جستجوی استاد راهنمای دانشگاهی

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

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.

• Artificial Intelligence
• Machine Learning
• AI Alignment
• +۲ مورد دیگر

Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.

Swati Aggarwalمشاهده پروفایل

استاد

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

Anne H Schistad Solbergمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Image Analysis
• +۶ مورد دیگر

Anne H Schistad Solberg is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. She leads research in digital signal processing and image analysis, with a focus on machine learning applications across multiple domains. As co-director of SFI Visual Intelligence, she oversees research on interpretable deep learning models, uncertainty quantification, contextual learning, and self-supervised learning approaches. Her research spans medical imaging (particularly cardiovascular ultrasound), environmental monitoring using satellite imagery, and seabed mapping with sonar technology. Professor Solberg's work demonstrates a consistent trajectory from foundational signal processing techniques to cutting-edge deep learning applications. Her recent publications show increasing specialization in medical image analysis, particularly in echocardiography enhancement and cardiac structure segmentation, while maintaining strong contributions to remote sensing and geophysical applications. She teaches several popular courses including IN2070, IN3310, and IN5400 (Machine Learning for Image Analysis), which is noted as the most popular master's/PhD course on deep learning at the University of Oslo. Professor Solberg serves as principal investigator for the Intelligent Cardiovascular Ultrasound Scanner (INCUS) project, collaborating with GE Vingmed Ultrasound to develop AI-enhanced cardiac imaging systems that improve diagnostic accuracy and productivity in echocardiography. Co-director of SFI Visual Intelligence research center Principal Investigator for the INCUS project (Intelligent Cardiovascular Ultrasound Scanner) Member of the Digital Signal Processing and Image Analysis (DSB) research group Member of the Strategic Research Initiative: Multimodal Medical Imaging and Image Analysis (MEDIMA) Her research group develops algorithms that address real-world challenges in medical diagnostics and environmental monitoring, with a particular emphasis on making deep learning models more interpretable and reliable for critical applications. The INCUS project, funded through User-driven Research-based Innovation (BIA), aims to reduce the time wasted during cardiac ultrasound examinations by implementing intelligent algorithms that learn from expert users and historical data.

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

• Computational Physiology
• Cardiac Electrophysiology
• Atrial Fibrillation
• +۷ مورد دیگر

Molly Maleckar is a Research Professor at the Computational Physiology Department of Simula Research Laboratory , Oslo, Norway. Her work bridges computational modeling, cardiac electrophysiology, and biomedical applications, with a focus on arrhythmia mechanisms, fibrosis modeling, and machine learning integration in cardiac risk prediction. Research Interests include: Computational Cardiology Ion Channel Dynamics Machine Learning in Medicine Excitable Tissue Modeling Cardiac Fibrosis Analysis Biomedical Simulation Scientific Contributions span 15+ publications (2018-2024) addressing atrial fibrillation, calcium handling, and AI-driven ECG analysis. Key collaborative projects involve patient-specific ventricular modeling and educational initiatives like the Simula Summer School in Computational Physiology .

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

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

Trym Vegard Haavardsholmمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Robotics
• +۳ مورد دیگر

Trym Vegard Haavardsholm is a 20% part-time Lecturer at the University of Oslo (UiO) within the Section for Autonomous Systems and Sensor Technologies. He also serves as Principal Scientist at the Norwegian Defence Research Establishment (FFI) and is a PhD candidate at the Department of Engineering Cybernetics, NTNU. His research focuses on computer vision, machine learning, robotics, and image analysis, with a particular emphasis on multispectral imaging systems and unmanned aerial vehicles (UAVs). Haavardsholm has contributed to advancements in sensor technologies for tactical reconnaissance, autonomous navigation, and real-time data processing. His work spans applications in defense, environmental monitoring, and emergency response systems. Research Interests Haavardsholm’s research integrates interdisciplinary approaches to develop innovative sensor systems and algorithms. Key areas include compact multispectral imaging for small UAVs, in-operation camera calibration, and anomaly detection in hyperspectral data. His contributions emphasize practical applications such as urban feature classification, collaborative indoor navigation, and bioaerosol detection. His work bridges theoretical computer science with applied engineering, addressing challenges in autonomous systems and sensor fusion. Publications His publications highlight trends in multispectral sensor design, UAV imaging, and real-time georeferencing. Recent work includes compact sensor systems for tactical use and advancements in pushbroom image rectification. Earlier research explored band selection algorithms for target detection and GPU-accelerated anomaly detection. Professional Roles As a lecturer, Haavardsholm contributes to academic supervision and teaching in autonomous systems. His dual role at FFI and UiO reflects his commitment to translating academic research into practical defense and civilian applications. His PhD candidacy at NTNU underscores his ongoing academic engagement in engineering cybernetics.

• Machine Learning
• Information Theory
• Deep Learning
• +۶ مورد دیگر

Zhiyuan Wu is a Doctoral Research Fellow at the University of Oslo , affiliated with the Digital Signal Processing and Image Analysis research group under the Faculty of Mathematics and Natural Sciences . Education: Bachelor’s degree in Communication Systems and Information Technology from Lanzhou University, China Master’s degree from the Technical University of Munich, School of CIT Research Focus: Zhiyuan Wu specializes in machine learning, with particular emphasis on probabilistic graphical models, information theory, and tackling real-world challenges such as distributional shifts and privacy concerns. His work explores entropy regularization techniques to address label shift in distributed learning systems, aiming to improve model robustness and data privacy across diverse domains like medical applications. Publications & Research Trends: His recent publication at the International Conference on Learning Representations highlights a novel approach to mitigating label shift through entropy regularization. This aligns with his broader research goals of enhancing the adaptability and interpretability of machine learning models in dynamic, privacy-sensitive environments. Labs & Teams: He is actively involved with the Digital Signal Processing and Image Analysis (DSB) research group, contributing to collaborative projects that bridge theoretical advancements with practical implementations in machine learning.

Benjamin Ricaudمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +۳ مورد دیگر

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

Ferreira Joao Carlosمشاهده پروفایل

استاد

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

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

Per-Arne Andersenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Reinforcement Learning
• Tsetlin Machines
• +۷ مورد دیگر

Per-Arne Andersen is an Associate Professor at the Department of Information and Communication Technology within the University of Agder . His research focuses on artificial intelligence , reinforcement learning , Tsetlin machines , and deep learning , with applications in real-time strategy games , industrial environments , and IoT systems . Projects: RESTORE Research Groups: CAIR - Center for Artificial Intelligence Research, CIEM - Center for Integrated Crisis Management, Intelligent Mechatronics (iTron) His work explores safe and sustainable reinforcement learning , interpretable AI , and generative environment modeling . He has developed frameworks like CaiRL and CostNet for high-performance RL environments and goal-directed learning. Recent publications include advancements in Tsetlin automaton analysis , GNSS jamming classification , and road quality detection . Articles from 2025-2016 span machine learning , computer vision , and environmental modeling . He contributes to IEEE , Springer , and LNCS publications, with a focus on interdisciplinary AI applications in crisis management , cybersecurity , and industrial optimization .

Ole-Christoffer Granmoمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Tsetlin Machines
• +۷ مورد دیگر

Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.

• Biomedical Instrumentation
• Medical Technology
• Clinical Engineering
• +۳ مورد دیگر

Jan Olav Høgetveit is an Associate Professor in the Department of Physics at the University of Oslo (UiO), within the Faculty of Mathematics and Natural Sciences. He also serves as Head of Research & Development in the Department of Biomedical and Clinical Engineering at Rikshospitalet, Norway’s national hospital. His work bridges physics and clinical practice, focusing on medical instrumentation. Education: Bachelor of Electronic Engineering (Technical Cybernetics), Oslo University College, 1993 Master of Electronics, University of Oslo (Department of Physics), 1997 Ph.D. in Physics (Technology Applications for Medical Devices), University of Oslo, 2008 Research Interests: Høgetveit specializes in biomedical instrumentation and clinical engineering, particularly in surgical technology and wireless communication impacts on medical devices. His work addresses challenges like real-time physiological monitoring during heart-lung machine use, non-invasive blood glucose detection, and bioimpedance-based viability assessment of organs. He emphasizes interdisciplinary collaboration between engineering and medicine to enhance patient safety and surgical outcomes. Scientific Contributions: His research trends span bioimpedance applications in ischemia/reperfusion injury, machine learning for surgical decision support, and electrosurgery safety. He has explored ventilator optimization during pandemics and implant-related thermal risks. Contributions highlight both hardware development (e.g., optically isolated current sources) and software innovations (e.g., neural networks for viability prediction). Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: Høgetveit received a 1998–2001 research council scholarship. As Head of R&D since 2001, he oversees translational projects. No formal advisees/students listed, though he collaborates extensively with teams on device development and clinical trials. Labs & Teams: Affiliated with UiO’s Department of Physics and Rikshospitalet’s Biomedical and Clinical Engineering department. Active in the Electronics research group at UiO. Engages with multidisciplinary teams addressing surgical instrumentation and physiological monitoring challenges.

Adín Ramírez Riveraمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Representation Learning
• +۶ مورد دیگر

Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.