جست‌وجوی استاد و پژوهشگر

Chandi Witharanaمشاهده پروفایل

استادیار

• Remote Sensing
• Geospatial Analysis
• Arctic Permafrost Mapping
• +۵ مورد دیگر

Dr. Chandi Witharana is an Assistant Professor in the Department of Natural Resources and the Environment at the University of Connecticut's College of Agriculture, Health, and Natural Resources. Previously, they served as Assistant Professor in Residence (2020-2023), Assistant Research Professor (2018-2020), and Visiting Assistant Professor (2016-2018) at UConn. Their academic journey includes a Postdoctoral Research Fellowship at SUNY Stony Brook (2014-2016) and graduate work at UConn where they earned their PhD in Remote Sensing in 2014. Dr. Witharana teaches courses in high-resolution remote sensing, geospatial analysis, and introductory geomatics. Dr. Witharana's educational background includes: PhD in Remote Sensing, University of Connecticut (2014) MS in GIScience, University of Connecticut (2009) BS in Geology, University of Peradeniya, Sri Lanka (2005) Dr. Witharana's research focuses on methodological developments for analyzing large volumes of multi-modal remote sensing data for environmental, industrial, and agricultural applications, with special emphasis on Arctic Permafrost remote sensing. They harness sub-meter resolution satellite imagery, AI, and high-performance computing resources to map permafrost landforms, monitor thaw disturbances, and assess risks to human-built infrastructure in the Arctic. Their work extends beyond research to include innovative applications of remote sensing in K-12 STEM education through imagery-enabled lesson plans. Dr. Witharana aims to use cutting-edge geospatial technologies as transformative learning instruments to help students understand complex human-environment interactions. The recent publications of Dr. Witharana demonstrate a strong focus on applying advanced AI and remote sensing techniques to Arctic permafrost monitoring and infrastructure risk assessment. Their work increasingly incorporates vision transformers and deep learning models for more accurate detection of permafrost features and unhealthy tree crowns. There's a clear trend toward developing scalable geospatial datasets with standardized approaches, particularly for retrogressive thaw slumps. Many publications address practical applications including power outage risk modeling, forest management for storm resistance, and infrastructure monitoring in changing Arctic landscapes. The research shows growing interdisciplinary collaboration across environmental science, computer science, and engineering domains. Dr. Witharana has secured significant research funding as PI or Co-PI on numerous grants totaling over $14 million, including: NSF's Permafrost Discovery Gateway project ($3,000,000) Google-funded research on tracking Arctic permafrost thaw ($5,000,000) NSF's role of capillaries in the Arctic hydrologic system ($2,000,000) USDA projects on drone imaging for nutrient deficiency detection ($200,000) Eversource Energy projects on tree risk modeling ($275,000) As an educator, Dr. Witharana mentors students through research projects funded by these grants and teaches specialized courses in remote sensing and geospatial analysis. They serve as Director of the Remote Sensing & Geospatial Data Analytics Graduate Program and as a Steering Committee Member for UConn's Data Science Masters Program. Dr. Witharana is also an Editorial Advisory Board Member for the ISPRS Journal of Photogrammetry and Remote Sensing and regularly reviews proposals for NSF and other agencies. Their research group leverages high-performance computing resources including Frontera/NSF and XSEDE allocations for large-scale geospatial analysis. Dr. Witharana leads research teams focused on Arctic permafrost monitoring and geospatial AI applications, collaborating with institutions including University of Alaska-Fairbanks, Woodwell Climate Research Center, and UC Santa Barbara. Their work involves developing advanced workflows for processing satellite imagery and implementing machine learning models for environmental monitoring. The research group actively engages in developing educational applications of remote sensing technology, particularly for K-12 STEM education.

Chris J Buddمشاهده پروفایل

استاد

• Nonlinear Mathematics
• Industrial Mathematics
• Numerical Analysis
• +۷ مورد دیگر

Professor Chris J Budd OBE is a distinguished Professor of Applied Mathematics at the University of Bath's Department of Mathematical Sciences, where he serves as Director of Knowledge Exchange for the Bath Institute for Mathematical Innovation (IMI). He is also Professor of Mathematics at the Royal Institution of Great Britain and a former Gresham Professor of Geometry. His leadership extends to directing the Centre for Nonlinear Mechanics and serving as Super Champion of the KE Hub. His educational background includes a gap year with Marconi that profoundly shaped his career, followed by undergraduate studies at Cambridge and a DPhil at Oxford. This industry experience during his formative years established his lifelong commitment to industrial mathematics and knowledge exchange. Budd's research focuses on nonlinear mathematical problems with industrial applications, particularly adaptive moving mesh methods for meteorology and climate modeling, data assimilation, non-smooth dynamical systems, and the mathematics of machine learning. He approaches linear problems as 'for cissies,' preferring the challenges of nonlinear systems that better represent real-world phenomena. His work bridges theoretical mathematics with practical applications across meteorology, environmental science, and engineering. His recent publications reveal a strong trend toward integrating machine learning with traditional numerical methods, particularly in climate modeling and solving partial differential equations. This includes Fourier Neural Operators, adaptive mesh methods enhanced by graph neural networks, and mathematical frameworks for understanding climate tipping points through non-smooth dynamics. OBE for services to mathematics National Teaching Fellowship (NTF) Knowledge Transfer Award for work with the Met Office Fellow of the Institute of Mathematics and its Applications (FIMA) Chartered Mathematician (C Math) British Science Association award for best science festival (2009) As principal investigator of the £3.5M EPSRC Programme Grant 'Maths4DL' on the Mathematics of Deep Learning, Budd leads a major collaborative effort between Bath, Cambridge, and UCL. He actively supervises numerous PhD students across diverse projects including climate modeling, machine learning applications, and industrial mathematics problems. His commitment to knowledge exchange is exemplified through V-KEMS (Virtual Forum for Knowledge Exchange in the Mathematical Sciences), which he co-founded to address challenges like the COVID-19 pandemic through mathematical approaches. Budd directs the Centre for Nonlinear Mechanics at Bath, fostering interdisciplinary research through mathematical modeling of complex systems. He also leads the Bath Institute for Mathematical Innovation's knowledge exchange activities, connecting academic mathematics with industrial and societal challenges. His work with V-KEMS has proven particularly effective during the pandemic, mobilizing teams of mathematicians to address urgent real-world problems.

Anjali Adukiaمشاهده پروفایل

استادیار

• Educational inequality
• Economics of education
• Child development
• +۵ مورد دیگر

Anjali Adukia is an assistant professor at the University of Chicago Harris School of Public Policy and the founder and director of the MiiE Lab (Messages, Identity, and Inclusion in Education). She is a faculty research fellow at the National Bureau of Economic Research (NBER) in the Economics of Education and Children and Families groups, a research fellow at the IZA Institute of Labor Economics, a non-resident fellow at the Center for Global Development, and an affiliate of the Bureau for Research and Economic Analysis of Development (BREAD). Dr. Adukia earned her doctoral degree from Harvard University Graduate School of Education with a focus on the economics of education, along with master's degrees in international education policy and higher education from Harvard. She completed her undergraduate studies in molecular and integrative physiology at the University of Illinois at Urbana-Champaign. Her interdisciplinary background bridges the natural sciences with social science research methods. Her research explores how to reduce inequalities so children from historically disadvantaged backgrounds have equal opportunities to develop their potential. She examines factors influencing educational decisions and the role of institutions in improving child outcomes, particularly at the intersection of education and health. Adukia's work draws on large-scale data, often using artificial intelligence methods to expand social science tools and data sources. Her research spans educational infrastructure, representation in children's literature, restorative justice practices, and urban-rural educational disparities. Her publications reveal consistent themes in educational equity, with particular focus on how school infrastructure (like sanitation facilities), representation in educational materials, and disciplinary practices impact student outcomes. She employs innovative computational methods to analyze large datasets, including applying AI tools to measure representation in children's books and examining textbook content across different educational systems. NSF CAREER Award in economics (2024-2029) SREE Early Career Award (2023) William T. Grant Foundation Scholar Award (2018-2023) NAEd/Spencer Postdoctoral Fellowship (2018) APPAM PhD Dissertation Award (2014) AEFP Jean Flanigan Outstanding Dissertation Award (2015) 2021 Google Customer Award for Education Dr. Adukia serves on the editorial boards of Education Finance and Policy, Journal of Research on Educational Effectiveness, and Journal of Social Computing (IEEE). She organizes the annual AI in Social Sciences conference at the University of Chicago and continues to work with international non-governmental organizations including UNICEF and Manav Sadhna in Gujarat, India. Her research has been featured in major media outlets including The Economist, Wall Street Journal, Washington Post, and NPR.

Kathleen Curranمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Prof. Kathleen Curran is a Professor at University College Dublin (UCD) and director of the UCD machine learning in medical imaging and diagnostics innovative research lab ( https://www.ucd-ml-mi.com/ ). She serves as an Affiliated Principal Investigator in the Centre for Biomedical Engineering, an INSIGHT funded investigator, and a funded investigator in the Science Foundation Ireland centre for research training in machine learning (ML-Labs). Her research integrates artificial intelligence, computer vision, and clinical medicine to develop interpretable AI solutions for medical diagnostics. Key focus areas include fetal ultrasound imaging, cardiac MRI reconstruction, neuroimaging for Alzheimer's disease and multiple sclerosis, and biomarker discovery for conditions like lymphangioleiomyomatosis and placenta accreta spectrum. She pioneers techniques in diffusion models, explainable AI, and multi-modal learning to address challenges in low-data medical scenarios. Analysis of her recent publications reveals dominant trends in applying generative models for medical data augmentation, developing uncertainty-aware diagnostic systems, and creating interpretable clinical AI tools. Her work consistently targets high-impact clinical applications including fetal development monitoring, cardiovascular disease management, and neurological disorder detection, with strong emphasis on real-world clinical implementation. Scientific recognition includes: 2019 InterTrade Ireland FUSION Project Exemplar Award (with Axial Medical Printing Ltd.) Three Enterprise Ireland Commercialisation Fund awards as Principal Investigator Horizon Europe consortium funding for SMASH-HCM project (Stratification, Management, and Guidance of Hypertrophic Cardiomyopathy Patients using Hybrid Digital Twin Solutions) Prof. Curran leads significant research funding initiatives including Horizon Europe and multiple Enterprise Ireland awards. Her group actively collaborates with industry partners like Axial Medical Printing Ltd. and participates in national research centers such as INSIGHT and ML-Labs, driving translational AI research from bench to bedside. The UCD machine learning in medical imaging and diagnostics lab ( https://www.ucd-ml-mi.com/ ) serves as her primary research hub, fostering interdisciplinary collaborations between computer scientists, clinicians, and biomedical engineers to advance clinical AI solutions.

Md. Abul Hassan Sameeمشاهده پروفایل

دانشیار

• Machine Learning
• Single-cell and spatial omics
• Comparative Genomics
• +۵ مورد دیگر

Md. Abul Hassan Samee is an Associate Professor at Baylor College of Medicine , specializing in Integrative Physiology . He is affiliated with the Computational and Integrative Biomedical Research Center (CIBR) , THINC@BCM , and the Cardiovascular Research Institute (CVRI) . Education: Postdoctoral Fellowship at Gladstone Institutes, University of California San Francisco PhD in Computer Science from University of Illinois Urbana Champaign Research Interests: Development of machine learning algorithms for biological datasets Single-cell and spatial omics analysis Comparative genomics in regeneration and aging Computational models for cancer and neurodegenerative diseases Publications focus on spatial transcriptomics, cardiac regeneration, and interpretable AI in genomic research. Recent projects include SPaSE for pathology scores and GraphAge for epigenetic aging. Grants from the National Institutes of Health support his work on Alzheimer's disease and MYH7 variant interpretation.

• Medical Augmented Reality
• Computer Vision
• Medical Image Analysis
• +۳ مورد دیگر

Ulrich Eck is a Professor and Vice-Director of the Chair for Computer Aided Medical Procedures at the Technical University of Munich (TUM). His research focuses on Medical Augmented Reality for surgery and training, Intra-Operative Navigation , and Haptic-Enabled Simulators . Key Research Areas: Medical Augmented Reality Computer Vision Robotics Surgical Data Science Human-Computer Interaction Notable Scientific Awards: Best Student Conference Paper Honorable Mention, IEEE ISMAR 2024 Best Paper Award, MICCAI 2022 Special Issue Best Demo Honorable Mention, IEEE VR 2021 Best Teaching Award, TU Munich 2020 Chancellor's Letters of Commendation, University of South Australia 2011 Best Demo Award, IEEE ISMAR 2011

• Architectural history
• Urban history
• Port-city studies
• +۵ مورد دیگر

Taishi Watanabe is Associate Professor at Waseda University’s School of Creative Science and Engineering, where he has taught since 2012. Holding a Ph.D. in Engineering from the University of Tokyo, he specializes in architectural and urban history, port-city studies, and design theory, and maintains an active architectural practice. Education: Doctor of Engineering, Department of Architecture, Graduate School of Engineering, The University of Tokyo (2009-2013) M.S. equivalent, Waseda University Faculty of Science and Engineering (2003-2005) B.S., Waseda University School of Science and Engineering (1999-2003) Research Interests: Watanabe investigates the historical evolution of port cities through their “blue infrastructure,” especially warehouse networks that mediate sea-land interfaces. Comparative studies between Japanese and Finnish National Romanticism, shrinking-population construction methods, and the concept of “cross-over architecture” that transcends functionalist typologies constitute his current theoretical core. Publications & Creative Work: He is author of the monograph Tokyo Coastal Theory (2017) and the exhibition catalogue Unity Architecture (2021), and has designed realized houses such as Morris House and Xover House . His articles trace waterfront transformations in Tokyo, Osaka and Nagoya, and explore ritual space, parking morphology and Olympic legacy narratives. Awards: Architectural Institute of Japan New Designer Award 2022 Japan Finland Design Association Award 2021 Residential Architecture Gold Award, Tokyo Association of Architects and Building Engineers 2021 Residential Architecture Encouragement Prize 2018 Grants & Projects: He currently leads five competitive grants including JSPS Kakenhi studies on Helsinki port models and Finnish artist colonies, and practical projects on “container gardens” and Okinawa “Murayashi” community networks. Teaching & Service: Watanabe chairs the Architectural Construction History Working Group of the Architectural Institute of Japan, serves on Minato Ward Landscape Council, and teaches design studios from undergraduate to doctoral level every semester.

• Audio-Visual Speech Recognition
• Machine Learning
• Signal Processing
• +۹ مورد دیگر

Satoru Hayamizu is a Professor at Waseda University 's Green Computing Systems Research Organization , with a career spanning over four decades. His research focuses on Audio-Visual Speech Recognition , Machine Learning , and Medical Informatics , as evidenced by 126 publications and an h-index of 18. Education: The University of Tokyo (PhD in Mechanical Engineering) Prior affiliations: Gifu University (2002-), National Institute of Advanced Industrial Science and Technology (1981-2001) Research Interests include: Audio-visual speech recognition with sparse representation and DNN techniques Development of low-cost CNN-based road condition detection systems Swallowing function evaluation using acoustic and image processing Human behavior analysis for service operation estimation Research Trends reveal consistent work in multimodal signal processing (2006-2024), deep learning applications (2012-2024), and medical diagnostic systems (2006-2017). His publications show integration of sparsity modeling (2012-2021), industrial equipment diagnostics (2018-2021), and social impact technologies (2013-2024). Research Projects funded by Japan Society for the Promotion of Science include: Swallowing timing estimation (2018-2021) Multimodal silent speech recognition (2016-2020) ICT-based piano learning systems (2013-2016) Keyword display mechanisms (2010-2012) Labs & Collaborations include partnerships with Satoshi Tamura (co-author on 12+ papers), Hidekazu Fukai , and Chiyomi Miyajima . His work bridges academic research and industrial applications , particularly in manufacturing AI (2024 book) and Timor-Leste infrastructure monitoring.

• Computer Vision
• Egocentric Vision
• Video Understanding
• +۳ مورد دیگر

Dima Damen is a Professor of Computer Vision at the School of Computer Science, University of Bristol , and leads the Machine Learning and Computer Vision Group . She also holds a position as Senior Research Scientist at Google DeepMind. Her research focuses on egocentric vision , video understanding , and action recognition , with significant contributions to human routine modeling , hand-object interaction analysis, and multimodal learning from real-world environments. EPSRC Early Career Fellow (2020-2025) ELLIS Society Member Active in organizing workshops and challenges (e.g., EPIC, Ego4D, EgoVis) Her recent work explores temporal discrimination in video captioning ( It's Just Another Day ), active memory representations for long egocentric videos ( AMEGO ), and hand-object interaction referral ( HOI-Ref ). She has co-authored 15+ articles in top venues like CVPR, ICCV, NeurIPS, and IJCV, with a focus on egocentric scene modeling , audio-visual binding , and cross-scenario generalization . Awards include the Best Paper at ACCV 2024 and recognition as an Outstanding Reviewer at CVPR 2020 . She has supervised numerous PhD students and postdocs , including Adriano Fragomeni, Jacob Chalk, Alexandros Stergiou, and others who now hold academic or industry roles. Her funded projects include VISUAL AI (EPSRC Programme Grant) and UMPIRE (EPSRC Early Career Fellowship), supporting innovations in egocentric dataset creation , real-time tracking , and industrial workflow assistance .

Lilah Fowlerمشاهده پروفایل

دانشیار

• Digital Art
• Sculpture
• Textile Art
• +۷ مورد دیگر

Lilah Fowler is a British-Japanese artist and Associate Professor at the Slade School of Fine Art, University College London, where she has taught Fine Art Sculpture across undergraduate, MA, and PhD levels since 2010. She is also an external examiner for BA(Hons) Fine Art at Goldsmiths, University of London, and has served as a visiting lecturer at institutions including Wimbledon School of Art, University for the Creative Arts, UCL's Department of Mathematics, and Hong Kong Baptist University. As a Fellow of The Higher Education Academy since 2014, she bridges academic rigor with contemporary art practice. Fowler's research explores the intersection of digital, analogue, and organic methods, moving fluidly between hand-weaving, ceramics, algal growths, and bespoke image-generating software. Her work investigates the geopolitics of hyper-landscapes, focusing on how infrastructure shapes our environment and experience. She examines the potential for abstraction inherent in any medium and the possibilities of translation between materials along the spectrum from plastic to pixel. Her practice is deeply collaborative, frequently engaging with researchers from diverse fields including mycology, plant science, biochemistry, quantum physics, computer programming, mathematics, and traditional weaving. Analysis of Fowler's recent exhibitions reveals consistent themes of material transformation, cultural hybridity, and the relationship between digital systems and physical landscapes. Her work often draws on Japanese arts and crafts traditions while addressing contemporary issues of identity, colonial legacies, and environmental change. The progression from early explorations of spatial perception to more recent large-scale public commissions demonstrates an evolving focus on community engagement and the political dimensions of public space. Fowler's teaching and practice are characterized by a commitment to interdisciplinary collaboration and experimental methodologies. She has developed significant partnerships with institutions including Meta (for the 'Cloudbursts' commission), Firstsite Colchester, and various public spaces across the UK. Her public artworks, such as 'Migrations' in Cambridge (2024), 'Commonplace' in London (2017), '14537/9431' in Bristol (2017), and 'Lantern Forest' at Whipps Cross Hospital (2020), demonstrate her ability to create meaningful interventions in diverse urban contexts. Her studio practice operates at the intersection of traditional craft techniques and cutting-edge digital technologies, creating installations that challenge viewers' perceptions of space, materiality, and cultural identity. Fowler's work frequently explores the imprint of time on objects and how this influences our relationship to both physical and digital environments. Through her teaching and practice, she continues to push the boundaries of contemporary sculpture and installation art.

• Behavioral Science
• Epidemiology
• Digital Interventions
• +۷ مورد دیگر

Dr. Marc A. Adams serves as the Assistant Dean of Education and Interim Program Director of the MPH in the School of Technology for Public Health at Arizona State University, while also holding a Professor position in the College of Health Solutions. He maintains additional affiliations with the Institute for Social Science Research as both a faculty member and Affiliated Faculty, and serves as a Senior Global Futures Scientist within the Global Futures Scientists and Scholars program. His academic leadership spans multiple institutional units focused on public health innovation and research. Dr. Adams' educational background includes a PhD in Public Health from the University of California, San Diego and San Diego State University (2009), an MPH in Public Health from San Diego State University (2003), a BA in Psychology from San Diego State University (2001), and postdoctoral training in Cardiovascular Epidemiology and Prevention at the University of California, San Diego (2011). As a behavioral scientist and epidemiologist, Dr. Adams' research spans the intersection of digital health interventions, physical activity promotion, and behavioral nutrition within diverse neighborhood contexts. His work integrates advanced AI and deep learning techniques to map pedestrian environment features across thousands of US neighborhoods, revealing critical inequities in built environments. He develops and tests interventions that consider how urban planning features interact with behavior change strategies to increase physical activity and healthy eating, particularly among underserved populations. His methodological expertise includes epidemiologic methodology, clinical trial design, and the development of ecological models for understanding behavior-environment interactions. Dr. Adams is an active member of the International Physical Activity and Environment Network (IPEN), contributing to international comparative research on how city design influences physical activity levels across 14 global cities. Dr. Adams' recent publications reveal a strong focus on neighborhood walkability, digital health interventions, and the use of AI to analyze built environments for public health purposes. His research consistently examines how environmental features moderate the effectiveness of physical activity interventions, with particular attention to socioeconomic disparities. The methodological approaches in his work span from traditional epidemiological studies to cutting-edge computer vision applications for analyzing street-level imagery at scale. Dr. Adams has secured substantial research funding from multiple sources including NIH, American Heart Association, and Robert Wood Johnson Foundation. His current projects include "Developing AI-measures of Pedestrian Environment Features for Physical Activity and Cancer Prevention in Rural Communities" (NIH/NCI, $413,673), "PED-PHAM: An Automated and Scalable Spatial Tool" (NIH/NHLBI SBIR, $275,000), and "WalkIT Arizona: Neighborhood walkability and moderation of adaptive interventions for physical activity" (NIH/NCI, $2,620,000). He serves as Principal Investigator on multiple projects while also collaborating as Co-Investigator on large-scale international studies through IPEN. His grant portfolio demonstrates expertise in both intervention development and environmental measurement, with a consistent focus on translating research findings into practical applications for public health practice. Dr. Adams leads research teams focused on developing and validating AI tools for assessing pedestrian environments and testing how these environmental features interact with digital health interventions. His work with the International Physical Activity and Environment Network connects him to a global consortium of researchers examining built environment influences on physical activity across diverse cultural contexts. His laboratory work bridges computer science, public health, and urban planning disciplines to create innovative approaches for measuring and improving neighborhood environments that support healthy behaviors.

• Machine learning
• Artificial intelligence
• Information systems
• +۱ مورد دیگر

Brooks Paige serves as an Associate Professor in Machine Learning at University College London's Department of Computer Science, where he leads research at the intersection of artificial intelligence, computational biology, and environmental science. His work bridges theoretical machine learning with high-impact applications in drug discovery, genomics, and climate modeling. His research portfolio spans: Machine Learning (core methodology development) Artificial Intelligence (generative models and deep learning) Information Systems (data-intensive applications) Cognitive and Computational Psychology (human-AI interaction aspects) Analysis of his 56 publications (2021-2025) reveals a dominant focus on generative modeling for molecular design, particularly protein-ligand binding prediction and antibody-epitope analysis. His methodological innovations include Gibbs sampling variants, Gaussian processes on non-Euclidean domains, and active learning frameworks, applied across biomedical and environmental domains including Arctic sea ice forecasting and urban analytics. No scientific awards are documented in available sources. Similarly, student advisement records, research grant details, laboratory facilities, and collaborative team structures remain unspecified in the current dataset.

Guohui Zhangمشاهده پروفایل

استاد

• Large-Scale Transportation Systems Modeling and Simulation
• Traffic Control and Operations
• Traffic Detection and Sensor Data Analysis
• +۳ مورد دیگر

Guohui Zhang is a Professor of Civil, Environmental and Construction Engineering at the University of Hawaii, where he has served since 2016, progressing from Assistant Professor (2016-2018) to Associate Professor (2018-2022) before attaining his current rank in 2022. His expertise spans transportation systems engineering with a focus on data-driven solutions for modern mobility challenges. His educational foundation includes: Ph.D. in Civil Engineering, University of Washington, Seattle (2008) M.S. in Systems Engineering, Tsinghua University, China (2003) B.S. in Control Engineering, Harbin Institute of Technology, China (2000) Professor Zhang's research integrates advanced computational methods with transportation theory across six core domains: Large-Scale Transportation Systems Modeling, Traffic Control and Operations, Sensor Data Analysis, Cyber-Transportation Security, Congestion Pricing, and Safety/Security systems. His work frequently employs machine learning and statistical modeling to address real-world problems like urban mobility optimization, disaster evacuation planning, and autonomous vehicle integration. Recent projects demonstrate particular innovation in applying generative adversarial networks to traffic hotspot prediction and Bayesian methods for crash analysis under extreme conditions. Analysis of his 2018-2020 publications reveals a strong shift toward data-intensive methodologies , with 60% of recent work utilizing deep learning or advanced statistical techniques. Key thematic clusters include autonomous vehicle systems (20%), natural disaster response (15%), and impaired driving/crash severity analysis (25%), reflecting his commitment to solving transportation's most pressing safety and efficiency challenges through computational innovation. His scientific recognition includes: 2009 PTV Vision Transportation System Simulation Scientific Award (Germany) 2009 Shining STAR Award from University of Washington's TransNow UTC As Principal Investigator on 8 major grants totaling over $1.2 million, Zhang has led projects for the New Mexico Department of Transportation, SOLARIS Institute, and City of Albuquerque. His research portfolio demonstrates exceptional versatility across domains including traffic microsimulation ($37k), crash database development ($11k), autonomous vehicle intersection control ($220k), and tsunami evacuation modeling. While specific advisees aren't listed, his teaching of graduate courses like CEE 696: Transportation Data Management indicates active mentorship of transportation engineering students. Professional leadership includes Guest Editor roles for IEEE Intelligent Transportation Systems Magazine and Transportation Research Part C , plus active committee service with the Transportation Research Board.

Sanka Rasnayakaمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Sanka Rasnayaka is a Lecturer at the School of Computing, National University of Singapore , where he has worked since 2022. He previously served as a full-time Teaching Assistant and PhD candidate at the same institution. PhD in Computer Science (2021), National University of Singapore B.Sc. Engineering (Honours) in Computer Science and Engineering (2016), University of Moratuwa, Sri Lanka Research interests focus on Artificial Intelligence and Computer Vision , specifically in biometrics, privacy, and human perception. Current projects explore: Biometrics & Continuous Authentication (gait, keystroke dynamics) Large Language Models for Software Engineering Physics-Informed Generative AI for flood modeling Recent publications demonstrate expertise in: Biometric authentication systems Privacy-preserving AI Transformer architectures for behavioral analysis Generative AI in environmental modeling Teaching includes foundational courses like CS1101S Programming Methodology (co-teaching since 2022) and CS2040 Data Structures and Algorithms (AY2410). He also developed workshops on Generative AI, LLMs, and Computer Vision. Projects supervised: CRISP Platform for classroom management Live Annotation Tool for educators AI Pal for customized LLM assistants Scientific awards include multiple Teaching Excellence Awards (2024-2025) and scholarships from NUS (2017-2021).

• Computer Vision
• Metric Learning
• Image Generation
• +۳ مورد دیگر

Dr. Xinyu Zhang is a Research Fellow at the Australian Institute for Machine Learning (AIML), University of Adelaide's Faculty of Sciences, Engineering and Technology. Her research bridges computer vision and machine learning, focusing on image/video generation, self-supervised learning, and multimodal retrieval for human-centric AI applications. Zhang's current investigations include: Causal representation learning and multimodal integration Bayesian deep learning frameworks Video generation with temporal consistency Lightweight detection transformers Unsupervised person re-identification Analysis of recent publications reveals strong emphases on generative modeling innovations (especially video synthesis), efficient transformer architectures for real-time applications, and self-supervised representation learning. Her work frequently addresses the alignment between latent representations and human perception across vision-language tasks. Dr. Zhang co-supervises graduate students on projects involving multi-agent 3D scene generation and knowledge transfer in low-supervision learning. She serves as conference reviewer for premier venues including CVPR, ICCV, and NeurIPS, contributing to the advancement of computer vision research.