جستجوی استاد راهنمای دانشگاهی

Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Swati Aggarwal is a Professor in Artificial Intelligence at the Faculty of Logistics, Molde University College (HiMolde). Her research focuses on AI applications in healthcare, ethics, cognitive development, and neural networks. She holds a PhD in Neutrosophic Neural Networks, a Master's in Information Technology, and a Bachelor's in Computer Science and Engineering. Previously, she was a Marie Curie Postdoc Fellow at NTNU, working on AI models for cognitive assessment in infants (AIM_COACH project). Research Interests - AI in Health/Medicine - Ethics in AI and Societal Impact - EEG/BCI for Cognitive Assessment - Machine Learning and Deep Learning Publications Her recent work spans AI ethics, BCI applications, adversarial attacks, and healthcare diagnostics. Notable contributions include EEG-based infant perceptual monitoring (2025) and malaria detection via EfficientNet (2023). She also explores cross-lingual adversarial robustness and blockchain in hospitality systems. Labs/Teams - ABC-AI: Applied, Basic, and Conscientious AI Group - Virtual Technologies and Learning Research Group

• Artificial Intelligence
• AI in Health and Medicine
• AI in Society
• +۶ مورد دیگر

Marta Molinas is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). Her research spans multiple interdisciplinary domains with a focus on EEG technology and brain-computer interfaces. She actively supervises numerous Master's projects and maintains extensive international collaborations with institutions including Kavli Institute for Systems Neuroscience, RIKEN Center for Brain Science, University of Tsukuba, Juntendo University, and several European universities. Professor Molinas' research interests center on developing innovative EEG technologies, particularly her FlexEEG concept for reduced-channel EEG systems with brain imaging capabilities. Her work integrates signal processing, artificial intelligence, and neuroscience to create practical applications in mental health, sleep research, neurorehabilitation, and human-computer interaction. She specializes in EEG source imaging, machine learning for brain signal analysis, and the development of brain-computer interfaces for various applications including locked-in syndrome communication, ADHD treatment, and driver monitoring systems. Her publication portfolio demonstrates strong trends in interdisciplinary research combining neuroscience with electrical engineering and artificial intelligence. The work shows particular emphasis on developing practical EEG-based systems that minimize invasiveness while maintaining analytical power, with applications spanning healthcare, rehabilitation, and human augmentation. Her research bridges theoretical signal processing with real-world implementations through numerous student projects and international collaborations. Professor Molinas actively supervises a large team of Master's and PhD students across multiple projects, with each project typically requiring two students working collaboratively. Her research is supported through numerous international collaborations with institutions in Japan, India, and Europe, indicating substantial research funding and project leadership. She has developed a pipeline of student projects that build upon previous work, creating a cumulative knowledge base within her research group. She leads the EEG ITK research team at NTNU, which focuses on developing the FlexEEG headset prototype featuring flexible, wireless, dry electrodes designed to move across the scalp. This team works at the intersection of neuroscience, electrical engineering, and computer science, developing applications for sleep research, mental health monitoring, neurorehabilitation, and brain-computer interfaces. The team collaborates extensively with international partners including the Kavli Institute for Systems Neuroscience, the International Institute of Integrative Sleep Medicine at University of Tsukuba, and several engineering departments across Europe and Asia.

• EEG Signal Processing
• Brain-Computer Interfaces
• Artificial Intelligence
• +۵ مورد دیگر

Jianhua Zhang is a Professor of Computer Science and founding deputy head of the AI Lab at the Department of Computer Science, OsloMet - Oslo Metropolitan University, Norway. He holds affiliations with the Faculty of Technology, Art and Design. His career includes roles as Scientific Director at Vekia (France), Head of Machine Learning Lab, and Professorships at East China University of Science and Technology and Beijing University of Technology. He has held visiting positions at TU Berlin, TU Dresden, and the University of Catania. Educations: PhD in Electrical Engineering and Information Sciences (Ruhr University Bochum, 2005), Postdoctoral Research at the University of Sheffield (2005-2006). Research focuses on artificial intelligence, computational intelligence, cognitive human-machine systems, neuroergonomics, affective computing, and AI-driven neuroergonomics. Applications span engineering, biomedicine, finance, and business. He has led over 20 large-scale projects and published extensively (4 books, 13 chapters, ~200 papers). Leadership roles include Chair of IFAC Technical Committee on Human-Machine Systems (2017-2023), Vice Chair of IEEE Norway Section, and editorial roles at journals like Frontiers in Neuroscience and Cognitive Neurodynamics . He organized major conferences like IFAC HMS2025 (Beijing) and ICMLT 2024 (Oslo). Awards: Stanford/Elsevier Top 2% Scientists (2023/2024), Senior Research Fellowship (CSC, 2012), Max Planck Fellowship (2011), Shanghai Pujiang Talent (2007), DAAD Scholarship (2002-2004). Grants and advising: PI for 20+ projects, advising PhD students in AI, machine learning, and control systems. Teaching includes courses on computational intelligence, IoT, and fuzzy systems at both undergraduate and graduate levels. Labs/Teams: AI Lab at OsloMet, Machine Learning Lab (Vekia), and collaborations with institutions globally. Current work emphasizes AI ethics, neuroergonomics in smart cities, and adaptive human-machine systems.

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +۱۱ مورد دیگر

Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.

• Artificial Intelligence
• Responsible AI
• AI Ethics
• +۱۳ مورد دیگر

Efthymios Papatzikis is a Professor of Infant Brain Development at Oslo Metropolitan University’s Faculty of Education and International Studies. He leads the Advanced Health Intelligence and Brain-Inspired Technologies (ADEPT) Research Group, focusing on brain development in the first 1500 days of life using neuroimaging, behavioral analysis, and AI-driven tools. Research Focus: Multimodal neuroimaging (qEEG, ABR, aEEG), computational neuroscience, AI in NICU diagnostics, and personalized sound/music interventions. Collaborations: Harvard University, Martinos Center for Biomedical Imaging, UCL, and Bergen University. Editorial Roles: Associate Editor for Frontiers in Pediatric Psychology, Guest Editor for Frontiers in Pediatric Neurology. His work bridges neuroscience with clinical neonatal care, emphasizing family-centered medicine and precision diagnostics. Recent projects include EU Cost Action CA22111 on real-world environments’ impact on brain development. Scientific Awards: Fellow of the Higher Education Academy (FHEA), UK Certified Specialist in Social Prescribing by the World Health Organization He contributes to journals as reviewer and editor, co-develops computational tools for NICU EEG analysis, and advises international foundations on maternal/child health and early childhood education.

• Neuroscience
• Neonatology
• Developmental Psychology
• +۷ مورد دیگر

Anis Yazidi is a Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, Medical Technology, and IoT Security, with a particular emphasis on Applications of AI in Healthcare, EEG Signal Processing, and Digital Transformation. Active research projects include AI Mind (dementia diagnostics), Glycopathology in dry eyes, and Pain and mental distress analysis Completed projects: Digital hate speech analysis, AI in reproductive technology, Nano-antibiotics development His recent publications (2023-2025) demonstrate expertise in: Tsetlin Automaton algorithms for concept learning EEG classification using visibility graphs and vision transformers AI ethics frameworks for medical practice Deepfake detection methodologies Collaborative work spans institutions in Norway, Czech Republic, and international AI research communities.

• Artificial Intelligence
• Machine Learning
• Medical Technology
• +۷ مورد دیگر

Anne Danielsen is a Professor and Deputy Head at the RITMO Center for Interdisciplinary Studies of Rhythm, Time, and Motion at the University of Oslo. Her research bridges musicology, cognitive science, and interdisciplinary studies, focusing on rhythm, music production, and the intersection of music, media, and technology. University: University of Oslo Role: Professor Projects: TIME (NFR TOP RESEARCH), RITMO (NFR Center of Excellence) Danielsen’s work explores microrhythm , groove , and temporal perception in music, with a particular emphasis on popular and African-American music . She investigates how music cognition interacts with sonic features and body posture in performance, integrating psychological and acoustic analysis . Her recent projects include MusicLab Copenhagen , a dataset for interdisciplinary concert research, and studies on beta oscillations and pupil responses in groove perception. Danielsen collaborates across disciplines, examining how personality traits and genetic factors influence musical sensibility through twin studies. In music production , she analyzes how digital audio workstations transform rhythmic structures and investigates acoustic chamber design for sound experiments. Her research also addresses gender patterns in music mediation and the cultural implications of rhythmic aesthetics.

• Popular Music
• African-American Music
• Rhythm and Sound Analysis
• +۵ مورد دیگر

Andres Soler is a Lecturer at NTNU's Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering. His research focuses on EEG signal processing for applications in brain-computer interfaces (BCI), stress/health monitoring, and low-density electrode systems. He has published extensively on topics including EEG source imaging, artifact removal, and optimized channel selection techniques. His work bridges biomedical engineering and machine learning, with notable contributions to driver alcohol detection systems and motor imagery classification for neurorehabilitation. Teaching roles include serving as Guest Lecturer for Biomedical Instrumentation and Control (TTK4270) and Adaptive Data Analysis (TTK7), while acting as main lecturer for Industrial Electrotechnics (TTK4240). His research group collaborates internationally on projects like FlexEEG and has presented at conferences such as IEEE EMBC and Brain Informatics. Key research directions include advancing EEG-based systems for clinical and automotive applications, developing algorithms for real-time brain activity decoding, and optimizing EEG hardware configurations for cost-effective implementations. Current trends show focus on enhancing signal quality through artifact mitigation strategies and improving BCI communication systems for locked-in patients.

• Biomedical Engineering
• Neuroscience
• Signal Processing
• +۳ مورد دیگر

Lars Magne Lundheim is a Professor at the Department of Electronic Systems at Norwegian University of Science and Technology (NTNU). He holds an MSc and PhD in electrical engineering from NTNU. His career includes roles as Associate Professor (2002–2010), Research Scientist at SINTEF, and part-time academic positions. He belongs to the Signal Processing Group and focuses on signal processing for radar systems, power consumption optimization, and engineering education. His research spans technical domains like multicarrier communication systems and pedagogical innovations such as project-based learning and curriculum design. Notable contributions include work on sustainability integration in STEM education and competency development through active learning strategies. Lundheim is a Senior Member of IEEE and has reviewed for major journals/conferences including IEEE Transactions on Signal Processing. Recent publications emphasize educational frameworks linking mathematics with engineering practice, while earlier works address technical challenges in radar distortion correction and power-efficient multiplier design. He has contributed to 16 PhD examination committees and teaches courses like TTT4270 (Electronic System Design) and TTT4260 (Electronic System Design and Analysis). Lundheim’s outreach includes presenting at events like the Læringsfestivalen and collaborating on national initiatives like the 'Fremtidens teknologistudier' educational strategy report. His interdisciplinary work bridges technical innovation with pedagogical excellence in electrical engineering education.

• Signal Processing
• Engineering Education
• Frequency Domain Radars
• +۳ مورد دیگر

Pedro Lind is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, where he serves in the Department of Information Technology with a focus on Artificial Intelligence. His academic appointments include active participation in research groups for Applied Artificial Intelligence and Mathematical Modeling. Dr. Lind's research spans interdisciplinary domains including: Biomedical AI applications (EEG classification, ECG analysis, eye tracking) Stochastic processes and complex systems modeling Trustworthy machine learning for security/privacy Physics-inspired computational methods Renewable energy statistics and modeling His recent publications demonstrate strong focus on developing novel AI methodologies for medical diagnostics (2024-2025), particularly using generative models and interpretable AI approaches for physiological data analysis. He leads significant research initiatives including the AI-Mind project developing diagnostic tools for dementia. Additional projects include international technology transfer collaborations with Czech Republic institutions. Dr. Lind maintains active research teams and labs focused on computational neuroscience and applied AI.

• Artificial Intelligence
• Physics
• Computer Technology
• +۴ مورد دیگر

Hugo Lewi Hammer er professor ved Oslo Metropolitan University, tilhørende Faculty of Technology, Art and Design og Department of Information Technology – Mathematical Modeling . Hans forskning fokuserer på forbedring av pålitelighet og transparens i maskinlæring, forsterkende læring og dyb læringsmodeller gjennom metodikk innen modelltolkning, usikkerhetskvantifisering, robust statistikk og kausal inferens. Hans nylige arbeid inkluderer: AI-drevet optimering i assistert reproduksjonsteknologi (embryoutvalg og sædcelleanalyse) Medisinsk bildebehandling (polypdeteksjon, meibomkertutgang) Neural nettverkstolkning og usikkerhetsmodellering i EEG-analyse Biomekanisk prediksjon av muskelutmatting Hans publikasjoner viser mangfoldige anvendelser av AI i medisin og teknologi, med spesialvekt på: Explainable AI (XAI) i diagnostikk og behandling Usikkerhetskvantifisering i dyb læring Automatisering av medisinske prosedyrer (ICSI, embryoanalyse) Stokastisk simulering og kausal inferens Hammer er engasjert i forskningsgruppene Applied Artificial Intelligence og Mathematical Modeling og har publisert over 130 vitenskapelige artikler og 7 forskningsrapporter.

• Artificial Intelligence
• Machine Learning
• Bayesian Statistics
• +۶ مورد دیگر

Peyman Mirtaheri is a Professor at the Department of Mechanical, Electronics and Chemistry (MEK) under the Faculty of Technology, Art and Design at Oslo Metropolitan University (OsloMet). He also holds an Adjunct Professor position at the Faculty of Biomedical Engineering at Michigan Technological University, USA. His primary role involves teaching courses such as Medical Sensors and Actuators (ACIT4720) and the upcoming BioInspired Systems (fall 2025). Research Interests: Professor Mirtaheri leads the ADEPT lab (ADvanced hEalth intelligence and brain-insPired Technologies) and ADEPT_technology, focusing on: Developing measurement techniques for brain activity detection and analysis Applying fNIRS (functional near-infrared spectroscopy) and EEG (electroencephalography) to study movement, balance, and cerebral cortex activity Advancing bio-inspired systems and health technology Key Projects: His active research includes Use of 3D printers in future rehabilitation (knowledge-based and patient-centered solutions) and completed work on Brain activity during walking and balance . Labs & Collaborations: He integrates his optical/NIRS laboratory into OsloMet's ADEPT lab, collaborating with institutions like Michigan Technological University and contributing to global conferences such as the IEEE International Symposium on Circuits and Systems and the ISPO World Congress .

• Medical Technology
• Biomedical Engineering
• Optics
• +۵ مورد دیگر

Gaute Einevoll is a Professor in the Department of Physics at the Norwegian University of Life Sciences (NMBU), Faculty of Science and Technology. His academic work bridges the disciplines of physics and neuroscience, with a strong focus on computational and mathematical modeling of brain function. Position: Professor of Physics Institution: Norwegian University of Life Sciences (NMBU) Department: Department of Physics Research Focus: Brain Physics, Computational Neuroscience His research interests lie at the intersection of physics and neuroscience, particularly in understanding the biophysical mechanisms underlying neural signals such as local field potentials (LFPs), EEG, and spike trains. He develops and applies mathematical models to interpret experimental data from multielectrode arrays and laminar recordings. His work includes modeling cortical circuits, thalamocortical networks, and the transition from grid cells to place cells in spatial navigation. He also investigates the biophysical basis of signal propagation and filtering in neural tissue. The 15 most recent publications reflect a consistent focus on computational modeling of neural activity, with strong emphasis on biophysical realism, data analysis methods, and tool development. Articles span topics from single-neuron contributions to EEG, astrocytic ion dynamics, and large-scale neural recordings. Keywords across these works include Computational Neuroscience, Biophysics, Neural Modeling, and Electrophysiology, with subfields covering LFPs, spike-train analysis, cortical circuits, and network dynamics. A recurring theme is the development of analytical and computational tools to bridge experimental data with theoretical understanding. No scientific awards are mentioned in the provided text. Gaute Einevoll has supervised research and taught courses such as FYS388/488 Computational Neuroscience and FYS102 Thermal Physics and Electromagnetism. He has contributed to major collaborative efforts like the Human Brain Project. While specific grants are not listed, his extensive publication record in high-impact journals suggests sustained research funding. He has also authored educational materials, including a textbook on physics and an edited volume on natural sciences. He is associated with research groups focused on computational neuroscience and brain physics. He has developed the LFPy tool for simulating extracellular potentials, indicating leadership in neuroinformatics tool development. His team likely involves interdisciplinary collaboration between physicists, neuroscientists, and computational biologists working on modeling neural systems.

• Computational Neuroscience
• Mathematical Neuroscience
• Brain Physics
• +۱۲ مورد دیگر

Tor Endestad is an Associate Professor in Cognitive Psychology at the University of Oslo, specializing in Cognitive Neuroscience and Brain Imaging Methodology. He leads the Endestad Brain Imaging Group (EBIG) and serves as Head of the fMRI Laboratory at the Center for the Study of Human Cognition. His research focuses on visual information processing, attention, memory control mechanisms, and motor responses to conflicting stimuli. International collaborations with University of Regensburg (Germany) and University of California, Berkeley (USA) Key research areas: source memory, constructive perception, neural oscillations, and music-brain interactions Recent publications span topics like auditory stream encoding, musical sensibility genetics, and neuroimaging techniques. His work integrates fMRI, EEG, and fNIRS methodologies across cognitive and clinical domains. EBIG members include postdoctoral fellows and researchers from psychology, medicine, computer science, and linguistics disciplines. The group participates in UiO:Life Sciences initiatives.

• Cognitive Psychology
• Cognitive Neuroscience
• Brain Imaging Methodology
• +۷ مورد دیگر