Find Academic Supervisors

• Medical Imaging
• Machine Learning
• Temporal Data Analysis
• +3 more

Svitlana Zinger is a Full Professor at Eindhoven University of Technology (TU/e), specializing in context-informed dynamic image analysis for clinical decision support. She leads research in medical image/video analysis for diagnosis/prognosis, with expertise in temporal data analysis, machine learning, and multimodal data processing. Her affiliations include the Center for Care & Cure Technology, Eindhoven MedTech Innovation Center (e/MTIC), NeuroPlatform, and EAISI Health. She holds a PhD from the École Nationale Supérieure des Télécommunications, France, and has extensive postdoctoral experience in image ontology and handwritten document analysis. Academic background: MSc in Computer Science (Dnepropetrovsk State University, Ukraine, 2000), PhD in 3D Data Interpolation (2004). Research focuses on neuroimaging, cardiovascular monitoring, and AI-driven medical decision support. Key projects include FORSEE (video-based adverse event detection), NEUROTREND (fMRI biomarkers for depression), and STRATUM (neurosurgical 3D tools). Research interests span brain network dynamics in autism/depression, hyperspectral imaging for tumor delineation, and camera-based physiological monitoring. She teaches courses like 'Image Analysis for Healthcare Technologies' and 'Medical Image Processing'. Awards include 2nd place in the CAMELYON17 challenge for metastasis detection. Supervises 32 research projects, collaborating with clinical and industrial partners globally. Her labs include the Biomedical Diagnostics Lab and Signal Processing Systems group. Current work explores biomarkers for accelerated cognitive aging in epilepsy and AI-driven tools for neurosurgery. Active in international collaborations across Europe and beyond.

Ashish PandharipandeView profile

Researcher

• Signal Processing
• Machine Learning
• Automotive Radar Systems
• +5 more

Ashish Pandharipande is a University Researcher at Eindhoven University of Technology (TU/e), affiliated with the Department of Electrical Engineering. His career spans academic and industrial roles, including Technical Director at NXP Semiconductors since 2021 and prior positions at Signify, Philips, and Samsung Advanced Institute of Technology. He holds a PhD in Electrical Engineering from the University of Iowa and conducted postdoctoral research at the University of Florida. Current Role: Technical Director, Innovation at NXP Semiconductors Past Academic Roles: Senior Researcher, Senior Scientist, Lead R&D Engineer His research focuses on signal processing , machine learning , and automotive radar systems , with a strong emphasis on semantic communication , LiDAR-radar fusion , and energy-efficient sensing . Recent work explores sparsity-aware algorithms, interference mitigation, and reconfigurable sensing frameworks for autonomous driving and IoT applications. The 15 most recent publications highlight advancements in automotive radar (e.g., MIMO systems, spoofing detection), semantic communication (e.g., on-demand forwarding, interference cancellation), and smart city applications (e.g., lighting control, RIS-assisted systems). Trends include integration of vision sensors for radar point cloud estimation and deep unfolding for sparse signal recovery. Ashish has supervised 7 students and has over 350 research outputs. His work contributes to UN Sustainable Development Goals in smart cities and energy efficiency .

• Computer Vision
• Sports Analytics
• Machine Learning
• +3 more

Mehrnaz Fani is an Adjunct Assistant Professor specializing in computer vision and sports analytics. Her research focuses on developing advanced algorithms for player tracking, rink localization, and event recognition in sports videos, particularly in ice hockey and soccer. She employs deep learning techniques and multi-task learning frameworks to enhance video analysis accuracy. Her work emphasizes applications such as puck localization, jersey number recognition, and pose estimation of athletes. Fani's contributions bridge the gap between computer vision and sports science, enabling automated analysis of broadcast sports content. Notable research trends include the use of neural networks (e.g., Hourglass, BendedNet) for geometric and pose estimation tasks, as well as parallel feature fusion for video structure analysis. Her methodologies often integrate temporal analysis and data augmentation to improve model robustness. Despite no listed awards, her publications reflect a strong focus on practical sports video analysis challenges. She has advised no documented students and no grants are mentioned. Her work is part of a broader effort to automate sports data extraction and enhance fan engagement through advanced video processing.

Mamoun T. MardiniView profile

Assistant Professor

• Data Science
• Wearables
• Health Informatics
• +3 more

Mamoun T. Mardini is an Assistant Professor at the Institute on Aging , University of Florida. His research focuses on leveraging data science, wearable technologies, and machine learning to address challenges in gerontology and healthcare. Education : Ph.D. Computer Science (2018), University of North Carolina M.S. Computer Engineering (2013), American University of Sharjah B.S. Computer Engineering (2009), Jordan University of Science and Technology Research Interests : Dr. Mardini’s work emphasizes: Development of AI-driven frailty assessment tools for surgical patients Applications of smart wearables for monitoring mobility and pain in older adults Machine learning models for predicting cardiovascular outcomes and disparities Analysis of social determinants of health using real-world data Key Contributions : His studies include: Pioneering work on reducing unnecessary TEE procedures using AI Development of the ROAMM smartwatch platform for ecological momentary assessment Investigations into racial disparities in healthcare outcomes using ML Awards : No awards explicitly mentioned in the provided data. Labs/Teams : Active in the Division of Epidemiology and Data Science in Gerontology (EDGE) .

• Ultrasound Physics
• Bubbles Physics
• Contrast Physics
• +5 more

Guillaume P.R. Lajoinie serves as Associate Professor across multiple institutes at the University of Twente: Digital Society Institute, MESA+ Institute, Physics of Fluids research group, and TechMed Centre. His interdisciplinary work bridges physics, engineering, and medical applications with emphasis on ultrasound technologies and microbubble dynamics for therapeutic and diagnostic purposes. His research profile is dominated by Ultrasound Physics (100% fingerprint match) and Bubbles Physics (85%), with significant contributions to Contrast Physics (42%), Acoustics Physics (37%), and Droplet Physics (32%). Current investigations focus on microbubble stability for drug delivery systems, immunogenic cell death mechanisms in cancer therapy, and computational simulation of ultrasound phenomena through projects like the PROTEUS simulator. His work consistently integrates fluid dynamics with medical imaging applications. 2025 publications reveal accelerating output in ultrasound simulation, microbubble engineering, and cardiovascular imaging techniques. Key trends include computational modeling of contrast agents, attenuation correction algorithms for plaque imaging, and physical cancer therapy-immunotherapy synergies. His research maintains strong clinical translation focus through TechMed Centre collaborations. Lajoinie has supervised 5 graduate students and maintains active research leadership through the Physics of Fluids group. His work demonstrates consistent growth in publication output since 2012, with 2025 showing peak productivity (9 outputs). He leverages cross-institute resources at University of Twente for experimental and computational approaches to biomedical ultrasound problems. Laboratory work centers on the Physics of Fluids experimental facilities with extensions to TechMed Centre's medical technology platforms. Current team efforts focus on microbubble production systems, ultrasound simulation frameworks, and tumor-on-a-chip models for therapy testing, reflecting his fingerprint's emphasis on Combination Therapy Medicine and In Vitro systems.

Tessio NovackView profile

Assistant Professor

• GIScience
• Critical GIS
• Volunteered Geographic Information
• +3 more

Tessio Novack is an Assistant Professor at the University of Warwick's Centre for Interdisciplinary Methodologies (CIM), where he convenes the Urban Analytics and Visualisation degree program. Previously, he was a postdoctoral researcher at Heidelberg University's GIScience Research Group and completed his PhD in urban remote sensing at the Technical University of Munich under a DAAD scholarship. His research focuses on theoretical and practical approaches to culture-bound spatial knowledges, leveraging digital data, computational methods, and GIS to represent alternative urban spatial perceptions for social change. Education includes a PhD in Urban Remote Sensing (Technical University of Munich, Germany, 2018) and a DAAD-funded scholarship. He also holds a postdoctoral background in GIScience from Heidelberg University (Germany). Research interests span GIScience, critical GIS, volunteered geographic information, citizen science, and urban geography. He teaches modules such as IM913 Spatial Methods and Practices in Urban Science, IM927 Digital Cities: Interdisciplinary Perspectives, and IM939 Data Science Across Disciplines. His publications emphasize urban spatial analysis, geoinformation systems, and interdisciplinary applications of GIS. He advises no listed students but contributes to interdisciplinary teams and research projects at CIM.

• Artificial Intelligence
• Computer Vision
• Human-Computer Interaction
• +15 more

Dr. Stuart James is an Assistant Professor in Visual Computing at Durham University's Department of Computer Science, with a research focus on Visual Reasoning and Spatial Reasoning. His work bridges 2D/3D scene understanding with applications in Cultural Heritage, Digital Humanities, and Assistive Technologies. Key projects include EU-funded initiatives RePAIR (FET), DCitizens (Twinning), and BoSS (Lighthouse). Previous roles include Researcher/PostDoc positions at Istituto Italiano di Tecnologia (IIT) and University College London (UCL), where he earned his PhD in visual information retrieval for sketches from the University of Surrey. PhD in Visual Information Retrieval (University of Surrey, 2015) hBSc in Computer Science with Games Development (University of Hull, 2009) Research interests span Artificial Intelligence, Computer Vision, Human-Computer Interaction, and Robotics, with emphasis on Sketch-based Retrieval , Scene Graphs , Visual Question Answering , and Knowledge Retrieval & Reasoning . The VIViD research group at Durham drives innovations in neuro-symbolic reasoning for cultural analytics. Publications highlight trends in 3D scene understanding, cultural heritage digitization, and AI-driven storytelling frameworks. Scientific accolades include: CVPR 2025 Outstanding Reviewer ICIAP 2019 Best Student Paper IPAS 2018 Best Paper Current projects involve EU grants totaling €10.6 million across RePAIR, DCitizens, and BoSS. Collaborations include IIT (External Scientist), UCL (Honorary Associate), and ITI/LARSyS (International Collaborator). The lab recruits PhD students and organizes VISART workshops, while also advertising KTP Associate positions in Machine Learning for railway monitoring.

• Pattern Recognition
• Artificial Neural Networks
• Expert Systems
• +7 more

Maher Ahmed is an Associate Professor at the Faculty of Science, Wilfrid Laurier University. His research focuses on pattern recognition, artificial neural networks, and expert systems. He has contributed to diverse fields including robotics, medical informatics, network security, and computer vision. Key research interests include developing algorithms for shape representation, improving network anomaly detection, and applying machine learning to financial fraud prevention. His work bridges theoretical computer science with practical applications in healthcare and autonomous systems. Publications span robotics localization, medical literature reviews, encryption techniques, and sign language recognition, reflecting a cross-disciplinary approach. He currently holds no listed awards but maintains an active research agenda with a focus on applied artificial intelligence.

Abdel-Hamid SolimanView profile

Professor

• Automation (Smart Cities, Energy Management)
• Fibre Optic Communication Systems
• Data Acquisition Systems
• +5 more

Abdel-Hamid Soliman is a Professor in the Department of Digital, Tech, Innovation & Business at Staffordshire University, UK. His academic roles include teaching, research leadership, and industrial collaboration. He holds a PhD in Image Processing and multiple postgraduate certifications in research supervision and methods. Education Background: PhD in Image Processing MSc in Information Systems and Data Acquisition Systems BEng (Hons) Distinction in Electronics and Communications PGC in Research Supervision PGC in Research Methods Research focuses on bridging academia and industry through automation technologies (Smart Cities, Energy Management, Safety Systems), fibre optics, IoT, and advanced signal/image processing. He leads EU-funded projects like CERES-TEMPUS, PEACE Erasmus Mundus, and WatERP-FP7. His work emphasizes community impact through innovation. Industrial Collaboration: Consultant for oil companies (VIBA, AGIP, etc.) Co-founder of ICentrica Ltd (spin-out company) Delivered projects for Sturge Industries, Universal Systems, and Direct Radio Response Grants & Partnerships: Secured EU funding via FP7 and Erasmus Mundus programs National/international partnerships in North Africa/Middle East Labs/Teams: Involved with Staffordshire's research groups and ICentrica Ltd's innovation activities. Active in training programs for industry professionals and university collaborations.

Daniel CremersView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +10 more

Prof. Daniel Cremers is a Professor of Informatics and Mathematics at the Technical University of Munich (TUM), leading the Chair of Computer Vision & Artificial Intelligence within the TUM School of Computation, Information and Technology. His work spans computer vision, machine learning, and mathematical image analysis, with contributions to autonomous systems, SLAM, and self-driving cars. Affiliations: Chair of Computer Vision & AI, TUM School of Computation, TUM. Education: Bachelor's in Mathematics and Physics (University of Heidelberg), Master's in Theoretical Physics (University of Heidelberg), PhD in Computer Science (University of Mannheim). Research Interests: Focuses on computer vision fundamentals, deep learning, variational methods, and real-world applications like autonomous systems. Key areas include 3D reconstruction, SLAM, and geometric processing. Recent Contributions: Over 12 papers accepted to CVPR 2025, including work on probabilistic bundle adjustment and dynamic scene reconstruction. Notable achievements include the ECCV 2024 Koenderink Award for LSD SLAM and the Leibniz Prize (2016). Awards: Leibniz Prize, ERC grants (Starting, Consolidator, Advanced), multiple best paper awards. Teaching: Offers courses like Computer Vision II: Multiple View Geometry and Introduction to Deep Learning. Supervises practical courses on deep learning methods and autonomous systems. Labs/Teams: Leads the Computer Vision Group, actively organizing conferences (e.g., GCPR/VMV 2024) and contributing to open datasets like DeepScenario. Engages in industry through startups and advisory roles.

Jeff W. LichtmanView profile

Professor

• Neuroscience
• Connectomics
• Synaptic Development
• +3 more

Jeff W. Lichtman is the Jeremy R. Knowles Professor of Molecular and Cellular Biology and Santiago Ramón y Cajal Professor of Arts and Sciences at Harvard University. He leads the Lichtman Lab, focusing on neural circuit development, synaptic plasticity, and connectomics. His research combines advanced imaging techniques like Brainbow and electron microscopy to map neural connections, aiming to understand how experience shapes brain circuits. Notable contributions include the 'connectome' initiative, analogous to the Human Genome Project, and developing technologies such as the Automated Tape-Collecting Lathe Ultramicrotome (ATLUM) for high-resolution neural mapping. Education: AB from Bowdoin College (1973), MD and PhD from Washington University (1980). Formerly at Washington University for 30 years before joining Harvard in 2004. Leadership roles include Dean of Science in the Faculty of Arts and Sciences (2023–present), director of the Harvard Center for Biological Imaging, and steering committee member of the Center for Brain Science. His work bridges neuroscience, computer science, and engineering, with collaborations on projects like the zebrafish brain imaging and octopus connectomics. Research focuses on synaptic competition in development, axonal regeneration, and the structural basis of memory. Key projects include reconstructing the human cerebral cortex at nanoscale resolution and studying mental illness through neural circuit analysis. Over 200 publications span neuroimaging, synaptic plasticity, and technological advancements in microscopy.

Sue WhitesidesView profile

Professor

• Algorithms
• Discrete Mathematics
• Computational Geometry
• +5 more

Sue Whitesides is a Professor and Chair of the Department of Computer Science at the University of Victoria, British Columbia. Previously, she held roles at McGill University's School of Computer Science, including directing its discrete mathematics and computational geometry research groups. Her research focuses on algorithms, discrete mathematics, computational geometry, motion planning, and graph layout, with applications in brain imaging and nanofabrication. She co-authored a textbook on discrete mathematics for computer science and organizes annual workshops at the Bellairs Research Institute, collaborating with institutions like INRIA-Lorraine. Her work bridges theoretical computer science with interdisciplinary applications in robotics, materials science, and biomedical imaging. Education: No formal education details provided. Research Interests: Algorithms, computational geometry, motion planning, graph drawing, discrete mathematics. She explores geometric coverage problems, self-assembly mechanisms, and algorithmic solutions for robotics and nanofabrication. Her recent work includes 3D surface assembly and minimum-distance robot localization. Publications: Over 15 key papers in computational geometry and algorithms, including studies on geometric separation in polygonal environments and randomized localization algorithms. Her work appears in venues like the ACM Symposium on Computational Geometry and the Journal of the American Chemical Society. Collaborations: Leads workshops at Bellairs with international partners like INRIA, focusing on collaborative problem-solving in geometry and computer graphics. Co-organizes events with researchers like Sylvain Lazard and Hazel Everett. Labs/Teams: Computational geometry lab at McGill collaborating with INRIA-Lorraine. Active in McGill's discrete mathematics and algorithms seminars.

• Biomedical Imaging
• Optical Coherence Tomography
• Ophthalmic Diagnostics
• +2 more

Myeong Jin Ju is Assistant Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, with joint appointment in the School of Biomedical Engineering. Research focuses on developing advanced optical imaging technologies for vision science applications. Laboratory specializes in optical coherence tomography (OCT) systems design, signal processing algorithms, and multi-modal imaging integration. Current work aims to improve clinical ophthalmic diagnostics through high-resolution retinal and choroidal visualization, automated pathology quantification, and functional assessment techniques. Applications target diabetic retinopathy, age-related macular degeneration, and inherited retinal diseases. Recent innovations include MHz-range OCT systems, polarization-diversity techniques, and computational approaches for motion correction and image enhancement. Research bridges engineering innovation with preclinical and clinical translation in vision science.

Marcelo H Ang JrView profile

Professor

• Robotics
• Mechatronics
• Intelligent Systems
• +4 more

Marcelo H. Ang Jr. is a Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), Faculty of Engineering. He serves as Director of the Advanced Robotics Centre at NUS and has been actively contributing to robotics research since joining NUS in 1989. Education: Ph.D. in Electrical Engineering, University of Rochester, NY (1988) M.Sc. in Electrical Engineering, University of Rochester, NY (1986) M.Sc. in Mechanical Engineering, University of Hawaii (1985) B.Sc. in Mechanical Engineering and Industrial Management Engineering, De La Salle University, Philippines (1981) Professor Ang's research spans robotics, mechatronics, and intelligent systems with particular expertise in mobile robotics, robot control systems, and intelligent control methodologies. His work consistently bridges theoretical foundations with practical industrial applications, especially in manufacturing automation. His research interests evolved from fundamental control theory to complex integrated systems involving multiple robots and sensor networks. His publication record shows a strong emphasis on solving practical robotics challenges, with increasing focus on vision-based systems, mobile manipulation, and multi-robot coordination in recent years. The research demonstrates progression from single robot systems to complex networked robotic systems. Scientific Awards: Awards for Excellence 2000, for Most Outstanding Paper in 1999 Volume (for "A Walk-Through Programmed Robot for Welding in Shipyards") Professor Ang has supervised numerous graduate students including PhD candidates like Zheng Liu who worked on multi-robot surveillance systems. His research has been supported by significant grants including "Integration of Solid Modeling Systems and Robot Controller Architectures Towards Intelligent Robotic Systems" ($267,150), "Management of Manufacturing Technologies" ($33,000), and "Research and Development of a Ship-Welding Robot" ($1,651,200 plus $60,000). He leads the robotics group at NUS involved in the Robust Mobile Manipulation project with SIMTech, focusing on developing advanced robotic systems for unstructured environments. Professor Ang is also the founding chairman of the Singapore Robotic Games, demonstrating his commitment to robotics education and outreach.

Claudio SilvaView profile

Professor

• Visualization and Graphics
• Geometric Computing
• Data Science
• +2 more

Claudio Silva is an Institute Professor of Computer Science and Engineering at NYU Tandon School of Engineering and Professor of Data Science at NYU Center for Data Science. He co-founded the Center for Urban Science and Progress (CUSP) and the Visualization, Imaging and Data Analysis (VIDA) Center. His academic journey includes a BS in mathematics from Universidade Federal do Ceará (1990) and PhD in computer science from SUNY Stony Brook (1996). Co-Director, VIDA Center Former Professor, University of Utah (2003-2011) Technology Consultant, MLB Advanced Media (2012-2017) Research Interests : At the intersection of visualization, geometric computing, and urban/sports analytics, his work includes: AR task guidance systems (ARGUS, HuBar) Urban data analysis (3D visualization, sidewalk mapping) LLM multimodal reasoning (POEM, BDIViz) Biomedical data tools (TopoMap++, T-Explainer) 2023-2025 Research Trends : Recent publications focus on neuroadaptive AR systems (AdaptiveCoPilot), LLM prompt optimization (POEM), explainable AI frameworks (T-Explainer), and urban data visualization (Urban Rhapsody). Collaborations span NYU, Northrop Grumman, and NSF/DARPA grants for projects like OSCUR and PTG. Scientific Honors : ACM Fellow (2024) IEEE Visualization Academy (2019) IEEE Technical Achievement Award (2014) Technology Emmy for Statcast (2018) His lab has developed open-source tools like ARGUS on GitHub and pioneered methods in fNIRS-based cognitive workload analysis for pilot training systems. Current projects include NASA-funded OpenSpace and NSF-backed OSCUR.