Jenny Bosten is an Associate Professor in Psychology at the School of Psychology, University of Sussex . Her research focuses on visual perception, particularly color vision and individual differences, using neuroimaging (fMRI, EEG) and psychophysics. ERC-funded project COLOURCODE (2020-2025) on cortical color representation Studied genetics of visual trait variation and anomalous trichromacy Collaborated with institutions including UC San Diego and University of Cambridge Her work explores how color perception is shaped by natural scene statistics and genetic factors. Publications address cortical encoding mechanisms, visual enhancement technologies, and ecological approaches to perception. Current teaching includes advanced neuroscience courses and supervision of MSc research projects. Professional roles include membership in the International Colour Vision Society and former Secretary of the Colour Group (Great Britain).
Dr. Israel Abramov is a Professor of Psychology at Brooklyn College and holds joint appointments at the CUNY Graduate Center in Cognition, Brain and Behavior and Biopsychology and Behavioral Neuroscience . His research focuses on color vision , visual perception , and infant visual development , with significant contributions to understanding gender differences in vision , retinal ganglion cell properties , and applications in museum lighting . Key educational milestones include an LL.B. in Law (1959) and B.A. in Psychology (1961) from University College London, followed by a Ph.D. in Physiological Psychology (1967) from Indiana University Bloomington. His academic career spans postdoctoral work in biophysics at Johns Hopkins and faculty roles at Rockefeller University and Brooklyn College since 1973. His 15 most recent articles (2012-2003) reveal a focus on color vision mechanisms , peripheral retinal function , and visual developmental disorders like Down Syndrome. Collaborative work with J. Gordon and others explores museum lighting optimization , infant ocular motor control , and chromatic adaptation in both healthy and clinical populations. Lab affiliations include interdisciplinary collaborations with CUNY Graduate Center researchers and institutions like SUNY College of Optometry . While no formal scientific awards are documented in the provided text, his publications in high-impact journals and book chapters underscore his influence in visual neuroscience.
Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.
Dr. Yingzi Lin is a Professor and Chair of Mechanical and Industrial Engineering at Northeastern University, Boston, MA. She directs the Intelligent Human-Machine Systems (IHMS) Laboratory and specializes in human-machine systems, biosensing, and human factors in healthcare and transportation safety. Her research is funded by NSF, NIH, NIST, and industry partners like GM and Bose. Education: PhD (2004), Mechanical Engineering from the University of Saskatchewan. Research Interests: Includes human-robot interaction, driver-vehicle systems, patient safety, and multimodal pain assessment. She develops technologies like the COMPASS system for objective pain measurement and cognition-driven navigation tools for firefighters. Key Grants: Principal Investigator for NSF-funded projects on pain assessment and Co-PI for NIH-funded VR-based stroke recovery studies. Collaborates on NIST initiatives for firefighter spatial systems. Awards: NSF CAREER Award (2010), NSERC UFA (2004), IEEE Computational Intelligence Society Outstanding Paper Award (2013), and 2023 Excellence in Mentoring Award. Labs & Teams: Leads the IHMS Lab, focuses on human-technology integration and robotics. Engages in interdisciplinary collaborations through Northeastern's Institute for Experiential AI and Experiential Engineering Education.
Mohamed Najim is a Professor at IMS Bordeaux (Laboratoire de l'intégration, du matériau au système) affiliated with the University of Bordeaux. He is a member of the Signal and Image Processing research group within the MOTIVE team, where he conducts cutting-edge research in multidimensional signal processing and image analysis. His work spans theoretical developments in signal modeling and practical applications in speech enhancement, image colorization, and communication systems. Professor Najim's research interests focus on advanced signal processing techniques, with particular expertise in autoregressive modeling, Kalman filtering, generative adversarial networks, and multidimensional system analysis. His work bridges theoretical signal processing with practical applications in image processing, speech enhancement, and wireless communications. He has made significant contributions to the development of novel algorithms for texture analysis, channel modeling, and noise reduction in various signal processing contexts. The analysis of his publication record spanning over 25 years reveals a consistent research trajectory focused on fundamental signal processing techniques with expanding applications into modern deep learning approaches. His recent work demonstrates a clear progression from traditional signal processing methods toward integrating machine learning techniques, particularly evident in his 2023 SPDGAN paper which combines manifold learning with generative adversarial networks for image colorization. Throughout his career, Professor Najim has maintained strong theoretical foundations while adapting to emerging technologies in the field. Mohamed Najim has supervised numerous research projects and collaborated extensively with colleagues across institutions. His work shows consistent funding support through participation in various research programs focused on signal processing applications. He has maintained active research collaborations with institutions including CNRS and HESAM University. Professor Najim conducts his research within the IMS laboratory, a leading research center for integration from materials to systems. The laboratory provides state-of-the-art facilities for signal processing research, including specialized computing resources for image processing and speech analysis. His work within the MOTIVE team focuses on developing innovative approaches to complex signal processing challenges across multiple application domains.
Gerald Borgia is a Professor in the Department of Biology at the University of Maryland's College of Computer, Mathematical and Natural Sciences, with affiliations at the Brain and Behavior Institute. His office is located in the Biology-Psychology Building (4239). He earned his Ph.D. in 1978 from the University of Michigan, specializing in the evolution of mate choice, social structure, and sociobiology. Borgia leads the Borgia Lab, which studies the evolution of mate choice and male display in nonresource-based mating systems, using bowerbirds as a model system. His research focuses on sexual selection, lek mating systems, complex male displays, female mate choice strategies, threat reduction in courtship, and the co-option of display traits. The lab maintains a long-term field site at Wallaby Creek in Australia for studying satin bowerbirds. Analysis of Borgia's recent publications shows consistent themes in sexual selection and animal communication. His work demonstrates increasing sophistication in experimental approaches, including robotic females, chemical manipulation of bowers, and cognitive testing. Recent articles focus on the role of cognitive ability in mating success, chemical signaling through bower paint, dynamic adjustment of male displays, and the relationship between aerobic capacity and sexual performance. Borgia actively advises graduate students and maintains a substantial research team. He recruits field assistants for Australian fieldwork and laboratory volunteers for video analysis. His NSF-funded research supports graduate students working on diverse projects including mate searching, juvenile display development, male-female signaling dynamics, and aerobic capacity studies. The lab collaborates with molecular systematists at the Smithsonian Institution and National Zoo.
Tamás Lovas serves as Associate Professor at the Department of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics. He teaches advanced courses in Building Information Modeling, Laser Scanning, Remote Sensing, and Intelligent Transportation Systems while supervising diploma theses in Surveying and Geoinformatics Engineering. Education: 1994: High school graduation, Városmajor High School, Budapest 1999: Certified Surveyor and Geoinformatics Engineer, Budapest University of Technology, Faculty of Civil Engineering 2005: PhD (Earth Sciences), Budapest University of Technology and Economics, Faculty of Civil Engineering Research Interests: Dr. Lovas specializes in laser scanning technologies and geospatial data processing with emphasis on airborne and terrestrial point cloud analysis. His work bridges civil engineering applications and computational methods, particularly in infrastructure monitoring and digital representation. Processing, classification, and modeling of airborne laser scanned data Accuracy testing of terrestrial laser scanning Processing and modeling of terrestrial laser scanned data Comparative study of spatial data acquisition technologies His 2022-2025 publications demonstrate accelerating integration of artificial intelligence in point cloud processing, with significant contributions to road surface extraction, urban land cover classification, and BIM automation. Current research trends show strong focus on digital twin development for autonomous vehicles and infrastructure management. Scientific Awards: Republic Scholarship (1998-1999) Karlsruhe Chancellor's Scholarship (1999) ERASMUS scholarship (2000) Korányi Fellowship (2001-2002) János Bolyai Research Scholarship (2008-2011) OHV 1st place (2008) Dean's commendation for ERASMUS committee work (2014) For Students Award - Teaching Department (2017) Advising and Grants: Dr. Lovas mentors students through diploma theses and TDK research projects on topics including object survey with amateur sensors and hull modeling. His research funding includes the prestigious János Bolyai Research Scholarship and international fellowships supporting collaborations with institutions like The Ohio State University. Labs and Teams: As founding member and supervisory board member of the Hungarian BIM Association, he drives industry-academia collaboration. His leadership roles include Deputy Dean of Education at the Faculty of Civil Engineering and responsibility for English language training programs, facilitating international academic exchange.
Nicholas Bishop, PhD is an Associate Professor in the Department of Human Development and Family Science at the University of Arizona's Norton School of Human Ecology. He holds a courtesy faculty appointment in the School of Sociology and has extensive research experience in aging populations, multimorbidity, and health disparities. His work focuses on complex health conditions in older adults, with particular attention to Mexican-origin populations and dietary interventions. Education: PhD, Arizona State University, Sociology, 2011 MA, University of Colorado at Denver, Sociology, 2007 BA, University of Colorado at Denver, Sociology, 2004 Research Interests: Dr. Bishop examines the progression of age-related health conditions, including cognitive decline, multimorbidity, and mobility limitations. He investigates modifiable risk factors such as dietary intake and food insecurity, using advanced statistical methods like latent class analysis and longitudinal modeling. Grants: Recent grants include: $106,231 subcontract from NIA for MEMORABLE study (multimorbidity and dementia) $34,665 Egg Nutrition Center grant on cognitive health and egg consumption $64,751 California Walnut Commission grant on walnut consumption and cognition Awards: 2019 College Achievement Award for Excellence in Scholarly Activities (Texas State University), 2010 Graduate Research Fellowship (Arizona State University). Labs/Teams: Leads research initiatives in the Norton School's Resilience and Health of Marginalized Populations group, collaborating with interdisciplinary teams on projects related to aging and chronic disease.
Katherine Storrs is a Senior Lecturer in the School of Psychology at the University of Auckland, New Zealand. She leads the Computational Perception Lab, where she combines computational modeling and psychophysical experiments to study visual perception. Her research is supported by a Marsden Fast Start grant, and she is actively involved in teaching and academic service. She earned her PhD in Psychological Science from the University of Queensland in 2015 and has held postdoctoral positions at Justus-Liebig University (Germany) and the MRC Cognition and Brain Sciences Unit (Cambridge, UK). She also worked as a Data Scientist at Twitter in London. Dr. Storrs' research focuses on how the visual system interprets material properties such as gloss, shape, and reflectance. She uses unsupervised deep learning models to simulate and predict human perception, particularly in ambiguous or complex visual environments. Her work bridges cognitive science, neuroscience, and artificial intelligence. Her most recent publications explore topics such as gloss perception, mental rotation, face similarity, and the role of statistical learning in shape encoding. These works frequently appear in high-impact journals like Nature Human Behaviour , PNAS , and Current Biology , demonstrating a strong trend toward using computational models to explain perceptual phenomena. Marsden Fast Start Grant (2021) Humboldt Research Fellowship (2019) UK National Finalist, FameLab (2017) Editorial Board Member, Nature Communications Psychology (2023–) Editorial Board Member, OpenMind (2022–) Social Media Editor, Perception and i-Perception (2020–) She supervises graduate students and teaches courses including PSYCH 306 (Research Methods), PSYCH 775 (Visual Perception in Brains and Machines), and PSYCH 109. She also mentors students in honours, master's, and PhD programs. Her lab is actively involved in interdisciplinary collaborations, particularly with researchers in machine learning and computational neuroscience. The lab is funded by the Marsden Fund and supported by access to high-performance computing resources for training deep neural networks.
Dr Nisha Nixon is a Fellow at St Catharine's College, University of Cambridge, holding an academic appointment in the School of Clinical Medicine with specialization in the Department of Ophthalmology. Appointed as an Academic Clinical Fellow in Ophthalmology in 2020 and admitted to the Fellowship in 2022, she serves as chief investigator for innovative clinical studies leveraging virtual reality technology. Her educational background includes preclinical and clinical medical training at St Catharine's College with first-class honors, complemented by intercalated studies in Physiology, Development and Neuroscience. Key distinctions include the Moses Holway Scholarship, John Addenbrooke Medical Prize, Thomas Hobbes Scholarship, and Robert Comline Prize in Systems Physiology. Dr Nixon's research focuses on revolutionizing oculomotor assessment through virtual reality applications , particularly for strabismus diagnosis and eye movement analysis outside traditional clinical settings. Her work bridges ophthalmology , medical technology development , and automated diagnostic systems , emphasizing cost-effective solutions using accessible hardware like VR headsets. Her publication portfolio demonstrates consistent advancement in diagnostic innovation, with recent work (2021-2023) centering on augmented reality strabismus screening and virtual reality ocular misalignment testing. Earlier research (2015-2017) established foundations in dry eye therapy, lacrimal surgery, and color vision adaptation for clinicians. Moses Holway Scholarship John Addenbrooke Medical Prize Thomas Hobbes Scholarship Robert Comline Prize in Systems Physiology As an educator, Dr Nixon has supervised Neurobiology and Human Behaviour for second-year medical students since 2012 and holds a Postgraduate Certificate in Medical Education (2016). Her current VR-based clinical study represents significant grant-funded research exploring automated diagnosis systems, with potential to transform community-based eye care delivery through low-cost technology.
Malcolm Innes is a Senior Lecturer at the Edinburgh Napier University's School of Arts and Creative Industries . With over 10 years of research outputs, his work spans lighting design, smart textile applications, and heritage conservation. Specializes in lighting for historical sites Leader in community co-design urban lighting Expert in light-sensitive material preservation Research trends focus on: Merging art and science in illumination Smart textiles for performance environments Heritage site lighting innovation Projects include: 12 Closes Community Co-design (2016-2022) Dunbar SciFest Projection (2015) Edinburgh Zoo Night Trail (2014-2015)
Dongyi Wang is an Assistant Professor in the Department of Biological and Agricultural Engineering at the University of Arkansas, where he directs the Smart Agriculture and Food Engineering (SAFE) Lab. His work bridges advanced technologies like artificial intelligence, robotics, and machine vision with agrifood manufacturing to enhance product quality, safety, and worker welfare. Ph.D. in Bioengineering from the University of Maryland, College Park B.S. in Electrical and Computer Engineering from Fudan University Visiting experience at The Chinese University of Hong Kong Research interests span smart agrifood manufacturing , robotics , machine vision , and artificial intelligence , with applications in crop monitoring, food safety, and healthcare. His lab develops solutions like automated defect detection, pathogen sensing, and sustainable processing systems. Article analysis reveals a focus on AI-driven agricultural automation , hyperspectral imaging , robotic manipulation of bio-products , and food safety innovations . Recent works include YOLO-based tomato defect segmentation, E. coli biosensing, and UAV-based blackberry monitoring. Awards & Memberships College of Engineering Dean’s Award of Excellence Rising Star Research Award (UARK) Outstanding Mentor Award (UARK) Professional memberships in ASABE and IEEE As an educator, he teaches instrumentation and artificial intelligence in agrifood manufacturing . The SAFE Lab, funded by USDA NIFA, NSF, and federal/local agencies (> $7M), prioritizes workforce development in AI/robotics for agrifood industries.
Dr. Ashley Wood is a Senior Lecturer and Director of Learning and Teaching at the School of Optometry and Vision Sciences, Cardiff University. An experienced optometrist and Cardiff graduate, Dr. Wood has been with the institution since 2012, contributing significantly to both research and teaching in the field of vision sciences. Dr. Wood's educational background includes a BSc in Optometry & Vision Science (2003-2006) and a PhD in "Retinal structure and function in Age-related Maculopathy" (2007-2011), both from Cardiff University. Professional credentials include Fellowship of the Higher Education Academy (FHEA), Accreditation for Eye Health Examination Wales (EHEW), and registration as an Optometrist with the General Optical Council (GOC: 01-32560). Research interests focus primarily on age-related macular degeneration (AMD), the leading cause of vision loss in the EU/UK. Dr. Wood employs objective assessment methods including Optical Coherence Tomography (OCT), ocular electrophysiology, and retinal imaging densitometry. Current work involves using a prototype long wavelength OCT device that enables visualization of the choroid, a layer of blood vessels beneath the retina not visible with standard OCT devices. This technology also allows retinal imaging under dark-adapted conditions, facilitating studies of physiological and pathological changes in the dark-adapted eye. Dr. Wood has secured significant research funding including a Macular Society PhD Studentship (£99,858), AltRegen Ltd. Clinical Trial (£1.2M), and multiple studentships from the College of Optometrists and Abbeyfield Research Foundation. Current research projects focus on improving AMD detection and monitoring to ultimately support better treatments and prevent vision loss. Scientific contributions include numerous publications on AMD detection, OCT imaging, and electrophysiological assessment. Key awards and recognitions include multiple CUROP Summer Studentships, Abbeyfield Research Foundation PhD Studentship, and ISCEV Travel Award. Teaching responsibilities are extensive, including leadership of the Investigative Optometry and Case Studies module (OP3206) and Abnormal Ocular Conditions module (OP3104). Dr. Wood also delivers color vision lectures, supports clinical sessions, and supervises research projects. International teaching experience includes collaboration with HU University of Applied Sciences in Utrecht, Netherlands for the "Eye Care and Diabetes; the Bigger Picture" summer school. Administrative roles include serving as Director of Learning and Teaching, Exams Liaison Officer, and chairing the Student-Staff Panel. Dr. Wood currently supervises multiple postgraduate students working on AMD-related research projects.
Alan J. Michaels is a Professor and Director of the NSI Spectrum Dominance Division at Virginia Tech, leading research in digital communications, electronic warfare, and quantum algorithms. He holds affiliations with the Bradley Department of Electrical and Computer Engineering and the Department of Mathematics. Previously, he served at Harris Corporation in engineering leadership roles. Education includes multiple degrees from Georgia Institute of Technology (B.S., M.S., Ph.D. in ECE; B.S., M.S. in Applied Mathematics; M.S. in Operations Research) and an MBA from Carnegie Mellon University. Research focuses on RF spectrum dominance, applied mathematics, and cybersecurity. Notable contributions include 44 U.S. patents, over 80 peer-reviewed publications, and leadership in $171M+ research projects. He pioneered initiatives like the Vertically Integrated Projects (VIP) model for undergrad research and the Southwest VA node of the Commonwealth Cyber Initiative (CCI). Awarded Fellow of the National Academy of Inventors for his inventive impact. His work bridges academia and industry, emphasizing practical applications in restricted research domains.
Dr. Suman Bista is a Research Fellow at the QUT Centre for Robotics, Queensland University of Technology. He holds a PhD in Signal Processing and Telecommunications from Université de Rennes. His primary research focuses on robotics, computer vision, and SLAM algorithms, with notable contributions to indoor navigation systems, semantic segmentation, and medical imaging applications. He has published extensively in top-tier journals like the International Journal of Robotics Research and conferences such as IEEE/RSJ IROS. Key research interests include visual servoing, resource-constrained robotics, and simulation environments for active scene understanding. His work bridges theoretical advancements with practical applications in autonomous systems and healthcare diagnostics. Dr. Bista’s publications highlight innovative solutions in robotics navigation, including the development of BenchBot environments for simulating real-world robotics challenges. His research emphasizes cross-disciplinary approaches, integrating computer vision techniques with robotics hardware design.
