Find Academic Supervisors

• Cyber-Physical Systems
• Machine Learning
• Autonomous Systems
• +5 more

Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.

Kjell Gunnar RobbersmyrView profile

Professor

• Mechatronics
• Machine Design
• Condition Monitoring
• +5 more

Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics

Simo AlamiView profile

Tutor

• Deep Learning
• Reinforcement Learning
• Inverse Reinforcement Learning
• +4 more

Simo Alami is a Ph.D. candidate and Tutor at Ecole Polytechnique, affiliated with the LIX computer science laboratory under the supervision of Professor Jesse Read. He teaches tutorials for the Advanced Machine Learning and Autonomous Agents course (Computer Science, Ingénieur 3A/Master1) since 2020, focusing on practical implementations of machine learning algorithms and autonomous systems. His academic background includes undergraduate and graduate studies in Mathematics and Computer Science at Université Pierre et Marie Curie and Ecole CentraleSupélec. Ph.D. Candidate, Ecole Polytechnique (2020-Present) Master's Studies, Mathematics and Computer Science, Université Pierre et Marie Curie and Ecole CentraleSupélec Alami's research centers on developing algorithms that learn from experience through self-learned metrics, with core expertise in Deep Learning, Inverse Reinforcement Learning, and Metric Learning. His work targets robotic applications where agents must perform novel tasks by understanding logical concepts, while also extending to energy management systems through non-invasive load monitoring. He explores how meta-learning frameworks can enable transferable reward structures across domains. His publication record reveals a strong trajectory in distributional reinforcement learning and inverse reinforcement learning using generative models, with significant contributions to metric learning for clustering and energy disaggregation. The consistent focus on transferable methodologies across robotics and smart grid applications demonstrates his interdisciplinary approach to solving real-world problems through advanced machine learning techniques. Scientific recognition includes: Nvidia Grant awarded in 2022 for research on Meta-Inverse Reinforcement Learning 3rd Prize at Hi Paris Hackathon (2021) for a reinforcement learning-based smart grid energy management solution While not supervising graduate students as a Ph.D. candidate, Alami has secured competitive external funding including the Nvidia research grant. His industry collaboration with Accenta during a visiting researcher position resulted in ECML 2022 publications on non-invasive load monitoring, highlighting his ability to bridge academic research with practical engineering applications. As an active member of the LIX laboratory at Ecole Polytechnique, Alami participates in cutting-edge computer science research while maintaining external collaborations. His visiting researcher position at Accenta demonstrates engagement with industry partners, and his organization of the IRT SystemX Electromobilité et territoires webinar in 2021 reflects leadership in knowledge dissemination within the energy and mobility research community.

Gita AlaghbandView profile

Professor

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +7 more

Professor Gita Alaghband is Chair and PhD Director at the Department of Computer Science and Engineering, University of Colorado Denver. Her work bridges high-performance computing with AI applications in computer vision, facial recognition, and deep learning optimization. Current research focuses on real-time multi-human tracking for autonomous systems Develops explainable AI solutions for medical and financial domains Leads the Parallel Distributed Systems (PDS) Lab Key publication trends include: 2025: Medical imaging AI for pediatric trauma analysis 2024: Financial forecasting with causal econometrics and adversarial defense systems 2023: Trajectory prediction models and HEVC compression frameworks Scientific recognition: IRC Best Paper Award (2020) for facial recognition research Students advised include industry leaders at Google, VMWare, and Nissan, with research spanning parallel computing, medical imaging AI, and autonomous systems.

Jaafar AlghazoView profile

Associate Professor

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +7 more

Dr. Jaafar Alghazo is an Associate Professor in the Math, Science and Technology Department at the University of Minnesota. He currently teaches courses in Information Technology Management and Software Engineering, including ITM 3900-E90: Internship, SE 2200-001: Intro to Software Engineering, and SE 4500-001: Senior Project I. Research Focus: Machine Learning, Deep Learning, Medical Image Analysis, Cybersecurity, IoT, UAV Communications Key Publication Trends: 15 most recent works span 2025–2023, emphasizing neural network optimization, Arabic sign language recognition, flood detection, and secure medical imaging techniques

• Operations Research
• Healthcare Optimization
• Predictive Maintenance
• +4 more

Rosita Guido is an Associate Professor in the Department of Mechanical, Energy and Management Engineering at Università della Calabria, specializing in Operations Research (MATH-06/A). Her academic role focuses on mathematical optimization and artificial intelligence applications across healthcare, manufacturing, and sustainable systems. She teaches graduate courses including Tools and Methods for Innovation in Healthcare and Business Intelligence within the Management Engineering program. Her research interests span healthcare optimization, predictive maintenance, machine learning applications, and sustainable supply chain management. Guido develops advanced mathematical models and algorithms for complex decision problems under uncertainty, with particular emphasis on healthcare systems optimization, tool condition monitoring, and resource allocation. Her methodological expertise includes mathematical programming, combinatorial optimization, stochastic programming, and hybrid AI techniques. Analysis of her recent publications reveals a strong interdisciplinary focus bridging engineering and healthcare. Key trends include the application of machine learning (particularly SVMs and CNNs) to medical diagnostics, optimization of healthcare operations (patient admission scheduling, bed management), and integration of AI with IoT for industrial applications. Her work demonstrates consistent innovation in solving real-world problems through mathematical modeling and algorithm development. As an active member of the Operations Research (Ricerca Operativa) research group at DIMEG, she contributes to projects addressing logistics optimization, green transportation systems, energy infrastructure management, and clinical process optimization. The group maintains international collaborations with prestigious research centers and companies including Amazon. Guido's teaching portfolio includes graduate-level courses in Management Engineering, where she integrates her research expertise into curriculum development. Her academic service includes participation in departmental governance and research initiatives within the Department of Mechanical, Energy and Management Engineering.

• Medical Image Analysis
• Deep Learning
• Machine Learning
• +7 more

İlkay Öksüz is an Associate Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), where he has been serving since 2021, following his appointment as a Doctoral Academic Staff member in 2020. He previously held research positions at King's College London (2017–2020), The University of Edinburgh (2016), and Yale University (2015–2016). He earned his Ph.D. in Computer, Decision and Systems Science from IMT School for Advanced Studies Lucca, Italy, in 2017, under the supervision of Prof. Sotirios Tsaftaris. B.Sc. in Electronics Engineering, Istanbul Technical University (2010) M.Sc. in Electrical-Electronics Engineering, Bahçeşehir University (2013) Ph.D. in Computer, Decision and Systems Science, IMT School for Advanced Studies Lucca (2017) His research focuses on medical image analysis, particularly in cardiac MRI, with core interests in image segmentation, registration, quality assessment, and reconstruction using deep learning. He also explores electricity price forecasting and explainable AI. His work bridges machine learning with clinical applications, aiming to improve diagnostic accuracy and automation in radiology. The 15 most recent publications highlight a strong trend in applying deep learning to medical imaging, especially cardiac and prostate MRI, mammography, and ECG analysis. A significant emphasis is placed on explainability , optimization , and clinical applicability , with frequent participation in MICCAI challenges and collaborations with radiology departments. Projects also extend into energy forecasting, showing interdisciplinary versatility. Scientific awards include: 2024 ITU Young Scientist Award in Engineering 2024 Parlar Research Incentive Award He leads the Predictive Intelligence and Medical Imaging (PIMI) Lab at ITU, mentoring graduate students and managing multiple funded projects, including the TUBITAK International Fellowship. His lab has achieved top placements in national and international AI competitions such as Teknofest and MICCAI challenges. He serves as principal investigator (PI) on several active grants focused on interpretable deep learning for medical imaging and electricity forecasting. The PIMI Lab, under his leadership, actively participates in high-impact medical AI challenges and has consistently achieved top rankings in competitions related to prostate cancer detection, breast imaging, and cardiac MRI reconstruction, demonstrating strong translational research impact.

• Self-Supervised Learning
• Unsupervised Representation Learning
• Computer Vision
• +13 more

Aiden Durrant is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen, actively contributing to research and teaching in computing science. He is affiliated with the South China Normal University Joint Institute and is accepting PhD students in Computing Science. His research spans machine learning, computer vision, and AI, with a focus on self-supervised and geometric representation learning. BSc (Hons) and MPhil in Computer Science, University of Lincoln PhD in Computing Science, University of Aberdeen Aiden Durrant’s research centers on self-supervised learning and the geometric structuring of learned representations. He investigates how to learn high-quality image representations without human-annotated labels, focusing on preserving semantic and hierarchical knowledge through hyperbolic and hyperspherical geometries. His work extends to multi-modal learning and has practical applications in environmental monitoring, agriculture, nuclear reactor safety, and climate change. He has developed novel architectures such as HMSN (Hyperbolic Self-Supervised Learning) and S-JEA (Stacked Joint Embedding Architectures) to improve representation quality. His recent publications (2023–2025) demonstrate a strong trend in foundation models, self-supervised learning, and deep learning for environmental and industrial applications. Key themes include crevasse mapping in glaciology, segment anything models for drone imagery, and hyperbolic clustering for semantic hierarchies. His earlier work (2019–2022) focused on nuclear reactor anomaly detection using deep learning and time-series analysis, contributing to the CORTEX Horizon 2020 project. Scientific Awards: PhD Poster Award, FISA - 9th European Commission conference on Euratom research and training in safety of reactor systems (2019) Durrant has secured competitive funding, including a 2024 SUSTAIN CDT Studentship (PI) and an Aberdeen Internal Pump-Priming grant (Co-PI), supporting research in AI for glacier mapping and sustainable crop breeding. He supervises PhD students and has co-led teaching modules in Machine Learning, Data Mining, and Research Methods at both undergraduate and postgraduate levels. He also contributes nationally as Deputy Director of the SICSA Graduate Academy, supporting research training across all 14 Scottish computer science departments. He is actively involved in interdisciplinary collaborations, particularly in agri-food data sharing, environmental sustainability, and healthcare AI, often working with cross-institutional teams. His external memberships include BMVA and leadership in organizing the BMVA 2025 Summer School.

• Multimodal signal processing
• Audio-video signal processing
• Biomedical signal processing
• +12 more

Richard M. Dansereau is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He holds a Ph.D. from the University of Manitoba and is a Professional Engineer (P.Eng.) and Senior Member of IEEE. He currently serves as Associate Dean (Graduate Studies) and Clerk of Senate, reflecting his leadership roles within the university. His research interests include: Multimodal and audio-visual signal processing Biomedical and biometric signal processing Image and speech signal processing Compressive sensing and deep learning for reconstruction Fractal and multifractal complexity measures, including Rényi dimensions Applications in medical imaging, speech enhancement, and radar systems Recent publications highlight his lab's focus on advanced deep learning techniques for image reconstruction (e.g., deep equilibrium models for compressive sensing), medical image analysis (e.g., PET reconstruction and cervical cell segmentation), and Riemannian geometry in radar signal processing for drone detection. His work integrates theoretical signal processing with practical applications in healthcare and defense. Scientific awards associated with his research group include: Ontario Graduate Scholarship Alexander Graham Bell Canada Graduate Scholarship (CGS D) John Ruptash Memorial Fellowship NSERC Best Project Award 1st prize in poster competition at hSITE 2012 Finalist for World Congress Award at WSCTS’2006 Dansereau actively supervises graduate students, with a long list of Ph.D. and M.A.Sc. alumni who have worked on topics such as speech separation, ECG analysis, image registration, and radar signal processing. He collaborates with researchers at institutions like the University of Ottawa Heart Institute and Defence Research and Development Canada (DRDC). His lab, the Signal Processing and Machine Learning Lab, continues to publish in top journals and conferences, securing research opportunities for Canadian, American, and British citizens in speech intelligibility research.

• Digital Signal Processing
• Biomedical Engineering
• Audio Processing
• +7 more

Rafik Goubran is a Professor in the Department of Systems and Computer Engineering at the Faculty of Engineering and Design, Carleton University. He holds the distinguished title of Chancellor’s Professor and currently serves as the Vice-President (Research and International). He earned his Ph.D. from Carleton University and is a licensed Professional Engineer (P.Eng.). His research expertise spans digital signal processing, biomedical engineering, audio processing, and the development of smart environments for senior independent living, with applications in patient monitoring, sensor systems, and real-time analytics. Dr. Goubran's recent publications highlight a strong focus on applying machine learning and sensor technologies to healthcare challenges, particularly in gerontechnology. His work involves non-invasive monitoring of sleep apnea, cognitive decline, driving behavior in older adults, and ambient health monitoring using smart homes and IoT systems. He explores innovative methods such as using pressure mats, audio analysis, and video magnification for vital sign detection and behavioral assessment. Life Fellow, IEEE Fellow, Canadian Academy of Engineering (CAE) Chancellor’s Professor, Carleton University Dr. Goubran has co-supervised 24 Ph.D. and 72 Master’s students and has secured significant research funding from NSERC, CIHR, NCE, and OCE for projects related to aging, health monitoring, and smart technologies. He is the co-leader of the TAFETA project and was the founding Director of the Ottawa-Carleton Institute for Biomedical Engineering. His work involves extensive collaboration with institutions like the Bruyère Research Institute and industry partners such as QNX and BlackBerry. He leads a research lab focused on technology-assisted environments, developing systems for patient monitoring, fall detection, and cognitive assessment, contributing significantly to the field of assistive and ambient intelligence for healthcare.

• Computer Vision
• Image Processing
• Photometric Stereo
• +12 more

Vasileios Argyriou is an academic researcher affiliated with Kingston University, with extensive contributions in computer vision, image processing, and AI applications in IoT, healthcare, and smart agriculture. His work spans photometric stereo, motion estimation, federated learning, and UAV-based remote sensing. Research Interests: His research focuses on developing advanced computer vision techniques including photometric stereo for 3D reconstruction, deep learning for anomaly detection, federated learning for privacy-preserving AI, and AI-driven solutions for smart farming and healthcare. He explores motion estimation, facial recognition, and synthetic data generation for training robust models. The recent publications (2024–2025) highlight a strong trend toward applying machine learning in real-world systems—particularly federated learning for intrusion detection, UAV-based agricultural monitoring, 5G security, and medical imaging. His work integrates computer vision with IoT, wireless communications, and edge computing, demonstrating interdisciplinary innovation. Scientific Contributions: Author of a book on image and video registration with quality metrics. Extensive publication record in top journals including IEEE Transactions, Sensors, and Pattern Recognition. Pioneering work in photometric stereo, motion estimation using phase correlation, and GAN-based face reenactment. He has advised or collaborated with numerous researchers across fields, though specific student names are not listed. His work often involves large-scale data analysis, simulation frameworks, and real-time systems. He is involved in projects related to smart cities, precision agriculture, and autonomous systems. Laboratories and Teams: His collaborations suggest involvement with research groups focused on intelligent systems, computer vision, and IoT at Kingston University and beyond, particularly with Panagiotis Sarigiannidis, Thomas Lagkas, and others in networking and AI.

Massoud ZolgharniView profile

Professor

• Computer Vision
• Medical Imaging
• Artificial Intelligence
• +7 more

Massoud Zolgharni is a Professor of Computer Vision at the School of Computing and Engineering, University of West London. He joined UWL as a Senior Lecturer in 2018 and was promoted to Associate Professor in 2020. Previously, he held academic positions at the University of Lincoln and was a Research Associate at Imperial College London. He is a Fellow of the Higher Education Academy and leads the MSc Artificial Intelligence and PhD programs at UWL. BSc in Mechanical Engineering, Amirkabir University of Technology Master’s and PhD, Brunel and Swansea Universities, UK His research focuses on computer vision and medical imaging , particularly in automated cardiac imaging using AI and deep learning . His work aims to develop low-cost, non-invasive techniques for echocardiography and cardiovascular diagnostics. He has published extensively in journals such as Computers in Biology and Medicine , IEEE Transactions , and European Heart Journal . Recent publications (2023–2025) highlight his leadership in AI-driven echocardiography, including left ventricle segmentation, Doppler analysis, and multimodal seizure detection. His research integrates deep learning, signal processing, and real-world clinical validation, establishing a strong interdisciplinary profile bridging computing and medicine. Scientific Awards : Fellow of the Higher Education Academy He has secured major grants from the British Heart Foundation , including a £1.5M Programme Grant (2022–2027) on AI integration in echocardiography. He serves on university research committees, acts as Critical Reader for the School, and organizes the monthly research seminar. He teaches across a wide range of computing and AI programs, contributing to curriculum development and research supervision.

Di YangView profile

Assistant Professor

• Remote Sensing
• Geospatial Analytics
• Machine Learning
• +7 more

Di Yang is an Assistant Professor at the University of Florida, specializing in geospatial technologies and environmental monitoring. His research integrates machine learning, remote sensing, and citizen science to address land use changes, ecosystem dynamics, and human-environment interactions across global scales. He leads projects funded by NASA, BLM, and NCAR, focusing on topics like forest management, climate resilience, and geospatial cloud computing. Education: Ph.D. in Geography, University of Florida (2019) M.S. in Environmental Engineering, Texas A&M University-Kingsville (2013) B.S. in Environmental Science, Liaoning University of Petroleum and Chemical Technology (2011) Research Interests: Remote sensing applications, geospatial analytics, machine learning for environmental monitoring, citizen science, land change science, and sustainability. Grants & Projects: NASA: Forecasting Mosquito-Borne Disease Risk in a Changing Climate (PI) NASA Early-Career: Evaluating Long-term Impacts of Land-use Transformation on Fire Regimes BLM: Monitoring Yermo xanthocephalus using UAS and AI/ML His lab develops innovative frameworks like the Open Land-Use Map strategy and leverages platforms like Google Earth Engine for large-scale environmental analysis. Yang actively collaborates on NEON biodiversity surveys and contributes to ecological network design.

• Mechanical Engineering
• Manufacturing Engineering
• Finite Element Analysis
• +5 more

Ambarish Kulkarni is an Associate Professor at Swinburne University of Technology's School of Engineering, specializing in mechanical design, virtual/augmented reality (VR/AR), and electric vehicle (EV) technologies. His research focuses on product design, EV drivetrains, battery packaging, medical devices, and bushfire shelter design. He holds a PhD from Swinburne, a Master's from Queensland University of Technology, and a Bachelor's from the Indian Institute of Industrial Engineering. Research interests span finite element analysis, bio-mechanical modeling, and Industry 4.0 applications. Notable projects include Bombardier Tram development, therapeutic sleep systems, and smart manufacturing frameworks. He has supervised over 20 HDR students on topics ranging from predictive maintenance to extended reality in education. Awards include the Teaching Excellence Award (2016) and Premier's Design Awards for medical and safety innovations. He is a Fellow of the Institution of Engineers (Australia) and actively involved in professional bodies like SAE International. Grants funded projects on hydrogen generators, smart manufacturing systems, and graphene supercapacitors. His work bridges academia and industry through collaborations with firms like Bambach Wires and iMOVE Australia.

Serveh KamravaView profile

Assistant Professor

• Machine Learning
• Fluid Dynamics
• Chemical Discovery
• +7 more

Serveh Kamrava is an Assistant Professor at the Colorado School of Mines, Department of Chemical and Petroleum Engineering. Her research bridges Chemical, Environmental, and Petroleum Engineering with advanced computational methods, focusing on energy storage, fluid dynamics, and machine learning applications in complex systems. Research Interests: Her work centers on developing physics-guided machine learning models to predict flow and transport in heterogeneous and porous materials. She aims to improve energy storage systems by enhancing efficiency and longevity through data-driven and multiscale modeling techniques. Key areas include deep learning for material reconstruction, fluid flow simulation, and dispersion prediction in geophysical and environmental contexts. Publication Trends: Her recent publications (2019–2021) demonstrate a strong focus on integrating deep learning with physical laws in porous media, particularly in geological and membrane systems. The work spans journals in geophysics, computational materials, environmental science, and chemical engineering, showing interdisciplinary impact. Scientific Awards: 2022 NSF Award 2022 ACS-PRF DNI Award Advising and Grants: Dr. Kamrava is actively recruiting PhD students, indicating ongoing research funding and mentorship activities. She likely holds active grants, including the NSF award, supporting her work in machine learning applications for energy and environmental systems. She advises prospective students with backgrounds in machine learning and computational fluid dynamics. Labs and Teams: She leads a research group focused on the interface of engineering and data science, utilizing state-of-the-art machine learning to solve multiscale problems in energy and environmental engineering. The group emphasizes Python-based computational modeling and interdisciplinary collaboration.