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Zijian WangView profile

Research Fellow

• Computer Vision
• Deep Learning
• Domain Adaptation
• +5 more

Dr. Zijian Wang is a Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on improving vision model transferability, domain adaptation, and deep learning applications in computer vision and remote sensing. He holds a PhD in Computer Science from The University of Queensland (2023). Research interests include domain adaptation techniques, neural network generalization, unsupervised learning, and applications in satellite imagery analysis, vegetation segmentation, and disaster damage assessment. His work emphasizes practical solutions for open-world domain shifts and efficient data utilization. Recent publications explore transfer learning via feature space perturbation, neural network-based satellite image delivery (MIDNet), and benchmark datasets for model generalization (CIFAR-10-Warehouse). He collaborates on platforms like FluMA for influenza monitoring and RoadAtlas for infrastructure management. Currently advises PhD candidates in vegetation segmentation for Australian wheat data and crop health assessment. Active in top conferences (CVPR, ICCV, NeurIPS) and journals (IEEE TPAMI, TMM).

• Population Genetics
• Medical Genetics
• Statistical Genetics
• +3 more

Alan Stark is an academic staff member at The University of Sydney, contributing to research in population genetics, statistical genetics, and genetic epidemiology. His work often intersects mathematical modeling with biological systems, particularly focusing on Hardy-Weinberg equilibrium, X-linked disorders, and rare heritable traits. He has published extensively in journals like Twin Research and Human Genetics and Genetics and Molecular Biology , collaborating with scholars such as Eugene Seneta on foundational genetic theories. His research addresses both theoretical and applied aspects of genetics, including mutation rates, disease prevalence modeling, and historical perspectives on genetic theories. Education details are not explicitly stated in the available text. His research interests span population structure analysis, random mating models, and the application of statistical methods to genetic problems. Recent studies include simulations of hemophilia B mutation rates and investigations into the X-linked analog of Hardy-Weinberg principles. Stark’s work also honors key figures in genetics like C. C. Li and E. M. Nicholls, contextualizing their contributions within modern genetic frameworks. No awards or current grants are listed in the profile. His research has implications for understanding genetic disorders such as neurofibromatosis, retinoblastoma, and microcephaly, leveraging interdisciplinary approaches between statistics and biology.

• Environmental Epidemiology
• Small Area Health Statistics
• Bayesian Hierarchical Models
• +3 more

Dr James Bennett serves as a Principal Statistician at the School of Public Health within Imperial College London's Faculty of Medicine, specializing in advanced statistical methodologies for public health research. His work bridges epidemiology, environmental health, and geospatial analysis with a focus on global health disparities. His academic foundation includes a BSc in Applied Mathematics from the University of Warwick followed by a PhD in Statistics at Imperial College London under Professor Jon Wakefield. His doctoral research, sponsored by Glaxo Plc, investigated 'Bayesian Analysis of Population Pharmacokinetic Models,' establishing his expertise in Bayesian statistical frameworks. Dr Bennett's research concentrates on environmental epidemiology, small area health statistics, Bayesian hierarchical modeling, and exposure assessment. He employs sophisticated spatial and temporal analytical techniques to examine health-environment interactions, particularly in urban settings across diverse global contexts. His methodological innovations enable precise mapping of health risks at granular geographic scales. Analysis of his recent publications (2023-2025) reveals a dominant focus on geospatial modeling of environmental stressors in sub-Saharan African cities, especially Accra, Ghana. His work integrates street-view imagery, deep learning, and sensor data to quantify air/noise pollution and urban vitality. Concurrently, he contributes to large-scale pooled analyses tracking global trends in diabetes, obesity, and hypertension across hundreds of population studies. No specific scientific awards are documented in available sources. While formal student advising isn't explicitly referenced, Dr Bennett's research involves extensive collaboration with international teams through Imperial College's public health initiatives. His current projects appear funded by urban health and environmental monitoring grants, continuing his long-standing engagement with population-level health data systems. His work with the GARFIELD-AF registry indicates ongoing involvement in cardiovascular outcomes research. His research operates within Imperial College's School of Public Health infrastructure, leveraging partnerships with global health consortia and environmental monitoring networks. Recent work emphasizes interdisciplinary collaboration between statisticians, epidemiologists, urban planners, and environmental scientists to address health inequities in rapidly urbanizing regions.

Alexandra C. N. KingstonView profile

Assistant Professor

• Sensory ecology
• Neurophysiology
• Vision
• +5 more

Alexandra C. N. Kingston, Ph.D., serves as Assistant Professor of Biological Science in The University of Tulsa's Oxley College of Health & Natural Sciences, where her research investigates neurobiology and sensory physiology in marine invertebrates. Her work uniquely bridges visual ecology and biomechanics, focusing on snapping shrimp adaptations that inform both sensory biology and neurotrauma protection. Education: Ph.D., University of Maryland B.S., Arizona State University Research Focus: Dr. Kingston's lab employs integrative approaches to study how invertebrates interact with environments through specialized sensory systems. Key projects examine snapping shrimp visual processing (the fastest documented in aquatic animals) and helmet-like armor that mitigates blast-induced neurotrauma through shock wave dampening. Her work spans neuroethology, biomineralization, and evolutionary adaptation in intertidal species like chitons and bivalves, revealing fundamental principles of biological materials and sensory function. Publication Analysis: Recent publications (2020-2025) demonstrate consistent interdisciplinary innovation, with 60% focusing on snapping shrimp sensory biology and 40% on chiton visual systems/biomineralization. Recurring themes include structural trade-offs in armor-vision relationships, distributed photoreception mechanisms, and biomimetic applications for neuroprotection. Her methodology integrates electrophysiology, materials analysis, and behavioral observation across marine taxa. Mentorship & Infrastructure: Dr. Kingston actively recruits undergraduate and graduate students into research, emphasizing hands-on discovery in her well-equipped lab. She directs projects involving behavioral assays, electroretinography, and materials characterization, fostering collaborations with engineering and neuroscience researchers. Her teaching portfolio includes Invertebrate Zoology, Comparative Neurobiology, and graduate seminars on organismal biology. Research Environment: The Kingston Lab operates within The University of Tulsa's specialized facilities, including controlled marine habitats and neurophysiology suites. Ongoing work explores the molecular basis of magnetite biomineralization in chiton teeth and high-speed neural processing in snapping shrimp, with potential applications for traumatic brain injury prevention and bio-inspired materials design.

• 2D Materials
• Silicon Nanomembranes
• Micro/Nanofabrication
• +4 more

Houk Jang is a Staff Scientist in the AI Accelerated Nanoscience division at Brookhaven National Laboratory's Center for Functional Nanomaterials. His expertise spans 2D materials, silicon nanomembranes, micro/nanofabrication, and analog neural networks. He leads the QPress initiative, developing autonomous tools for 2D material-stack production and characterization, aiming to automate and accelerate material research for CFN users. Education: B.S. (2010) in Materials Science and Ph.D. (2014) in Nano Engineering, both from Sungkyunkwan University, Korea. Postdoctoral research at Yonsei University and Harvard University. His work focuses on integrating AI with nanomaterials to advance optoelectronics, flexible electronics, and quantum systems. Research highlights include creating graphene-based flexible electronics, developing optical machine vision processors, and pioneering methods for 2D material layer detection. His 2022 Nature Electronics publication demonstrated in-sensor optoelectronic computing using silicon, while 2020 Advanced Materials work introduced atomically thin optoelectronic processors. Publications span over 40 peer-reviewed articles, emphasizing photonics, quantum effects in 2D materials, and neuromorphic devices. His lab collaborates on projects involving terahertz spectroscopy, nanoscale imaging, and bioinspired optical systems. Currently directs nanofabrication efforts and advises on scalable material synthesis techniques. Active in developing high-performance infrared photodetectors and tunable electronic devices leveraging graphene and topological materials.

• Machine Learning
• Data Mining
• Computer Vision
• +7 more

Guansong PANG is a full-time Assistant Professor at the School of Computing and Information Systems , Singapore Management University (SMU) , where he leads the Machine Learning & Applications (MaLA) Lab . He holds a PhD from University of Technology Sydney (2019) and has previously served as a Research Fellow at the Australian Institute for Machine Learning , University of Adelaide. Research Interests : His work centers on machine learning and data mining , particularly focusing on anomaly detection , open-world learning , and foundation models for graphs, time series, and tabular data. He investigates security and safety in foundation models , including hallucination mitigation and AI-generated content detection , with applications spanning network intrusion detection , fraud detection , industrial defect detection , and biometric anti-spoofing . Recent Awards : Lee Kong Chian Fellowship (2024-2026) World's Top 2% Scientists (2022-2024) DSAA 2023 Best Paper Award (Applications Track) Most Influential KDD 2023 Paper Professional Roles : Area Chair for NeurIPS, ICLR, ICML, CVPR, KDD, PAKDD, IJCAI, and AAAI; Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and Pattern Recognition ; Editorial Board member for IEEE Intelligent Systems and International Journal of Data Science and Analytics .

Sarah J WaughView profile

Associate Professor

• Spatial Vision
• Visual Acuity
• Visual Crowding
• +7 more

Dr Sarah J Waugh serves as Reader in Optometry and Vision Sciences at the University of Huddersfield's Department of Optometry and Vision Sciences within the School of Applied Sciences. She is Co-Director of the Centre for Vision across the Life Span and maintains an active research program while accepting PhD students. Her academic journey spans multiple prestigious institutions including Aston University, Anglia Ruskin University, University of Melbourne, University of Houston, and McGill University. Queensland University of Technology: Diploma in Applied Science (Optometry) University of Melbourne: Master of Science University of Houston: PhD in Psychophysical Visual Mechanisms Waugh's research focuses on spatial vision mechanisms with particular emphasis on visual crowding, amblyopia detection, and pediatric vision development. Her work bridges fundamental visual neuroscience with clinical optometry applications, especially in developing improved diagnostic methods for children's vision disorders. Recent publications demonstrate her leadership in establishing crowding distance as a superior metric for amblyopia detection compared to traditional acuity measures. Analysis of her 15 most recent publications reveals consistent focus on visual crowding mechanisms across developmental stages, with increasing emphasis on clinical translation. Her work demonstrates how crowding metrics outperform traditional acuity tests in detecting amblyopia, particularly in pediatric populations, while also exploring color vision deficiencies and binocular vision disorders. Fellow of the American Academy of Optometry Active reviewer for Vision Sciences Society and international journals Grant reviewer for Biotechnology and Biological Sciences Research Council Waugh has supervised 7 PhD students to completion and 4 Postdoctoral Research Fellows while externally examining 5 PhDs internationally. Her research program is supported through multiple grants including an Evelyn Trust grant to establish clinical psychophysical testing services. She maintains active collaborations through the Centre for Vision across the Life Span and has established unique NHS pediatric ophthalmology services at university clinics. Current work explores virtual reality treatments for amblyopia and improved visual screening methods for children.

Byungseok MinView profile

Associate Professor

• AI applications for Intelligent information System
• Abnormal behavior classification
• AI Object detection and classification
• +2 more

Byungseok Min is an Associate Professor in the Department of Artificial Intelligence and Data Science at Sejong University, South Korea, a position he has held since 2025. His academic career is complemented by extensive industry experience, including roles as CRO at Markany Co., Ltd. (2024-2025), CTO at XAVIS Co., Ltd. (2020-2024), and engineering positions at Samsung Electronics (2009-2020). His educational background includes: Ph.D. from Purdue University, USA (2009) M.S. from Yonsei University (2003) B.S. from Yonsei University (2001) Professor Min's research focuses on AI applications for intelligent information systems, with specific interests in abnormal behavior classification, AI object detection and classification, AI imaging for image quality enhancement, and privacy information detection and de-identification in CCTV and documents. His work bridges computer vision and display technology, aiming to improve visual perception and image quality in consumer electronics and industrial systems. His recent publications (2018-2019) center on high dynamic range (HDR) display technology, addressing challenges such as ultra-high dynamic range implementation, color mismatches due to observer metamerism, and burn-in artifact assessment. These works reflect a strong emphasis on quantitative evaluation of display metrics, experimental results, and multimedia applications, contributing to advancements in display devices and visual perception.

• Myopia
• Contact Lenses
• Refractive Errors
• +5 more

Baskar Pitty Theagarayan is a Senior Lecturer in Optometry at the Department of Optometry and Vision Sciences, School of Applied Sciences, University of Huddersfield, United Kingdom. He is also a member of the Centre for Vision across the Life Span. His research focuses on myopia, color vision defects, binocular vision, and the impact of optical interventions. He currently accepts PhD students for research projects. Academic Role: Senior Lecturer in Optometry Institution: University of Huddersfield Research Affiliation: Centre for Vision across the Life Span Research Interests: Myopia progression and management Color vision deficiency (CVD) impacts on quality of life Binocular vision anomalies Optical lens design and prismatic effects Contact lens-related refractive error correction Visual perception and ocular motor control Publication Trends: Recent works include clinical studies on accommodative response in young adults (2025), cross-sectional analyses of binocular vision in children with disabilities (2025), CVD impact assessments (2023), and systematic reviews of color vision devices (2022). His research spans optical engineering, clinical optometry, and quality-of-life evaluations. Advising: Accepts PhD students for vision-related research projects.

Samantha A. BrooksView profile

Associate Professor

• Equine Genetics
• Genetic Disorders
• Coat Color Genetics
• +4 more

Dr. Samantha A. Brooks is an Associate Professor of Equine Physiology in the Department of Animal Sciences at the University of Florida. Her research focuses on genetic factors influencing horse health, including coat color genetics, gait variation, metabolic disorders, and skeletal defects. She leads the Brooks Equine Genetics Lab, which conducts studies on traits such as gait mechanics, body size, and disease susceptibility using genomic and transcriptomic approaches. Education: B.S. in Agricultural Biotechnology (University of Kentucky), Ph.D. in Veterinary Science (University of Kentucky, specializing in Equine Genetics under Dr. Ernest Bailey), followed by a Paul Mellon Postdoctoral Fellowship at the Gluck Equine Research Center. Research Interests: Genetic basis of equine diseases, genetic diversity in breeds, and applications of genomics to improve breeding practices. Key projects include identifying genetic markers for coat colors, laminitis, and neurological conditions. Current studies involve gait analysis via AI, body size QTL mapping, and metabolic syndrome genetics. Professional Contributions: Taught Equine Biology and Management at Cornell University, developed online extension courses on equine genetics, and collaborates on international initiatives like the S1094 Multistate Project. Awards include the Paul Mellon Postdoctoral Fellowship. Labs/Teams: Leads the Brooks Equine Genetics Lab, which uses cutting-edge techniques like whole-genome sequencing and machine learning to analyze equine traits. Ongoing projects include the 'Gait Study' and 'Size Study' to map genetic influences on locomotion and body dimensions.

Justin BroackesView profile

Professor

• Metaphysics
• Philosophy of Perception
• Color Theory
• +4 more

Justin Broackes is a Professor of Philosophy at Brown University, where he has held positions since 1992. He earned his doctorate at Oxford in philosophy of mind and language, with additional expertise in early modern philosophy and the history of metaphysics. His research focuses on color perception, substance theory, and the philosophy of Iris Murdoch. Broackes has held visiting roles at the École Normale Supérieure (Paris) and the Institut Jean Nicod, and is affiliated with Harvard’s Vision Research Lab and Brown’s Center for Vision Research. His work spans metaphysics, epistemology, and the history of philosophy, with notable projects on color-blindness and Locke’s theory of substance. He is currently editing a collection of essays on Iris Murdoch’s The Sovereignty of Good . Broackes has authored over 20 journal articles and book chapters, with recent contributions to Behavioral & Brain Sciences , Erkenntnis , and Philosophical Quarterly . He serves as an Associate Editor for Nous and Behavioral & Brain Sciences , and has organized conferences on Murdoch’s philosophy and early modern metaphysics. Teaching encompasses ancient through contemporary philosophy, including courses on Wittgenstein, British empiricism, and metaphysics. His research interests intersect cognitive science and neuroscience, particularly in how philosophical theories of perception engage with empirical findings on color vision and sensory deprivation.

Ilgin GulerView profile

Associate Professor

• Transportation Engineering
• Intelligent Transportation Systems
• Traffic Signal Control
• +8 more

Ilgin Guler is an Associate Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University’s College of Engineering. Her research focuses on intelligent transportation systems, traffic operations, infrastructure sustainability, and equitable mobility solutions. She contributes to interdisciplinary projects such as the Equitable Optimization of Transportation Systems and Services addressing climate and sustainability challenges. Her research interests span Transportation Engineering , Intelligent Transportation Systems , Traffic Signal Control , Connected Vehicles , Pavement Management , and Transit Signal Priority . She applies advanced techniques including deep learning and optimization to improve urban and rural transportation systems. Her work emphasizes safety, equity, and sustainability in infrastructure planning and operations. The recent publications highlight a strong trend in integrating machine learning with classical traffic theory, enhancing infrastructure resilience through data-driven maintenance, and evaluating shared mobility impacts on safety and equity. Her research bridges civil engineering with data science, focusing on real-world applications in Pennsylvania and beyond. Scientific Awards: No awards explicitly mentioned in the provided text. She advises graduate students in transportation engineering and has been involved in multiple research projects, including seed-funded initiatives from the Institute for Energy and the Environment (IEE). While specific grant amounts and student names are not listed, her collaborative work with faculty like Vikash Gayah and Anik Sengupta indicates active mentorship and team leadership. Her research is associated with initiatives such as the Equitable Communities and the Built Environment and leverages tools from connected vehicle technology, impact-echo testing, and generative AI for infrastructure assessment.

• Quantum Key Distribution
• Neuroscience
• Machine Learning
• +3 more

Dr. Yijun Wang is a researcher affiliated with the Chinese Academy of Sciences, State Key Laboratory on Integrated Optoelectronics, and Institute of Semiconductors in Beijing, China. His work spans multiple disciplines including quantum key distribution, brain-computer interfaces, and machine learning applications in cybersecurity and biomedical imaging. Key Research Areas: Quantum cryptography, Neural signal processing, Transformer-based models, Network security, and Digital health systems. Recent publications highlight advancements in EEG authentication systems 2026 , radar-based activity recognition 2025 , and quantum communication security 2025 . His work demonstrates interdisciplinary expertise bridging theoretical algorithms and practical engineering solutions. Scientific contributions include: Developing attention mechanisms for image restoration 2025 Creating reliability-enhanced BCIs via graph-driven fusion 2025 Designing semi-supervised solvers for CAPTCHA breaking 2021 Dr. Wang's collaborations appear across journals like Pattern Recognition , IEEE Transactions , and conferences including AAAI 2025 , ICLR 2025 , and EMBC 2023 . Current projects integrate LLMs with digital phenotyping for hypertension management 2025 and explore self-taught reasoning mechanisms 2025 .

Blas Salvador DominguezView profile

Assistant Professor

• Production Technologies
• Computer Architecture
• Applied Robotics
• +5 more

Blas Salvador Dominguez is an Assistant Professor at the University of Cádiz, affiliated with the Department of Automation, Electronics, Architecture and Computer Networks Engineering and the TEP940 Applied Robotics research group. His work focuses on Robotics, Microfluidics, and Biomedical Devices, particularly in applications such as neonatal monitoring and wearable health sensors. PhD in Microfluidic Radiopharmaceutical Systems (University of Seville, 2019) Research interests include Smart Insoles , Anthropomorphic Prostheses , and Microfluidic Reactors . Recent publications highlight advancements in deep learning for weld inspection , edge-cloud neonatal monitoring , and PDMS-based sensors . Collaborations with the Institute of Electron Microscopy and Materials (IMEYMAT) underscore his interdisciplinary approach. Scientific awards are not explicitly listed, but his work has been supported by projects in Production Technologies and Applied Robotics . He has contributed to 15+ publications spanning wearable sensors, radiopharmaceutical synthesis, and PCB-MEMS integration.

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

Adriana Birlutiu is a Lecturer in the Computer Science Department at 1 December 1918 University of Alba Iulia , Romania. Her expertise lies in machine learning, computer vision, bioinformatics, and transfer learning, with a recent focus on porcelain-industry optimisation. Education Ph.D., Radboud University Nijmegen, Netherlands (2011) M.Sc., Babeș-Bolyai University of Cluj-Napoca & University of Lorraine (Erasmus), 2005 B.Sc., Babeș-Bolyai University of Cluj-Napoca, 2004 Research Interests Adriana's research spans machine learning , deep learning , computer vision , and bioinformatics . She has contributed to preference learning, domain adaptation, protein–protein interaction prediction, and automated quality control in porcelain manufacturing. Her recent projects integrate deep neural networks with industrial computer-vision systems to detect defects and recognise characters on ceramic surfaces. Publication Trends Across 15 recent publications (2010-2019), Adriana has consistently explored transfer learning , multi-task learning , and Bayesian methods . Articles cluster around two major axes: biomedical applications (protein networks, cancer relapse prediction, respiratory-motion modelling for radiotherapy) and industrial AI (porcelain defect detection, character recognition). The work shows a clear evolution from theoretical machine-learning foundations to practical, domain-specific implementations. Grants & Projects SIVAP (2016-2018): Intelligent ML & computer-vision system for porcelain manufacturing optimisation, UEFISCDI PN-III-P2-2.1-BG-2016-0333. CMRCC (2017-2018): Computational Models for Reproducing Ceramics Colors, UEFISCDI PN-III-P2-2.1-PED-2016-1835. Student Supervision & Mentoring Adriana has supervised more than 25 undergraduate and master’s theses. Her students have won multiple awards at national conferences such as In-Extenso and SCCSS-IEECC , covering topics from automated defect detection to web applications for academic scheduling. Teaching Responsibilities She teaches courses including Machine Learning , Mathematical Software , Fundamental Algorithms , Object-Oriented Databases , and Modelling and Simulation at both undergraduate and master levels.