جست‌وجوی استاد و پژوهشگر

• Bayesian Statistics
• Environmental Science
• Climate Change
• +۵ مورد دیگر

Jonathan Rougier is a Visiting Professor at Imperial College London's Faculty of Natural Sciences (Department of Mathematics) with an honorary appointment at the University of Bristol. He specializes in statistics, focusing on applications in environmental science, climate change, and geohazards. His work bridges academia and industry, emphasizing probabilistic modeling, uncertainty quantification, and decision-support systems for natural hazards. Education & Affiliations: Honorary staff at Imperial's Mathematics and Statistics departments. Collaborates with institutions on global geophysical and climate projects. Research Interests: Bayesian methods, spatio-temporal modeling, volcanic eruption prediction, climate change impacts, and risk assessment. His research addresses challenges in environmental data analysis, including unreliable historical records and complex simulation outputs. Articles Trends: Recent work focuses on volcanic hazard modeling (e.g., Aso volcano likelihoods), Antarctic ice mass evolution, and Kalman filter applications in environmental monitoring. Themes include uncertainty quantification, multi-scale computational methods, and interdisciplinary data integration. Advising & Grants: No formal advisees listed. Contributions include methodological advancements for climate projections and hazard analysis, supported by collaborative research networks. Labs/Teams: Engages in global initiatives like the VOLCORE database and Antarctic mass balance studies, emphasizing interdisciplinary teams for geophysical and environmental challenges.

Gyanendra Pokharelمشاهده پروفایل

دانشیار

• Biostatistics
• Cancer epidemiology
• Bayesian & computational statistics
• +۴ مورد دیگر

Dr. Gyanendra Pokharel serves as an Associate Professor in the Department of Mathematics and Statistics at the University of Winnipeg. He concurrently holds an adjunct appointment as Assistant Professor in the Department of Community Health Sciences at the University of Manitoba. His educational qualifications include: Ph.D. in Applied Statistics from the University of Guelph, Canada M.Sc. in Applied Mathematics from Wilfrid Laurier University, Canada M.Sc. in Pure Mathematics from Tribhuvan University, Nepal B.Sc. in Mathematics from Tribhuvan University, Nepal Dr. Pokharel's research spans Biostatistics , Cancer Epidemiology , Bayesian & Computational Statistics , and Infectious Disease Modeling . He develops advanced methodologies for spatial infectious disease surveillance, including emulation-based inference and ensemble learning techniques. His cancer epidemiology work focuses on breast cancer stage-shifting through simulation studies, while his rheumatology research employs network meta-analysis to evaluate treatment effectiveness. His expertise in Statistical Learning enables innovative classification and clustering approaches for complex health data. Analysis of his 15 most recent publications (2014-2024) reveals a dominant focus on infectious disease modeling (60% of articles), particularly spatial-temporal methods and machine learning applications. Biostatistics contributions (40%) center on cancer epidemiology and rheumatology, featuring network meta-analyses and discrete-choice experiments for patient preference studies. His work consistently integrates computational statistics with real-world health challenges. No specific scientific awards are documented in the provided materials, though his research is competitively funded. Dr. Pokharel actively mentors students through his NSERC Discovery Grant-funded projects. He teaches advanced statistical computing courses (STAT 2903/3904) and core statistics courses (STAT 1301/1302), with past instruction in statistical learning (STAT 4103). Prospective graduate students in biostatistics, spatial statistics, or machine learning applications are encouraged to contact him for research opportunities.

Joe Watsonمشاهده پروفایل

پژوهشگر

• Statistics
• Environmental Statistics
• Spatio-Temporal Modeling
• +۱ مورد دیگر

Joe Watson is a researcher affiliated with the Department of Statistics at the University of British Columbia (UBC), Faculty of Science. His work focuses on statistical methodology for environmental and spatio-temporal data analysis. Research Interests: Joe's research addresses challenges in environmental statistics, particularly preferential sampling and spatio-temporal modeling. His studies provide theoretical frameworks and computational tools for analyzing complex ecological and environmental datasets. Publications: His notable contributions include advancements in Monte Carlo testing for sampling bias, general theories for environmental network sampling, and multi-data-type species distribution models.

Jun Zhuمشاهده پروفایل

استاد

• Spatial statistics
• Spatio-temporal statistics
• Markov random fields
• +۷ مورد دیگر

Jun Zhu is a Professor of Statistics at the University of Wisconsin-Madison, affiliated with the Department of Statistics and cross-listed in the Department of Entomology, Department of Biostatistics and Medical Informatics, and the Center for Demography and Ecology. His research focuses on spatial and spatio-temporal statistical methodologies, applied to ecological, environmental, agricultural, and health systems. He holds prestigious awards including the Distinguished Achievement Medal from the American Statistical Association and election to the International Statistical Institute. Education: PhD in Statistics (Iowa State University, 2000), MSE in Mathematical Sciences (Johns Hopkins University, 1995), BA in Mathematics and Computer Science (Knox College, 1994). Research emphasizes methodological development for spatial data analysis, with applications in agriculture, ecology, and public health. Key contributions include cluster detection algorithms, spatial regression models, and spatio-temporal analyses of environmental and demographic phenomena. His work bridges theoretical statistics with interdisciplinary collaborations, addressing real-world challenges such as disease mapping and climate change impacts. Scientific Awards: Distinguished Achievement Medal (ASA) Elected Member (International Statistical Institute) Fellow (American Statistical Association) Presidential Award (American Society of Agronomy) Teaching and advising: Leads courses on spatial statistics, time series analysis, and statistical consulting. Advises on methodological and collaborative projects at the intersection of statistics and environmental science. Labs/Teams: Active in interdisciplinary research teams focused on spatial demography, ecological modeling, and statistical ecology.

Mahdi Ahmadiمشاهده پروفایل

استادیار بالینی

• Environmental Science
• Air Quality Management
• Energy Technology
• +۳ مورد دیگر

Mahdi Ahmadi is a Clinical Assistant Professor in the Department of ITDS at the University of North Texas. His work focuses on environmental engineering, air quality management, and the intersection of emerging technologies like AI with societal impacts. His research spans topics such as residential wood stove emissions, energy transition strategies, and the effects of shale gas activities on ozone pollution. Dr. Ahmadi’s research interests include pollutant emissions characterization, duty cycle testing protocols for heating appliances, and predictive modeling of environmental pollutants. He has contributed to studies analyzing NOx emissions from diesel trucks, the impact of wood species on heater emissions, and the application of AI in labor market analysis. His publications demonstrate a strong focus on bridging technical innovation with real-world environmental challenges, including renewable energy systems, climate change mitigation, and regulatory policy assessment. Notable projects include evaluations of EPA certification programs for wood heaters and investigations into the geographic implications of home electrification.

Hyowon Banمشاهده پروفایل

استاد

• Geographic Information Science
• Spatio-temporal Analysis
• Semantic Uncertainty
• +۶ مورد دیگر

Hyowon Ban, Ph.D., is a Professor in the Department of Geography at California State University, Long Beach (CSULB) . Her research integrates Geographic Information Science with multidisciplinary perspectives across geography, mathematics, art, dance, music, and psychology , emphasizing spatio-temporal analysis, semantic uncertainty, and digital cartography . She contributes to visual analytics and the representation of space in complex systems. Education: Ph.D., Geography, The Ohio State University, 2009 MA, Social Studies (Geography), Ewha Womans University, 2001 BA, Social Studies (Geography), Ewha Womans University, 1999 Research Trends: Ban’s work addresses uncertainty in geospatial concepts , including exurban areas and undersea features , using GIScience and visualization tools . She explores interdisciplinary applications of spatio-temporal analysis in fields like dance and urban development. Publications: Her 15 most recent works span GIScience , choreographic data analysis , and urban ontologies , with a focus on dynamic movement modeling and marine geographical standardization . Notable trends include collaborations with Ola Ahlqvist and contributions to interactive geovisualization software interfaces. Advising: Ban has chaired or served on thesis committees for CSULB graduate students, including Molan Choi (hydrology), Stephen Strand (motorsport analysis), and Scott Winslow (historical geography). Labs & Collaborations: She is affiliated with CSULB’s Geo-SpACE Lab and has collaborated with the Korea Hydrographic and Oceanographic Agency on marine feature name standardization projects since 2012.

Godwin Y. Dogbeyمشاهده پروفایل

پژوهشگر

• Biostatistics
• Design and Analysis of Experiments
• Biometry
• +۱۲ مورد دیگر

Godwin Y. Dogbey, PhD, is a biostatistician affiliated with the University of Iowa in the Carver College of Medicine , Department of Internal Medicine . With over 20 years of experience, he specializes in statistical consulting, teaching, and multidisciplinary research spanning biomedical, clinical, social, and economic domains. Design and analysis of experiments Biostatistics and biometry Systematic reviews and meta-analyses Econometrics and psychometric methods IRB compliance and human subject research His research focuses on applying statistical methodologies to address complex issues in public health , nutrition , epidemiology , and health equity . Recent work employs advanced techniques like structural equation modeling , Bayesian methods , and spatio-temporal analysis to study diseases such as diabetes, hypertension, and malaria in Ghana, along with socioeconomic and environmental determinants. Publications highlight his expertise in methodological rigor across biomedical and social sciences. Dr. Dogbey contributes to multidisciplinary teams, bridging statistical insights with clinical and social research. He has extensive experience supporting medical students, residents, and faculty through statistical advisory roles and IRB collaboration.

Andrew B. Lawsonمشاهده پروفایل

استاد

• Spatial Biostatistics
• Bayesian Statistics
• Hierarchical Multilevel Modeling
• +۳ مورد دیگر

Andrew B. Lawson is a Professor in the Department of Public Health Sciences at the Medical University of South Carolina (MUSC), part of the College of Medicine. He holds a PhD in Spatial Statistics from the University of St. Andrews, UK. His research focuses on Bayesian statistics, spatial biostatistics, and hierarchical multilevel modeling, with over 160 journal articles and 10 authored books, including seminal works like *Handbook of Spatial Epidemiology* and *Bayesian Disease Mapping*. He is an MUSC Distinguished Professor and ASA Fellow, and serves as founding editor of *Spatial and Spatio-temporal Epidemiology*. His work includes advising the WHO on disease mapping and risk assessment. Education: PhD in Spatial Statistics (University of St. Andrews, UK). Research interests span spatial epidemiology, statistical methods for disease surveillance, and computational tools like OpenBUGS and INLA. Recent work addresses cancer survival disparities and spatial health policy applications. Notable awards include the MUSC Distinguished Professor title and ASA Fellowship. He has led numerous international short courses on Bayesian disease mapping and spatial epidemiology techniques.

• Intelligent Transportation Systems
• Machine Learning
• Computer Networks
• +۳ مورد دیگر

Zongtao Duan is an active researcher with a focus on intelligent transportation systems, cybersecurity, and machine learning applications. His work spans areas including traffic flow prediction, silent data corruption detection, vehicular networks security, and deep learning architectures for autonomous systems. He collaborates frequently with colleagues like Lei Tang and Junchi Ma, publishing in top-tier journals such as IEEE Transactions on Intelligent Transportation Systems and Future Generation Computer Systems. His research emphasizes practical solutions for urban mobility challenges, including ridesharing optimization and railway congestion control, while also addressing foundational issues in hardware reliability and error propagation modeling. Recent contributions include advancements in multimodal retrieval systems and knowledge graph applications in scientific data management. Zongtao has contributed to over 40 peer-reviewed publications since 2004, with notable work in 2025 exploring SDC evaluation frameworks and scenario-based testing for autonomous vehicle safety. His interdisciplinary approach bridges computer science, transportation engineering, and cybersecurity to address real-world technical challenges.

• French Language Practice
• Romance Studies
• Computer Vision
• +۳ مورد دیگر

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Manuel Felipe Muciéntes Molina is a Professor of Computer Science and Artificial Intelligence at the Research Center on Intelligent Technologies (CiTIUS), University of Santiago de Compostela (USC). He holds a PhD from USC (2002) focusing on fuzzy logic-based control systems for autonomous robots. His research emphasizes machine learning applications in computer vision, process mining, robotics, and healthcare. He teaches courses like Statistical Learning, Supervised Machine Learning, and Deep Learning. Key Projects: RAI4P: Responsible AI for Process Mining 2.0 QC4SUSTEX: Authentication and security of sustainable textiles CAREBOT: Intelligent robotic units for hospital care Research Interests: Machine learning, computer vision (object detection, tracking), process mining, robotics navigation, and healthcare AI applications. Labs/Teams: Leading the Grupo GSI (Systems Intelligence) within CiTIUS, focusing on intelligent systems and AI-driven solutions.

JOSE RAMON RIOS VIQUEIRAمشاهده پروفایل

استاد

• Spatial Data Management
• Environmental Data Integration
• Geographic Information Systems (GIS)
• +۶ مورد دیگر

Jose Ramon Rios Viñuela is a Professor at the Department of Electronics and Computing, University of Santiago de Compostela, affiliated with the Higher Technical School of Engineering. He holds a PhD from the University of A Coruña (2003) for his thesis on spatio-temporal data management. His research focuses on spatial databases, environmental data integration, smart city technologies, geographic information systems (GIS), and spatio-temporal data modeling. Key projects include TRAFAIR (traffic-air quality analysis), COGRADE research group (computer graphics and data engineering), and GeoHbbTV frameworks for geospatial interactive media. He leads efforts in distributed spatial data processing, semantic interoperability, and sensor network integration for environmental monitoring. Notable contributions include frameworks like TAQE (traffic-air quality modeling), GeoSPARQL query systems, and MeteoSIX meteorological platforms. His work bridges earth sciences with public accessibility through smart environmental infrastructures, emphasizing citizen engagement and open data dissemination. Current research trends explore big data analytics for geospatial applications, federated environmental data systems, and low-cost sensor networks for urban sustainability. He collaborates on projects involving air quality forecasting (GRAL model), spatial join optimization in large data lakes, and semantic mediation of environmental observation systems. His academic contributions span over 60 peer-reviewed publications across GIS, database systems, and environmental informatics.

Elaine Fergusonمشاهده پروفایل

پژوهشگر

• Infectious Disease Ecology
• Epidemiology
• Rabies Control
• +۷ مورد دیگر

Dr. Elaine Ferguson is a Researcher at the University of Glasgow , affiliated with the Infectious Disease Ecology group. Her email is Elaine.Ferguson@glasgow.ac.uk . Research Interests : Rabies control, vaccination campaigns, epidemiology, zoonotic diseases, statistical modeling, and public health policy. Key Collaborations : Projects in Tanzania, Bangladesh, and global health initiatives. Her recent work focuses on mathematical modeling of rabies transmission and control strategies, community engagement in vaccination campaigns, and spatio-temporal analysis of disease dynamics. She has contributed to genomic studies and public health interventions. Supervision : Supervised Luka, Martha Muthina in a project on rabies control in East Africa.

Natalia Baileyمشاهده پروفایل

مدرس ارشد

• Cross-sectional dependence
• Factor models
• Spatial models
• +۴ مورد دیگر

Natalia Bailey is a Senior Lecturer at the Department of Econometrics and Business Statistics at Monash University. She holds a PhD from Queen Mary, University of London, and previously served as a Research Associate at the University of Cambridge. Before joining Monash in 2017, she was a Lecturer at Queen Mary, University of London, and worked in the private sector in London for eight years as an economist/strategist at institutions including Citigroup, Lehman Brothers, and Merrill Lynch. Her research focuses on cross-sectional dependence, factor models, and spatial models, with applications to finance, macroeconomics, and climate change impacts on agriculture. Bailey has contributed to projects like the 'Big Data Analytic Approach to Assessing Impacts of Harmful Temperatures on Wheat Crops in Northern Victoria,' where she collaborated with experts to model agricultural resilience under climate change. Her recent work includes analyzing US house prices using spatial models with heterogeneous coefficients and developing the RMT-based LM test for cross-sectional independence in panel data. Bailey’s research bridges theoretical econometrics with practical applications, addressing issues such as climate change, agricultural productivity, and economic forecasting.

• Statistics
• Machine Learning
• Data Science
• +۳ مورد دیگر

Diego Nascimento is an Assistant Professor in Statistics at NEOMA Business School. His research focuses on statistical modeling, machine learning, spatio-temporal dependencies, and data visualization, with applications in supply chain management, neurosciences, and industrial projects. He has published in top-tier journals like the Journal of the Royal Statistical Society Series C and Pattern Recognition , collaborating with companies in South America and healthcare institutions. His work bridges theoretical statistics with practical industry challenges, emphasizing Bayesian methods and dynamic models. Key research areas include spatio-temporal modeling, neural data analysis, and optimization techniques. Recent studies involve clinical trials on transcranial stimulation effects and predictive analytics for supply chains during the pandemic. He actively contributes to interdisciplinary projects, combining statistical rigor with real-world applications in healthcare and environmental monitoring. Award-winning research includes advancements in imbalanced classification, safety-stock prediction, and entropy analysis in clinical trials. His collaborative projects reflect a commitment to both academic innovation and industrial relevance, with ongoing work in neuroimaging and supply chain resilience.