Find Academic Supervisors

• Statistical Learning
• High-Dimensional Data Analysis
• Network Statistics
• +7 more

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

• Computer Vision
• Intelligent Robot Control
• 3D Computer Graphics
• +12 more

Dr Jiacheng Tan is a Senior Lecturer in the School of Computing at the University of Portsmouth , where he has been a faculty member since 2002. He is actively involved in research and PhD supervision, with a strong focus on intelligent systems and robotics. Dr Tan earned his BEng in Mechanical Engineering from Jilin University of Technology (1983), an MSc in Mechatronics from Xidian University (1989), and a PhD in Computer Graphics from De Montfort University (2001). Prior to joining Portsmouth, he served as a Visiting Researcher in robotics at the University of Salford (1996–1997) and a Research Fellow in artificial intelligence at the Open University (2000–2002). His research centers on computer vision, intelligent robot control, 3D graphics, and human-robot interaction . He investigates how robots can understand and act upon natural language commands by grounding spatial relations, recognizing objects, and reasoning about tasks in unstructured environments. His work integrates fuzzy logic, knowledge engineering, and machine learning to enable robots to learn from demonstrations and interact meaningfully with humans. The trends in his publications reveal a consistent focus on robotics and intelligent systems , evolving from early work in virtual environments and telerobotics to recent contributions in cloud-based scientific visualization and machine learning applications in astrophysics. His interdisciplinary research spans computer science, control theory, and cognitive systems. Dr Tan has not received any explicitly mentioned scientific awards in the provided text. He serves as a PhD supervisor and has contributed to multiple research projects, including the development of integrated AI frameworks for robotic manipulation. While specific grant details are not listed, his collaborations and publications suggest active involvement in funded research initiatives. He has worked with researchers across institutions on topics ranging from scientific visualization to intelligent interfaces. Dr Tan is affiliated with the Computational Intelligence Research Group at the University of Portsmouth. His lab work involves developing virtual environments, symbolic representations of 3D scenes, and natural language interfaces for robot control, supporting both academic research and practical applications in automation.

• Medical Imaging
• PET/CT Imaging
• Quantitative Image Analysis
• +5 more

Drew A. Torigian is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania, with extensive contributions to medical imaging research. His work focuses on developing advanced methodologies for PET/CT image analysis, disease quantification, and anatomical segmentation through innovative applications of deep learning and computer vision techniques. Dr. Torigian's research interests span multiple critical areas in medical imaging including quantitative image analysis, PET/CT applications, deep learning in medicine, radiation therapy planning, and anatomical segmentation. His work has significantly advanced the field of medical image analysis through the development of novel algorithms for disease quantification without explicit object delineation, standardized anatomic space frameworks, and attention-based neural networks for medical image interpretation. His recent publications demonstrate a strong trend toward integrating artificial intelligence with medical imaging, particularly in developing gaze-guided neural networks, geographical attention mechanisms, and hybrid transformer-convolutional architectures for improved medical image analysis. These works consistently address critical challenges in radiology including disease quantification, anatomical segmentation, and the development of clinically interpretable AI models. Dr. Torigian has received recognition through his substantial publication record in top-tier medical imaging venues including Medical Image Analysis, IEEE Transactions on Biomedical Engineering, and leading medical imaging conferences. His research has been consistently funded through various mechanisms supporting innovation in medical imaging technology. Through his mentorship and collaborative research, Dr. Torigian has contributed to advancing the field of medical imaging with practical applications in radiation therapy planning, disease quantification, and the development of standardized methodologies for image analysis. His work bridges the gap between computer science innovation and clinical radiology applications.

• Computer Science
• Machine Learning
• IoT
• +7 more

Ali Hassan is a researcher affiliated with the National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science, Department of Computer and Software Engineering. His work spans multiple domains in computer science, engineering, and applied mathematics, focusing on areas such as machine learning, IoT, energy systems, and medical informatics. His research explores: Reinforcement learning applications for battlefield information systems IoT antenna design and performance evaluation Optimization of second-life battery systems in electric vehicles Transformers for real-time vehicle collision avoidance Image hashing techniques using visual attention models Neural network-based phasor estimation for power grids Biomedical sensor systems for non-invasive health monitoring Mathematical modeling of viral dynamics Recent publications demonstrate his emphasis on interdisciplinary approaches combining AI, signal processing, and sustainability. He has collaborated with institutions across Pakistan, France, Saudi Arabia, and the USA, with a focus on practical implementations in cybersecurity, energy optimization, and healthcare technology.

• Computer Vision
• Image Processing
• Stereo Matching
• +4 more

Asmaa Farouk Mohammed is a researcher at Vienna University of Technology's Department of Software Technology and Interactive Systems. Her work focuses on computer vision, stereo matching, and real-time algorithms, with a strong emphasis on cost-volume filtering and adaptive support weight techniques. She holds a PhD in computer science and has contributed significantly to 3D video technology and interactive video segmentation. Key research areas include: Efficient stereo matching algorithms for real-time applications Spatio-temporal filtering for video processing Interactive systems for object segmentation Geodesic-based image processing methodologies Her publications demonstrate a clear trajectory in advancing both theoretical and applied aspects of visual correspondence and 3D reconstruction. Collaborations with researchers like Michael Bleyer and Margrit Gelautz highlight her role in interdisciplinary projects. While no specific grants or awards are listed, her extensive publication record reflects sustained contributions to the field of computer vision since 2009. She is affiliated with the Network Lab at TU Wien, contributing to cutting-edge research in interactive systems and algorithm optimization.

Gültekin IşıkView profile

Associate Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Assoc. Prof. Gültekin Işık is affiliated with the Department of Computer Hardware at Iğdır University's Faculty of Engineering. He holds a PhD in Computer Engineering from Hacettepe University (2011–2019), focusing on Turkish dialect recognition using deep learning. His academic roles include serving as Head of the Department from 2019 to 2021. His research interests span deep learning applications in computer vision (e.g., YOLO-based crowd detection), optimization algorithms (e.g., Slime Mould Algorithm), environmental modeling (solar power efficiency prediction), and healthcare (breast cancer detection). He has authored numerous peer-reviewed articles and books, including works on convolutional neural networks for plant disease identification and hybrid optimization techniques for data clustering. Prof. Işık teaches advanced courses such as İleri Derin Öğrenme (Advanced Deep Learning) and Sinir Ağları (Neural Networks). His recent work emphasizes real-time video analysis for public health (social distancing) and energy systems optimization. Despite no listed awards, his contributions to interdisciplinary fields like bioacoustics and renewable energy are notable. He has advised two master’s students: Mehmet Şirin Gündüz (2023) on YOLO-based crowd detection and Seda Bayat (2021) on bird species recognition using deep learning. His research integrates theoretical models with practical applications in domains ranging from agriculture to medical imaging.

Mohamed ShehataView profile

Professor

• Computer Vision
• Image and Video Processing
• Biomedical Applications
• +4 more

Mohamed Shehata is a Professor in the Department of Computer Science, Mathematics, Physics, and Statistics at the University of British Columbia's Irving K. Barber Faculty of Science. He holds an adjunct professorship at Memorial University of Newfoundland's Computer Engineering Department. His research focuses on computer vision, biomedical applications, and intelligent camera systems. He earned his B.Sc. (Zagazig University), M.Sc. (Zagazig University), Ph.D. (University of Calgary), and P.Eng. licensure. Previously, he worked at Intelliview Technologies Inc. as Vice President of Engineering and Research. He has held roles as an assistant and associate professor at Memorial University before joining UBC in 2019. He serves as Editor-in-Chief of the IEEE Canadian Journal of Electrical and Computer Engineering and has contributed to over 70 peer-reviewed publications. His work spans video surveillance systems, domain generalization, and medical imaging applications. Education: B.Sc. (Honors), Zagazig University M.Sc., Computer Engineering, Zagazig University Ph.D., University of Calgary Research Interests: He explores cutting-edge topics like federated learning for domain adaptation, biomedical image analysis, and lightweight neural networks for embedded systems. His recent work emphasizes cross-domain generalization and few-shot learning for medical diagnostics and object tracking. Professional Contributions: Dr. Shehata has supervised graduate students and led projects in computer vision applications. His publications bridge theoretical advancements with practical systems like drone-based surveillance and IoT healthcare devices. He actively contributes to IEEE committees and academic journal editing.

Kristine ThompsonView profile

Associate Professor

• Photography
• Digital Art
• Death Representation
• +4 more

Kristine Thompson is an Associate Professor of Photography and MFA Graduate Coordinator at LSU's School of Art within the College of Art & Design. She holds a BS from Northwestern University and an MFA from the University of California, Irvine. Her work explores representations of death, mourning practices, and the intersection of past and present through photography and digital media. Her research focuses on how photographic imagery circulates globally, particularly in contexts of grief and memorialization. Notable projects include Images Seen to Images Felt (2016), which transforms digital news imagery into tactile photograms, and Still Beneath the Surface (2018), an installation probing personal and cultural mourning through buried photographs. She has exhibited at institutions like Commonwealth & Council (Los Angeles) and Armory Center for the Arts. Thompson has received grants including the DAAD Fellowship and Center for Cultural Innovation's Investing in Artists Award. Her curatorial work includes projects at UCR/California Museum of Photography. She teaches courses ranging from basic photography to graduate-level studies in art as research and portraiture. Her publications include collaborative books like Past & Present Tense (2018) and Images Seen to Images Felt (2018), which contextualize her artistic and curatorial explorations of memory and mortality.

• Cartography
• Geomatics
• Historical GIS
• +7 more

Jakub Kuna is an Assistant Professor at the Department of Geomatics and Cartography, Faculty of Earth Sciences and Spatial Management, Maria Curie-Skłodowska University (UMCS) since 2015. He earned his PhD in Earth Sciences (Geography) in 2017. His work bridges cartography, geomatics, and historical GIS. Key Roles: Researcher, Lecturer, Project Coordinator for NPRH grants Projects: Atlas historyczny miast polskich, DARIAH-PL, NCN Miniatura 4.0 Research Focus: He specializes in cartographic mathematics, spatial-temporal analysis, 3D/4D modeling, and geostatistical methods in R programming. His work advances Historical GIS for reconstructing urban landscapes and processing archival aerial imagery. Key Publications: His 2025 Scientific Reports paper on loess gully sustainability and 2024 ISPRS Journal methodology for multi-sheet historical map georeferencing highlight his technical contributions. Recent monographs include Zamość’s digital reconstruction in the Atlas historyczny miast polskich series. Scientific Awards: NPRH Grant (2023-2027): Leadership role in historical city atlas project European Funds (2021-2024): DARIAH-PL digital infrastructure NCN Miniatura Grant (2020-2021): Pioneering 1944 Lublin orthophotomap Academic Leadership: He supervises 18 MA/BA theses on topics like Lublin’s WWII urbanism and Shtetl Routes tourism. As Deputy Chair of the Polish Cartographic Association (2024) and ICA member (2021-), he shapes national cartographic standards.

Bahram JavidiView profile

Professor

• Real-time multi-dimensional imaging
• Nanotechnology in optical sensing
• Biomedical optics/photonics
• +4 more

Bahram Javidi is a Professor in the Department of Biomedical Engineering at the University of Connecticut. His research focuses on advanced optical imaging technologies, including real-time 3D sensing, visualization, and information processing. He integrates nanotechnology, biomedical photonics, and quantum optics into novel imaging systems for medical and underwater applications. Key Research Areas: 3D integral imaging, digital holography, compressive sensing, optical encryption, and biomedical imaging. Recent Publications: Highlight innovations in underwater signal detection, lensless imaging for disease screening, and adversarial attack defense using optical systems. Technological Impact: Develops portable, low-cost medical diagnostic tools and augmented reality visualization systems. Collaborations: Works with interdisciplinary teams in biomedical engineering, computer science, and optical physics.

• 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.

• Robotics
• Mobile Robots
• Intelligent Vehicles
• +3 more

Urbano J. Nunes is a Full Professor at Coimbra University and Senior Researcher at the Institute for Systems and Robotics (ISR-UC) , where he coordinates the Human-Centered Mobile Robotics Lab . His career spans national and international funded projects in mobile robotics , intelligent vehicles , and human-machine interfaces , with over 160 publications in journals and conferences. He has supervised 13 completed PhD students and currently guides 4 more. Key Roles : IROS Advisory Committee (2013-), IEEE ITS Society Vice President (2011-2012), IEEE RAS Technical Committee Cochair (2006-2011) Editorial Leadership : Associate Editor for IEEE Transactions on Intelligent Vehicles (2015-), former Associate Editor for IEEE Transactions on Intelligent Transportation Systems (2007-2016) Research Interests focus on human-centered mobile robotics , including mobile service robotics , assistive robotics , autonomous vehicle navigation , pattern recognition , and machine learning . His recent work involves 3D LiDAR processing, multispectral imaging in agriculture, and brain-computer interface (BCI) reliability improvements. Scientific Awards : IEEE ITS Society Outstanding Service Award (2006) IEEE RAS Most Active Technical Committee Award (2006) NiSIS Competition Winner for Automotive Dataset Analysis (2007) Grant Leadership includes 33 funded projects from 2001 to 2025 across domains like digital agriculture (GreenBotics), green automotive innovation (GreenAuto), and telerehabilitation platforms (INPACT), with international collaborations spanning EU programs and Portugal’s FCT grants. Labs & Teams : Coordinates the Human-Centered Mobile Robotics Lab at ISR-UC, integrating cross-disciplinary teams in robotics, AI, and BCI research. His group partners with institutions like CERN and European Commission JRC in cybernetic transportation systems.

Mårten WadenbäckView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Mårten Wadenbäck is an Assistant Professor and Docent at Linköping University, affiliated with the Department of Electrical Engineering (ISY) and the Faculty of Science and Engineering. He is a member of the Computer Vision Laboratory (CVL) and actively participates in the Wallenberg Autonomous Systems Program (WASP), a prestigious research initiative in Sweden. His research lies at the intersection of computer vision and machine learning, with a focus on robust and invariant feature representations. Key areas include keypoint detection, dense feature matching, and geometric deep learning for 3D vision. His work often explores symmetry-invariant and equivariant neural architectures, particularly for point cloud and hyperspherical data. The recent publications demonstrate a strong trend in developing learning-based methods for local and dense image correspondence, with increasing emphasis on robustness, invariance, and efficiency. His contributions frequently appear in top-tier venues such as CVPR and 3DV, reflecting high impact in the computer vision community. While no scientific awards are listed in the provided text, his involvement in WASP indicates recognition and competitive funding. There is no information available about student advising or external grants in the current data. Mårten Wadenbäck conducts his research within the Computer Vision Laboratory (CVL) at Linköping University, a team focused on advancing core methodologies in visual perception and machine learning, particularly for autonomous systems.

• 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.

• Digital Photogrammetry
• Computer Vision
• Geospatial Imaging
• +10 more

Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.