Interaction , and Neurophysiological Mechanisms of Human Auditory Predictions (AudioPred) . Labs and Research Groups: He is a key member of the Robotics and Intelligent Systems (ROBIN) group, the RITMO Centre of Excellence , and the Creative Computing Hub Oslo (C2HO) . These groups support interdisciplinary research in AI, robotics, music, and human interaction.

Aliaksandr Hubinمشاهده پروفایل

دانشیار

• Statistics
• Artificial Intelligence
• Econometrics
• +۴ مورد دیگر

Aliaksandr Hubin is an Associate Professor at the University of Oslo's Department of Mathematics and Natural Sciences, affiliated with the Center for Computational Inference in Evolutionary Life Science (CELS) and Statistics and Data Science research groups. His career spans Bayesian methodology development, machine learning applications, and interdisciplinary research. PhD in Statistics (University of Oslo, 2018) MSc in Operations Research (Molde University College, 2014) Specialist degree in Economic Cybernetics (Belarusian State University, 2013) Research spans: Bayesian model selection and averaging Probabilistic machine learning Computational statistics Applications in genomics, clinical diagnostics, and aquaculture Weak supervision methods in NLP Medical decision support systems Recent publications focus on: Bayesian neural network compression Clinical score validation Outlier detection mechanisms Evolutionary optimization in inference Domain-informed deep learning Scientific recognition: NIMA 2014 award Multiple student research awards (2012-2013) Graybill poster competition second place (2017) Methodological contributions include: Genetically modified MCMC algorithms Adaptive simulated annealing EM Probabilistic framework development

• Software Engineering
• Digital Twins
• Healthcare IoT
• +۳ مورد دیگر

Hassan Sartaj serves as a Postdoctoral Fellow within the Department of Engineering Complex Software Systems at Simula Research Laboratory. His research bridges advanced software engineering methodologies with critical healthcare applications, focusing on medical device safety and reliability through innovative digital twin frameworks. His research profile centers on AI-driven software engineering for healthcare systems , with core expertise in digital twin creation , uncertainty-aware simulation , and LLM-enhanced testing . Key contributions include developing meta-learning approaches for medical device digital twins (MeDeT) and quantum extreme learning machines for practical software testing. His work consistently addresses real-world challenges in healthcare IoT, particularly in medicine dispensers and cancer registry systems, emphasizing safety-critical validation. Analysis of his 15 most recent publications reveals a dominant trend toward integrating foundation models with cyber-physical systems engineering . Over 70% of his 2024-2025 output explores LLMs for uncertainty identification in self-adaptive robotics, differential testing of medical rule engines, and environment simulation for digital twins. This reflects a strategic pivot toward leveraging generative AI for validating safety-critical healthcare software, with strong emphasis on practical DevOps implementation in evolving healthcare applications.

• Artificial Intelligence
• Computer Science
• Cellular Automata
• +۳ مورد دیگر

Tom Glover is an Assistant Professor in the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. His office is located at Stensberggata 29, 0170 Oslo (Office number: SG204) with contact information including mobile: +47 975 38 894 and office: +47 672 37 712. Dr. Glover's research focuses on the intersection of cellular automata, artificial intelligence, and complex systems. His work spans theoretical computer science, artificial life, and practical applications in reservoir computing and control systems. His research group is part of the Innovation, Digital Transformation and Sustainability Research groups within the Department of Computer Science. Analysis of his publications reveals a consistent focus on cellular automata as computational models, with particular emphasis on their application in reservoir computing, network dynamics, and self-organizing systems. His work demonstrates how simple computational rules can generate complex behaviors with applications across multiple domains including control systems and biologically inspired computing. As an active researcher, Glover has published in reputable venues including the International Journal of Parallel, Emergent and Distributed Systems, Complex Systems, and proceedings from the Artificial Life Conference series. His recent work (2023-2024) shows continued development in sensitivity analysis of cellular automata and network topologies.