جستجوی استاد راهنمای دانشگاهی

Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano

• Machine Learning
• Graph Neural Networks
• Time Series Analysis
• +۷ مورد دیگر

Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.

• Autonomous Systems
• Sensor Technologies
• Robotics
• +۳ مورد دیگر

Tønnes Nygaard is an Associate Professor at the Department of Technology Systems, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on evolutionary robotics, morphological adaptation, and embodied artificial intelligence. He leads projects like COCOMO (Co-evolution of Control and Morphologies) and works extensively with the DyRET (Dynamic Robot for Embodied Testing) platform. Key research interests include robot control systems, adaptive morphology design, and real-world implementation of evolutionary algorithms. His work bridges theoretical computer science with practical robotics applications, emphasizing hardware-software co-evolution and embodied cognition principles. Publications span topics like morphological adaptation in quadruped robots, semi-supervised learning for terrain classification, and overcoming convergence issues in multi-objective evolutionary algorithms. Nygaard collaborates internationally and contributes to both academic journals and conferences in robotics and AI. No scientific awards are explicitly listed, though his impactful contributions to real-world evolutionary robotics suggest potential recognition pending explicit mentions. Advising and grant activities are central to his role, though specific student names or grant amounts are not detailed in the provided texts. Labs/Teams: Core contributor to the DyRET project and affiliated with the Section for Autonomous Systems and Sensor Technologies at UiO.

• Robotics
• Evolutionary Algorithms
• Morphological Adaptation
• +۳ مورد دیگر

Andreas Carlson is a Professor at the University of Oslo's Department of Mechanics. His research focuses on fluid dynamics, interfacial flows, and biophysics, with particular emphasis on elastohydrodynamics, membrane adhesion, and numerical modeling. He holds a PhD from the Royal Institute of Technology (2012) and was a Post-Doctoral Fellow at Harvard University (2012-2015). Key awards include the Silver Medal ERCOFTAC Leonardo Da Vinci competition (2012) and the Sigvardt Eklund's Award (2008). His work spans academic staff roles and active participation in projects like LUBRIBOT (sustainable soft robotics) and DyWeSS (dynamic wetting on soft materials). Carlson's research explores fluid dynamics in complex systems, including droplet behavior, soft matter physics, and biomembrane mechanics. His recent studies investigate phenomena like viscoelastic film spreading, fiber-based droplet control, and biomembrane stability. He contributes to courses such as MEK3230 - Fluid Mechanics and collaborates internationally on projects involving fluid-structure interactions and environmental engineering. Education: PhD in Mechanics, Royal Institute of Technology (2007-2012) Post-Doctoral Fellow at Harvard University (2012-2015) Key Projects: A sustainable soft robot (LUBRIBOT) Dynamic wetting on soft solids (DyWeSS) Nano- and micro-poro-elastic fog nets Research Groups: Interface Dynamics in Geophysical Flows (EarthFlows) Mechanics

• Fluid Dynamics
• Interfacial Flows
• Low Reynolds Flows
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Stefano Nichele is a Professor at the Department of Computer Science and Communication, Østfold University College, Norway. He holds additional roles as Professor II at OsloMet and has served in leading academic positions since 2014. His research focuses on Artificial Life (ALife), Neuro-Inspired AI, and Machine Learning, with a particular emphasis on cellular automata, reservoir computing, and neuro-inspired substrates. Nichele co-directs the Østfold AI (ØAI) hub and is an active member of IEEE, ELLIS, and the Norwegian AI Research Consortium (NORA). He earned his PhD in Computer Science from NTNU (2015) and completed his MSc at the University of Insubria, Italy. His work bridges computational systems and biological substrates, exploring criticality in neural networks and quantum-evolutionary algorithm interactions. He has received prestigious awards, including the Young Research Talent grant (2019) and the Distinguished Early-Career Investigator award (2024). Nichele’s research spans theoretical and applied domains, with over 50 publications on cellular automata dynamics, neuro-inspired robotics, and AI ethics. His recent projects include studying in vitro neural networks for computational capacity assessment and developing frameworks for body-brain co-evolution in soft robotics. Education: PhD in Computer Science, NTNU (2015) MSc in Computer Science, University of Insubria (2009) Awards: Young Research Talent grant (2019) Distinguished Early-Career Investigator (2024) Grants & Roles: Co-director of the Østfold AI hub Board member of NORA (Norwegian AI Research Consortium) Labs & Collaborations: Focus on neuro-inspired AI systems and unconventional computing Partnerships with institutions like Simula Metropolitan and the International Society for Artificial Life (ISAL)

• Artificial Life
• Neuro-Inspired Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Ørjan Grøttem Martinsen is a Professor of Electronics at the Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo. He also holds a temporary research position at the Medical Technology Business Area of Oslo University Hospital. With over three decades of experience, he has established himself as a leading expert in bioimpedance research and applications. Education: High-voltage engineer degree (1983) Cand. scient. in electronics/measurement technology (1990) Dr. scient. with thesis on skin's electrical properties (1995) Professor Martinsen's research centers on bioimpedance—the passive electrical properties of biological tissues that vary with anatomy and physiology. His work spans diverse applications including medical diagnostics (skin cancer detection), food quality assessment (fresh vs. thawed fish), skin condition monitoring (moisture levels), and stress level evaluation. His research bridges physics, engineering, and medical applications, creating practical diagnostic tools from fundamental electrical principles. He has pioneered methods to characterize tissue properties through impedance measurements, with particular focus on electrodermal activity and skin impedance. His recent publications (2022-2025) demonstrate a strong interdisciplinary approach combining bioimpedance with machine learning, robotics, and advanced signal processing. The work spans from fundamental biophysics (GABA detection, tissue characterization) to practical applications (dental anxiety assessment, ADHD treatment evaluation). Key trends include integration of AI with bioimpedance measurements, development of novel sensor systems, and expansion into new application areas like optogenetics and micro-robotics. Awards and Recognition: IEEE Senior Member (2006) CLABIO Award (2012) Fellow at Institute of Physics (FInstP) (2015) Dr. Honoris Causa, Tallinn University of Technology (2018) UiO Innovation Award (2019) Member of Norwegian Academy of Technical Sciences (2021) Professor Martinsen has served as Editor-in-Chief of the Journal of Electrical Bioimpedance since 2010 and was President of the International Society for Electrical Bioimpedance (2010-2016). His research has attracted significant funding, enabling collaborations across engineering, medical, and biological disciplines. He has supervised numerous students and researchers in the Bioimpedance Group at UiO, fostering a strong research environment that bridges theoretical and applied work. His work is conducted primarily through the Oslo Bioimpedance Group and Sensorama SmartSense research teams, which focus on developing innovative measurement techniques and applications of bioimpedance technology. These groups maintain strong collaborations with medical institutions and industry partners to translate research findings into practical healthcare solutions.

• Bioimpedance
• Biomedical Physics
• Electronics and Instrumentation
• +۳ مورد دیگر

Kristin Bergaust serves as Professor at Oslo Metropolitan University within the Faculty of Technology, Art and Design's Department of Aesthetic Subjects, specializing in Drama and Theater Communication. Appointed in 2008, she previously held a part-time professorship at the University of Agder's Faculty of Art from 2015-2017. Her academic foundation includes: Visual Arts education from the State Academy of Fine Arts Cand.mag degree in Literature and Language Bergaust's research critically examines art-technology intersections through ecological and social lenses. She pioneers work in digital media, video, and sound within media art, contemporary art, and relational aesthetics frameworks. Her focus on environmental sustainability manifests in projects like "Oslofjord Ecologies" and "FeLT," which investigate living technologies and fjord ecosystems through artistic research methodologies. This work bridges creative practice with urgent socio-ecological concerns. Analysis of her 16 scientific publications reveals consistent interdisciplinary exploration across art-science-technology domains. Key trends include bioart applications, unconventional computing approaches, and feminist perspectives on sustainable tech development. Her publications demonstrate growing emphasis on ecological crisis response through artistic intervention, particularly in marine environments and swarm robotics applications. Bergaust founded and directed Atelier Nord media lab for artists in Oslo, establishing critical infrastructure for media art since the 1990s. She currently leads the "FeLT- Futures of Living Technologies" project investigating human-organism-machine interactions, while actively participating in research groups including "Art – Practice – Research" and "Creative Practice – Interventions in Space, Place and Environment." Her work extends to major platforms like the Oslofjord Triennale and EU Green Week initiatives.

• Art and Technology
• Ecological Art
• Socially Engaged Art
• +۶ مورد دیگر

Jorge Santos is Professor of Fishery Biology at The Norwegian College of Fishery Science, UiT The Arctic University of Norway, Tromsø. His work integrates fisheries ecology, socio-ecological systems, and innovative art-science approaches to address coastal and marine challenges. Research Focus: Fisheries ecology and bio-geography, trophic networks, effects of fishing, coasts and estuaries, small-scale fisheries, fisheries management, coastal culture, futures of coasts and fisheries, Blue Economy, education and outreach, and quantitative methods. Key Trends: Recent work emphasizes climate change impacts on marine communities, rotational harvesting effects, geographic life-history variations, and interdisciplinary art-science collaborations for ocean communication. His publications reveal a strong shift toward integrating ecological science with cultural narratives and future scenario planning. Scientific Recognition: No formal awards listed in provided materials. Advising: Supervises bachelor, master, and PhD students across biological and social dimensions of marine systems. Grants & Projects: Coordinates NorLanka Blue (marine sciences/blue economy in Sri Lanka), SAMAKI (MSc/PhD school in Tanzania/Zanzibar), and Blue Route (global PhD education). Serves on MASMA Programme Committee and leads consultancies for fisheries/education development. Active in CRAFT research group (Center for Arctic Humanities) and projects AFO-JIGG (small-scale fishing welfare) and FUTURES4Fish (coastal futures), Santos pioneers transdisciplinary approaches bridging science, art, and community engagement.

• Fisheries ecology and bio-geography
• Trophic networks and life-histories
• Effects of fishing
• +۸ مورد دیگر

Geir Johnsen is a Professor at the Department of Biology, Norwegian University of Science and Technology (NTNU), with a part-time research position (Prof II) at the University Centre on Svalbard (UNIS). He is also a co-founder of the NTNU spin-off company Ecotone, which develops optical techniques for marine environmental mapping. Currently, he is on a one-year research stay at the University of Hawaii at Manoa, focusing on spectral bioluminescence in marine taxa and coral reef monitoring. University: Norwegian University of Science and Technology (NTNU) Department: Department of Biology Additional Roles: Prof II at UNIS, Co-founder of Ecotone Research Areas: Marine ecology, bio-optics, photosynthesis, pigment chemotaxonomy, underwater robotics, and sensor development for in situ identification of marine biogeochemical objects. His work integrates autonomous systems for Arctic polar night studies, coral reef mapping, and microplastics analysis. Scientific Output: Over 130 peer-reviewed papers, book co-editorships, and leadership in the Centre of Excellence (AMOS) for autonomous marine systems. Key 2025-2024 publications address climate change in the Arctic, hyperspectral marine mapping, microplastics detection, and photophysiological studies in polar regions. Advising: 3 MSc and 5 PhD students (total 47 MSc and 17 PhD advisees) Outreach: Public articles on deep-sea mining ethics, light pollution in the Arctic, and marine robotics.

• Marine Ecology
• Biodiversity
• Bio-optics
• +۵ مورد دیگر

Sidney Pontes-Filho is a Postdoctoral Fellow at Simula Research Laboratory in Oslo, Norway, with a strong academic affiliation to the Norwegian University of Science and Technology (NTNU) where he completed his PhD in Computer Science. His primary departmental affiliation is with the Department of Computer Science at Oslo Metropolitan University (OsloMet). His research spans multiple institutions including Simula Research Laboratory where he works in the Numerical Analysis and Scientific Computing department. His educational background includes a B.Sc. in Computer Science from Federal University of Paraíba, Brazil (2013), an M.Sc. in Computer Science from Technical University of Kaiserslautern, Germany (2018), and a Ph.D. in Computer Science from NTNU. His research focuses on the intersection of complex systems, unconventional computing, computational neuroscience, and artificial general intelligence, with particular emphasis on neural cellular automata and criticality phenomena. Pontes-Filho's publication record reveals a consistent trajectory in exploring how critical systems can serve as substrates for intelligent behavior. His work demonstrates how neural cellular automata operating near critical points exhibit emergent properties relevant to artificial intelligence, including attention mechanisms, scalability, and robust control systems. His research spans theoretical frameworks like EvoDynamic, practical applications in soft robotics control, and medical imaging tools for brain extraction from fMRI data. His scientific contributions show an interdisciplinary approach merging concepts from statistical physics, neuroscience, and computer science to develop novel computational paradigms. The evolution of his work demonstrates increasing sophistication in connecting theoretical criticality concepts with practical AI applications, particularly in embodied systems and neuromorphic computing.

• Complex Systems
• Unconventional Computing
• Computational Neuroscience
• +۳ مورد دیگر

Evi Zouganeli is a Professor at the Department of Mechanical, Electrical and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet). She leads the Automation, Robotics, and Intelligent Systems (ARIS) research group and serves as a board member of the Norwegian Artificial Intelligence Society (NAIS). Her work bridges academic research and industrial applications in intelligent systems. Research Focus: Zouganeli specializes in Machine Learning and Computer Vision applications for Cognitive Robotics, with emphasis on assistive technologies, smart cities, and smart industry. Her research explores neuromorphic computing, sensor network optimization, and multi-modal AI integration. Publications Trends: Recent works focus on AI-enabled robotics, sensor placement optimization in smart homes, and neuromorphic reservoir networks. Earlier contributions include European collaborative research in photonic technologies and broadband networks. Additional Contributions: She actively participates in public discourse about AI implementation challenges and Norway’s AI development potential, engaging with industry leaders and policymakers.

• Machine Learning
• Computer Vision
• Intelligent Systems
• +۷ 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.

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

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

Olga Korostynska is a Professor at Oslo Metropolitan University within the Faculty of Technology, Art and Design, Department of Mechanical, Electrical and Chemical Engineering. Her research spans digital health, wearable sensors, and robotics, with a focus on AI-integrated medical devices and environmental monitoring systems. Digital Health Technology Implementation Wearable Sensor Development Biomedical and Environmental Applications Recent publications highlight her work on microwave sensors for blood lactate monitoring, robotic meat processing platforms, and gamified rehabilitation devices. Her projects often bridge interdisciplinary domains, including EQUALS-EU initiatives for women in technology and AI-driven healthcare solutions. Korostynska contributes to sensor technology conferences and collaborates with teams like ADEPT (Advanced Health Intelligence and Brain-Inspired Technologies) to advance non-invasive diagnostics and rehabilitation tools.

• Digital Health
• Wearable Sensors
• Artificial Intelligence
• +۶ مورد دیگر

Asieh Abolpour Mofrad is a researcher at the Department of Informatics, University of Bergen (UiB), with a dual PhD background in behavioral sciences and informatics. She is currently engaged in the Retail Fresh project, leveraging machine learning to reduce food waste in the retail sector in collaboration with Link Retail. Previously, she contributed to the DRONE (Drug Repurposing for Neurological Diseases) project at the Department of Global Health and Public Health, UiB, where she applied machine learning to Norwegian health registry data to investigate Parkinson's disease treatments. Her academic credentials include two doctoral theses: one from OsloMet (2021) on the integration of behavior analysis and machine learning for modeling stimulus equivalence, and another from UiB (2021) on clique-based neural associative memories. Her research bridges artificial intelligence, cognitive modeling, neural networks, and public health applications. Her research interests span machine learning, neural associative memory, reinforcement learning, cognitive modeling, health informatics, and drug repurposing. She employs computational frameworks such as projective simulation and tournament-based neural networks to model complex cognitive and biological phenomena. The analysis of her recent publications reveals a consistent focus on machine learning applications in both cognitive science and healthcare. Her work includes modeling stimulus equivalence, designing neural memory architectures, solving stochastic optimization problems, and analyzing large-scale health data for neurological disease risk. These efforts reflect interdisciplinary innovation, combining theoretical computer science with real-world applications in psychology and medicine. Scientific Contributions: Developed the Enhanced Equivalence Projective Simulation (E-EPS) framework for modeling derived relations in behavior analysis. Designed tournament-based neural networks for efficient sequence storage and bidirectional retrieval. Applied machine learning to large-scale health registries to identify drug classes associated with Parkinson’s disease risk. Contributed to adaptive learning systems based on flow theory for educational optimization. She has advised no publicly listed students but collaborates extensively with researchers across institutions. Her work is supported by interdisciplinary research projects and she actively disseminates code via GitHub, particularly in Jupyter notebooks for reproducibility. She is affiliated with research teams in informatics and global health at UiB, contributing to both theoretical and applied machine learning initiatives. Laboratories and Research Teams: Department of Informatics, University of Bergen – Machine Learning and Neural Systems group. DRONE Project – Interdisciplinary team on drug repurposing using AI and health data. Retail Fresh Project – AI for sustainable retail, in collaboration with industry partners.

• Machine Learning
• Neural Networks
• Associative Memory
• +۷ مورد دیگر