جستجوی استاد راهنمای دانشگاهی

Daniel M. Royمشاهده پروفایل

استاد

• Machine Learning
• Statistics
• Probabilistic Programming
• +۷ مورد دیگر

Daniel M. Roy is a Full Professor at the University of Toronto, with cross-appointments in the Department of Computer Science, Department of Statistical Sciences, and Department of Electrical and Computer Engineering. He serves as Research Director at the Vector Institute and holds the CIFAR Canada AI Chair. Research Focus: Foundational principles of prediction, inference, and decision-making under uncertainty across machine learning, statistics, mathematical logic, applied probability, and computer science. Scientific Contributions: Key work in learning theory, statistical network analysis, probabilistic programming, and information-theoretic frameworks for generalization. Awards: ICML 2024 Best Paper Award for "Information Complexity of Stochastic Convex Optimization" and promotion to Full Professor in 2024. Student Advising: Actively mentors Ph.D. candidates and postdoctoral researchers with strong quantitative backgrounds, particularly at the intersection of machine learning, statistics, and computer science. Email: daniel.roy@utoronto.ca

• 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

• 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 .

Ali Ramezani-Kebryaمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Federated Learning
• +۹ مورد دیگر

Ali Ramezani-Kebrya is an Associate Professor with tenure in the Department of Informatics at the University of Oslo (UiO), where he leads research in machine learning theory. He holds dual Principal Investigator roles at the Norwegian Center for Knowledge-driven Machine Learning (Integreat) and SFI Visual Intelligence, and is an active member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. His service includes Area Chair positions for NeurIPS and AISTATS, and Action Editor for Transactions on Machine Learning Research. His research focuses on theoretical foundations of deep learning with emphasis on understanding input data distribution encoding in neural network layers. Key themes include minimizing statistical risk under resource constraints, addressing distribution shifts in distributed settings, and developing practical tools for robust federated learning. Current applications span emotion recognition, marine data analysis, and neuroscience, reflecting his commitment to real-world machine learning challenges as evidenced by his FRIPRO-funded Machine Learning in Real World (MLReal) project. Recent publication trends reveal three dominant threads: (1) label/covariate shift mitigation in distributed systems through entropy regularization and density ratio estimation; (2) communication-efficient optimization via layer-wise quantization and adaptive compression techniques achieving 150% speedups; and (3) robustness guarantees against tailored attacks and distribution shifts. These works consistently bridge theoretical bounds with empirical validation across domains from GAN training to federated settings. Scientific recognition includes: FRIPRO Grant for Early Career Scientists (2025) for MLReal project SFI Visual Intelligence Spotlight Publication award (2023) for federated learning work He actively mentors 11 graduate students across Oslo and Tromsø universities, with recent PhD placements at Apple and NVIDIA. Current grant portfolio features the FRIPRO Early Career award and leadership roles in two major Norwegian research centers. His lab maintains strong industry collaborations through Vector Institute and EPFL, with recent hiring for PhD and postdoc positions in physics-informed machine learning.

• Artificial Intelligence
• Knowledge Management
• Secure Software Development
• +۳ مورد دیگر

Shao-Fang Wen is an Associate Professor at the Department of Information Security and Communication Technology, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). Their work focuses on integrating artificial intelligence with cybersecurity, secure software development, and socio-technical systems. Research Interests: Artificial Intelligence and Cybersecurity Secure Software Development Semantic Web and Ontology Socio-Technical Systems System Security Assurance Publications and Contributions: Shao-Fang Wen has contributed significantly to developing frameworks for AI security assurance, quantitative security evaluation models (e.g., SAEOn metamodel), and contextualized learning systems for software security. Their work spans journal articles, conference papers, and book chapters, emphasizing ontology-based approaches and open source community dynamics. Teaching: IIKG2001 - Software Security

Tønnes Nygaardمشاهده پروفایل

دانشیار

• Robotics
• Evolutionary Algorithms
• Morphological Adaptation
• +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.

• Biometrics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.

Daniel Romeroمشاهده پروفایل

استاد

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +۵ مورد دیگر

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

• Artificial Intelligence
• Physics
• Computer Technology
• +۴ 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.

• Cybersecurity
• Critical Infrastructure Security
• Reinforcement Learning
• +۳ مورد دیگر

Laszlo Tibor Erdodi is an Associate Professor at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Information Security and Communication Technology within the Faculty of Information Technology and Electrical Engineering . His research focuses on cybersecurity in critical infrastructure, reinforcement learning for penetration testing, and vulnerability exploitation in power grid systems. Erdodi collaborates with institutions like CIGRE and IEEE on projects evaluating security of wide-area monitoring systems and industrial control systems. Key research areas include advanced persistent threats (APT) targeting power grid substations, stealthy data integrity attacks on critical infrastructure, and the application of reinforcement learning to simulate cyber-attacks (e.g., SQL injection and CTF challenges). His work often involves hardware-in-the-loop testbeds to assess vulnerability mitigation strategies in real-world scenarios. Publications emphasize cybersecurity for industrial control systems, penetration testing methodologies, and ethical hacking frameworks. Notable contributions include studies on IEC 61850/60870-5-104 protocol vulnerabilities, PTP timing attacks in substations, and lightweight IoT security solutions (e.g., LIST framework). Erdodi’s work bridges theoretical cybersecurity models with practical implementation, addressing challenges in both academic and industrial sectors. He contributes to conferences like IEEE PES ISGT Europe and CIGRE symposiums, focusing on resilient critical infrastructure design and automated threat detection systems.

Sukalpa Chandaمشاهده پروفایل

دانشیار

• Machine Learning
• Zero-Shot Learning
• One-Shot Learning
• +۷ مورد دیگر

Sukalpa Chanda is an Associate Professor at the Department of Computer Science and Communication, Halden University College. His research focuses on Machine Learning with applications to Document Image Analysis, Computer Vision, and Video Image Analysis, including advanced methods like Zero-Shot Learning, Deep Learning, and Transformer Networks. PhD in Computer Science from NTNU Appointments: Postdoctoral Researcher at Uppsala University (2018-2019) and Groningen University (2016-2018) Research Interests: Chanda specializes in Zero-Shot and One-Shot Learning for document and image analysis, with applications in handwriting recognition, face generation, and biomedical imaging. His work bridges theoretical machine learning with practical implementations in cultural heritage preservation and healthcare diagnostics. Scientific Collaboration: He collaborates with institutions like Indian Institute of Technology (Pallakad/Patna) and leads the Hugin Munin Project under The Digital Society research priority area. His team includes Master’s students and research assistants working on Transformer Networks and generative models. Key Publications: Recent works include frameworks for zero-shot action recognition (T2L, 2025), Nordic manuscript writer identification (2023), and advanced medical image segmentation networks (PAANet, 2021). His research spans document analysis, deep metric learning, and biomedical applications.

Audun Jøsangمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Trust and Reputation Systems
• +۴ مورد دیگر

Audun Jøsang is a Professor of Cybersecurity at the University of Oslo and holds a Visiting Professor position at Queensland University of Technology (QUT) . His work focuses on trust and reputation systems in digital environments, with notable contributions to subjective logic —a mathematical framework for reasoning under uncertainty. Education : MSc in Information Security from Royal Holloway, University of London , MSc and PhD in Telecommunications from Norwegian University of Science and Technology (NTNU) Books : Subjective Logic: A Formalism for Reasoning Under Uncertainty (2016), Cybersecurity: Technology and Governance (2011) His research spans cybersecurity , artificial intelligence , and risk assessment , with applications in online marketplaces , social media , and decision-making under uncertainty . He has developed mathematical models for uncertainty-aware influence propagation and competitive information spread using subjective logic. Audun Jøsang’s work bridges theoretical foundations and industrial applications of computational trust and security analytics . His recent publications explore ethical AI , fair federated learning , and dynamic intelligence assessment for artificial general intelligence development.

Mahmoud Sayed Mahmoud Eidمشاهده پروفایل

پژوهشگر ارشد

• Data-driven fault diagnosis
• Electric power trains
• Electromagnetic modeling
• +۱ مورد دیگر

Mahmoud Sayed Mahmoud Eid is a PhD Research Fellow in Engineering Sciences at the University of Agder. His research focuses on fault diagnosis in electric powertrains using AI-driven methods. He holds a B.S. in Mechatronics Engineering from Helwan University (2013) and an M.S. in Advanced Mechanical and Robotics Engineering from Ritsumeikan University, Japan (2019). Research Focus: Develops computational models for early fault detection in electric motors and inverters under noisy operational conditions. His methodologies integrate electromagnetic theory with machine learning for industrial applications in transportation and energy systems. Publications: His 6 most recent articles (2022-2025) focus on AI-enhanced diagnostics for power electronics and motor systems, demonstrating consistent innovation in signal processing and classifier design for industrial reliability.

Narada Dilp Warakagodaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Autonomous Systems
• Computer Vision
• +۳ مورد دیگر

Narada Dilp Warakagoda is an Associate Professor at the University of Oslo, affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on Artificial Intelligence, Autonomous Systems, Computer Vision, Robotics, Machine Learning, and Deep Learning, with notable contributions to underwater acoustics, renewable energy forecasting, and unmanned vehicle control systems. He collaborates extensively with researchers across disciplines to advance applications in environmental monitoring, energy systems, and autonomous technologies. His work integrates advanced machine learning techniques with domain-specific challenges, such as synthetic aperture sonar analysis, spatio-temporal wind forecasting, and reinforcement learning for autonomous navigation. He has published in leading journals and conferences, addressing topics like probabilistic solar irradiance prediction, graph-based wind energy modeling, and deep learning for seabed munitions detection. Warakagoda’s research bridges theoretical advancements with practical implementations, emphasizing applications in robotics, environmental science, and defense technologies. His projects often involve interdisciplinary teams, reflecting his commitment to solving real-world problems through innovative AI-driven solutions.