Jui-Kai Wang (Ray) is an Assistant Professor in the Department of Ophthalmology at UT Southwestern, joining in 2024. His research focuses on ophthalmic image analysis using machine learning and deep learning techniques across modalities like OCT, OCTA, LSFG, and color fundus photography. PhD in Electrical and Computer Engineering (2016), The University of Iowa MS in Computer and Communication Engineering, National Cheng Kung University BS in Electronic Engineering, Southern Taiwan University of Science and Technology Research spans ocular disease diagnosis , neurodegenerative imaging , and radiation therapy effects , leveraging multimodal imaging and advanced analytics. Recent work includes OCT layer segmentation, papilledema classification, and vascular biomarker discovery. Scientific contributions recognized through the Xtreme Research Award (Heidelberg Engineering, 2021) and ARVO travel grant (2019). Collaborative projects include NIH-funded research on glaucoma and radiation-induced vision loss.
Prof. Gülgün Fatma ŞENGÖR is a leading academic at Istanbul University's Faculty of Aquatic Sciences , Department of Fisheries and Seafood Processing Technology. With a career spanning over three decades, she has held academic positions from Assistant Professor (1998) to her current Professor status (2021). Her research focuses on seafood safety, nanotechnology applications in food preservation, and sensory/chemical quality assessment . Doctorate (1995): Ege University, Fishery Processing Technology Postgraduate (1991): Ege University, Fishery Processing Technology Undergraduate (1987): Ege University, Fishery Processing Technology Her work explores nanofiber coatings, sous-vide cooking, and time-temperature indicators to enhance seafood shelf life and safety. She has published extensively in Journal of Food Safety , LWT-Food Science and Technology , and Turkish Journal of Fisheries . Awards include Elsevier Review Certificates and TÜBİTAK's Başarılı Araştırmacı Ödülü (2012). She supervises doctoral theses and consults on national/international research projects. Scientific Contributions: 16+ funded projects (2008-2025) Key role in EU Hygiene Standards implementation Editorial roles in journals like Acta Aquatica Turcica
Lee Smith is Professor of Public Health at Anglia Ruskin University (ARU), where he serves in the Faculty of Science and Engineering within the Department of Life Sciences. He is an epidemiologist with expertise in physical activity and sedentary behaviour, currently leading ARU's COVID-19 research group and serving as a member of the Cambridge Centre for Sport and Exercise Sciences (CCSES). Lee Smith's educational background includes: PhD in Epidemiology from the University of Cambridge MSc in Physical Activity and Health from Loughborough University BSc in Applied Sport Science from Loughborough University Professor Smith's research primarily focuses on physical activity promotion and sedentary behaviour reduction across the lifespan and within special populations. His work examines the relationship between physical activity and health outcomes, sedentary behaviour and health, and the role of physical activity in cancer survivorship. He has developed interventions using mobile phone apps to promote physical activity in cancer survivors and has investigated sedentary behaviour reduction techniques. His extensive publication record (over 1,170 publications) demonstrates a consistent focus on epidemiological studies related to physical activity and sedentary behavior. Recent work shows expansion into broader health topics including vaccine safety, mental health outcomes, and global health burden studies, while maintaining his core expertise in physical activity epidemiology. His research spans multiple methodologies including systematic reviews, meta-analyses, and large-scale epidemiological studies across diverse populations. Professor Smith actively supervises research students and has led multiple research grants in his field of expertise. His work has involved collaborations across multiple institutions and countries, contributing significantly to the understanding of physical activity's role in public health. As leader of ARU's COVID-19 research group and a member of the Cambridge Centre for Sport and Exercise Sciences, Professor Smith contributes to important interdisciplinary research initiatives that bridge epidemiology, public health, and exercise science.
Filippo Stanco is a Full Professor at the Department of Mathematics and Computer Science at the University of Catania. He serves as President of the Bachelor's Degree in Computer Science (since 2017) and Deputy Rector for Technological Innovation (since 2019). His career includes positions as Associate Professor (2014-2023), Assistant Professor (2006-2014), and research fellowships at the Universities of Catania and Trieste. Education: PhD in Computer Science (2003) from University of Catania Bachelor's Degree in Computer Science (1999, summa cum laude) from University of Catania His research spans image processing, computer vision, and machine learning with applications in cultural heritage preservation, medical imaging, industrial automation, and multimedia systems. He leads the Archeomatica Project developing digital tools for archaeology and coordinates multiple national research projects including DREAMIN and CLEAR. Stanco has published extensively on food image analysis, document authentication, video processing, and heritage conservation. His work frequently employs deep learning, 3D modeling, and color science techniques across interdisciplinary domains. Honors: Best Session Paper Award at IPAS 2020 Cookpad Student Travel Grant at CEA 2018 Meritorious Papers recognition in Computers in Biology and Medicine (2016) He coordinates the Archeomatica research group and serves on editorial boards for multiple journals including ACM Journal of Computing and Cultural Heritage and IET Image Processing. Stanco has supervised over 200 theses and teaches courses on multimedia systems, game development, and image processing.
Professor Ender Mete Ekşioğlu serves in the Department of Electrical and Electronics Engineering at Istanbul Technical University, where he has held academic positions since 1999. His current role as Professor began in 2019 following progression from Associate Professor (2013-2019) and Assistant Professor (2005-2012) ranks. PhD in Electronics and Communication Engineering, Istanbul Technical University (2000-2005) Degree from University of Michigan (1994-1997) Additional studies at University of Michigan (1997-1999) Professor Ekşioğlu's research spans Machine Learning, Deep Learning, and advanced Signal Processing with emphasis on medical imaging applications. His work bridges traditional signal processing techniques with modern deep learning approaches, particularly in Magnetic Resonance Image Reconstruction where he applies sparsity principles and neural network architectures. Key focus areas include image denoising, segmentation, and enhancement across medical, underwater, and atmospheric imaging contexts. His publication trends demonstrate consistent innovation in imaging inverse problems, with recent work integrating pixel-level processing with physical modeling in underwater imaging and developing topological awareness for medical image segmentation. The research shows strong continuity between traditional signal processing methods (like DCT) and cutting-edge deep learning architectures. Professor Ekşioğlu has led six significant research projects funded by TUBITAK and BAP, including 'Deep Learning in Image Processing Inverse Problems' and 'Parallel magnetic resonance imaging techniques and applications'. These projects have resulted in substantial research output with applications spanning medical diagnostics to remote sensing. h-index of 13 based on Scopus citations 2000+ total research outputs Active supervision of 27 theses in progress His laboratory work focuses on imaging inverse problems, with particular strength in MRI reconstruction techniques that combine deep learning with recursive algorithms. Current research directions include topological awareness in segmentation networks and hybrid approaches that integrate traditional signal processing with neural networks for improved robustness.
Hugo Raguet serves as an Associate Professor at INSA Centre-Val de Loire in Blois, France, with research affiliations at the Fundamental and Applied Computer Science Laboratory (jointly associated with University of Tours). His work bridges theoretical computer science and applied mathematics, focusing on algorithmic solutions for real-world computational challenges. His research expertise spans: Signal Processing: Developing efficient algorithms for real-time applications and medical imaging Optimization: Advancing convex and non-smooth optimization techniques with graph-based regularization Sensitivity Analysis: Pioneering targeted methods using dependence measures for domain-specific phenomena Image Processing: Extending raster-to-vector conversion to multicolor systems Publication analysis reveals consistent innovation in splitting methods (Douglas-Rachford, forward-backward) and preconditioning techniques, with strong emphasis on computational efficiency for graph-structured problems. His work frequently bridges mathematical theory with practical implementations in signal processing and machine learning. No scientific awards are documented in available sources. Student advising and grant information remain unspecified. Raguet actively contributes to the Fundamental and Applied Computer Science Laboratory, where he develops open-source tools for academic collaboration including live conferencing software and LaTeX templates for scientific communication.
David Alleysson is a permanent researcher at CNRS, affiliated with Grenoble-Alpes University. He co-directs the Vision & Emotion team at the Laboratoire de Psychologie et Neurosciences Cognitives (LPNC) and works on color vision, psychophysics, and digital image processing. Education : Diplôme d'Ingénieur Informatique (1994, Geneva), DEA informatique (1995, Grenoble), PhD (1999) under Jeanny Hérault at LIS laboratory. Research : Focuses on chromatic information processing in the human visual system and numerical modeling of color spaces. His work bridges physics of light and human color perception. His inventions include patents for color image reconstruction algorithms, chromatic compensation systems, and advanced CMOS sensor designs. He has contributed to fields like chromatic signal processing , computational imaging , and visual perception modeling . Teaching : Involved in L3 MIASHS, M2 Cognitive Sciences, and 3A ISAE programs. Labs : Co-director of the Vision & Emotion team at LPNC laboratory.
Scott Tyo is an accomplished electrical engineering professor currently serving as an Adjunct Professor at UNSW Canberra's School of Engineering and Technology. He joined UNSW Canberra in 2015 as Head of the School and Professor of Electrical Engineering, following previous appointments as a professor at the University of Arizona's College of Optical Sciences (2006-2015) and the University of New Mexico (2001-2006). His academic journey began with a PhD from the University of Pennsylvania in 1997, after which he served in the US Air Force working on High Power Microwave Systems and Space-Based Remote Sensing, including military faculty service at the US Naval Postgraduate School from 1999-2001. Professor Tyo's research spans polarimetry, antenna design, and remote sensing applications. His work began with underwater imaging systems based on differential polarimetry, where he established foundational research in polarimeter optimization. His career evolved to include significant contributions to ultra wideband and high-power microwave antennas, particularly through collaborations with Dr. Carl Baum at AFRL and later with Prof. Rick Ziolkowski at Arizona on metamaterial-inspired electrically small antennas. More recently, his research has expanded into tropical cyclone analysis using satellite remote sensing techniques. His work demonstrates remarkable interdisciplinary reach, connecting optical engineering with atmospheric science. Analysis of his recent publications (2020-2025) reveals three primary research thrusts: 1) Advanced polarimetry techniques including scene-adaptive imaging systems and multi-harmonic reconstruction methods; 2) High-power microwave engineering with focus on cascaded oscillators and electrically small antenna arrays; and 3) Tropical cyclone analysis through satellite remote sensing of cloud radiative effects. These areas reflect his ability to bridge fundamental optical engineering with practical applications in defense and climate science. Professor Tyo has maintained an exceptionally active publication record spanning over 25 years, with continuous contributions through 2025. His work shows strong international collaboration, with co-authors from multiple countries and institutions. His research has practical applications in defense systems, remote sensing technologies, and climate monitoring, demonstrating both theoretical depth and real-world impact.
Andrew Stockman is the Steers Professor of Investigative Eye Research at the UCL Institute of Ophthalmology and an Honorary Consultant at Moorfields Eye Hospital since 2004. His career spans 17 years at UCSD and over 20 years at UCL, where he leads the Visual Neuroscience and Function theme and directs the Colour & Vision Research Laboratories. PhD, University of Cambridge (1984) MA, University of Oxford (1984) BA, University of Oxford (1979) His research focuses on visual psychophysics, including color vision, rod vision, visual adaptation, and clinical vision. Notable contributions include the Stockman & Sharpe cone fundamentals adopted by CIE standards, flicker interaction studies, and clinical research linking molecular genetics to visual dysfunction. Recent publications (2024-2017) span color science, visual adaptation, and clinical applications. Key themes include individual variability in cone spectral sensitivities, temporal vision dynamics, and genotype-phenotype correlations in optic neuropathies. Fellow, Optical Society of America NATO/SERC Postdoctoral Fellowship Stockman has supervised numerous PhD and MSc students while teaching courses like "Advanced Visual Neuroscience" and "Visual Neuroscience" at UCL. His lab maintains the CVRL database, a critical resource for vision scientists.
Prof. Adrian Evans serves as Deputy Head of Department in the Department of Electronic & Electrical Engineering at the University of Bath, where he leads research within the Electronics Materials, Circuits & Systems Research Unit (EMaCS) and The Foundry: Centre for Digital, Manufacturing & Design. His academic profile demonstrates active engagement in doctoral supervision and cutting-edge research across multiple domains of image processing and biometrics. His primary research focuses on biometrics—particularly 3D face recognition techniques resilient to facial expressions—and advanced colour/multispectral image processing. He has pioneered methods for colour edge detection, nonlinear filtering, and scale-space sieve-based segmentation. His motion estimation work specializes in analyzing non-rigid bodies like clouds and glaciers, while his nonlinear image processing research centers on mathematical morphological sieves and granulometric texture analysis. Recent publication trends (2021-2025) reveal a strategic shift toward computer vision applications in transportation (multi-camera vehicle tracking systems) and healthcare (non-contact cardiorespiratory monitoring). His work increasingly integrates radar technology with computer vision for vital sign detection while maintaining foundational contributions to mathematical morphology and image enhancement techniques. No major scientific awards are documented in the provided materials. His supervisory record includes 11 doctoral students, with current openness to new PhD candidates. Research funding spans UK government and industry collaborations including EPSRC projects like High Speed 4K Video Transmission (2016-2018) and multiple KTP partnerships with Seiche Measurements Limited and Navtech Radar Limited. He operates within Bath's EMaCS research unit and The Foundry center, leveraging these interdisciplinary environments for digital manufacturing and design innovation. His work directly supports UN Sustainable Development Goals through applications in smart transportation systems and healthcare technology development.
Alice POREBSKI is an active Associate Professor (Maître de Conférences) at Université du Littoral Côte d'Opale, where she is affiliated with LISIC (Laboratoire d'Informatique Signal et Image de la Côte d'Opale). Her research focuses on computer vision and image processing with particular emphasis on texture analysis and classification techniques across color and hyperspectral imaging domains. Dr. POREBSKI's research interests center on feature selection methodologies for texture classification, with extensive work on Local Binary Patterns (LBP), histogram analysis, and multi-color space approaches. Her recent work has evolved toward environmental applications, particularly the detection and classification of marine plastic debris using hyperspectral imaging systems mounted on aquatic drones. She has developed specialized CNN architectures optimized for spectral-spatial feature extraction in environmental monitoring contexts. Her research bridges fundamental computer vision techniques with practical environmental applications, demonstrating how texture analysis methods can address real-world pollution challenges. Her publication record shows a clear progression from foundational texture analysis techniques to applied environmental monitoring systems. Early work focused on optimizing LBP parameters and multi-color space analysis for standard texture classification tasks. More recently, her research has centered on hyperspectral imaging for marine pollution detection, with significant contributions to benchmark datasets and compact feature representation methods specifically designed for drone-based remote sensing applications. This evolution demonstrates both technical depth in image processing and strategic application to pressing environmental issues. Dr. POREBSKI has made substantial contributions to the development of feature selection methodologies that enable efficient texture classification while reducing computational complexity. Her work on clustering-based sequential selection approaches and novel LBP variants has advanced the field of texture analysis, particularly for high-dimensional data like hyperspectral imagery. Through collaborations with researchers including Nicolas Vandenbroucke and Adam El Bergui, she has developed complete systems from theoretical foundations to practical implementations for environmental monitoring.
Dr. Jenny Bosten is a Research Fellow in Neuroscience at Gonville and Caius College, affiliated with the Department of Psychology at the University of Cambridge. Her academic work bridges psychological and neuroscientific approaches to understanding visual perception. Her research interests focus on the neural mechanisms underlying color vision and visual processing. As a member of Cambridge's neuroscience community, she contributes to the vibrant research environment in sensory neuroscience and psychophysics. Her work appears to emphasize experimental approaches to understanding how humans perceive and interpret visual information. Dr. Bosten collaborates with Professor John Mollon, a leading expert in visual neuroscience, suggesting her research has significant connections to fundamental questions in vision science. While specific details of her projects are not provided in the available text, her position within Cambridge's Department of Psychology indicates active engagement in both theoretical and experimental neuroscience research.
Ana Maria Garcia Nolasco da Silva is an Adjunct Professor in the Department of Arts at the School of Education, Polytechnic Institute of Lisbon. Holding a Ph.D. in Aesthetics and Philosophy of Art from the University of Lisbon, she specializes in the cultural intersections between Africa and Portugal within art, design, and craftsmanship contexts. Her academic credentials include: Ph.D. in Aesthetics and Philosophy of Art, Faculty of Letters, University of Lisbon Master's in Aesthetics and Philosophy of Art, Faculty of Letters, University of Lisbon Bachelor's in Plastic Arts - Painting, Faculty of Fine Arts, University of Lisbon Her research centers on African-Portuguese cultural synergies with emphasis on Lusophone Macaronesian islands (Azores, Madeira, Cape Verde, São Tomé and Príncipe) and migration-driven cultural constellations in continental Portugal. She investigates how artisanal traditions, political gestures, and feminist perspectives manifest in contemporary artistic practices, particularly through collaborative projects and biennials in Lusophone Africa. Her work bridges visual anthropology, postcolonial theory, and sensory studies to examine haptic visuality and hybrid identities. Analysis of her 15 most recent publications reveals dominant trends in decolonial art practices, with recurring focus on São Tomé and Príncipe's cultural events, Catarina Branco's artisanal reinterpretations, and René Tavares' performance works. Her scholarship consistently explores how migration reshapes creative expression across the Lusophone Atlantic, emphasizing community-based art and feminist interventions in postcolonial contexts. No scientific awards were documented in the source material. While specific student supervision isn't listed, her academic leadership includes chairing the 2017 "Rhizomes" International Conference and co-editing conference proceedings. Her research is supported by institutional affiliations rather than externally documented grants, with active participation in international conferences across Portugal, Spain, Argentina, and Germany. She engages with collaborative networks through conference organization and cross-institutional projects, though dedicated labs or permanent research teams aren't specified in available materials.
Jennifer Dionne is an Associate Professor of Materials Science and Engineering at Stanford University . She leads a multidisciplinary research group focused on controlling light-matter interactions at the nanoscale. Current faculty member Research spans nanophotonics, plasmonics, and biophotonics Research interests include: Biophotonics for label-free molecular-to-cellular disease detection Active nanophotonics with electro-optic tunability Plasmonics-enabled sustainable chemical catalysis Extreme energy-shifting nanoparticles (upconversion/scintillation) Quantum computing applications through light control Publication trends demonstrate expertise in plasmonic materials, surface plasmon polaritons, and metal-insulator-metal structures. Her work develops innovative nanophotonic devices for biomedical and energy applications while advancing fundamental understanding of light-matter interactions. Labs & teams : The Dionne Group comprises materials scientists, chemists, physicists, synthetic biologists, and engineers from multiple engineering disciplines collaborating toward a shared vision of transformative light-based technologies.
Dr. Bilgi Görkem Yazgaç is a Research Assistant at Istanbul Technical University, Department of Electrical-Electronics. His research focuses on Circuits and Systems Theory, Artificial Intelligence, and Audio and Speech Processing, with a strong emphasis on Embedded Systems and Fractional-Order Calculus applications. PhD in Electronic Engineering (2017), Istanbul Technical University Master's in Electronics Engineering (2014), Istanbul Technical University Licence in Electronics Engineering (2009), Istanbul Technical University His work spans interdisciplinary areas such as agricultural monitoring, where he developed embedded systems for pest detection and wheat seed classification, and signal processing for speech and environmental sound analysis using Fractional-Order Calculus. He has contributed to 8 research outputs, including conference proceedings and journal articles, with an h-index of 4 and over 40 citations. Key research fields: Embedded Systems, Fractional-Order Calculus, Agricultural Monitoring, Speech Processing, Artificial Intelligence, Circuits Theory
