جست‌وجوی استاد و پژوهشگر

John Shi Wen-zhongمشاهده پروفایل

استاد

• Urban Informatics for Smart Cities
• Geographic Information Science and Remote Sensing
• Artificial Intelligence-based Object Extraction and Change Detection from Satellite Imagery
• +۲ مورد دیگر

Professor John Shi Wen-zhong is Chair Professor of Geographical Information Science and Remote Sensing at The Hong Kong Polytechnic University, where he serves as Head of the Department of Land Surveying and Geo-Informatics. He also holds leadership positions as Director of the Otto Poon Charitable Foundation Smart Cities Research Institute and Director of the PolyU-Shenzhen Technology and Innovation Research Institute (Futian). Professor Shi is recognized as an international leader in uncertainty modeling and quality control for spatial data and spatial analyses, with contributions dating back to the 1990s. He currently serves as President of the International Society for Urban Informatics and Editor-in-Chief of the international journal Urban Informatics. Professor Shi's research focuses on urban informatics for smart cities, geographical information science and remote sensing, artificial intelligence-based object extraction and change detection from satellite imagery, intelligent analytics and quality control for spatial big data, and mobile mapping and 3-D modelling based on LiDAR and remote sensing imagery. His work has solved fundamental uncertainty issues in spatial data and spatial analyses, making significant contributions to geographical information science. He has authored over 300 research articles in Web of Science-indexed journals and 20 books, and has been granted 44 patents as of July 2023. Professor Shi has received numerous prestigious awards for his groundbreaking work: ESRI Award for Best Scientific Paper by the American Society for Photogrammetry and Remote Sensing (2006) State Natural Science Award (Second Award), China's highest award for fundamental research (2007) Wang Zhizhuo Award by the International Society for Photogrammetry and Remote Sensing (2012) Founder's Award by the International Spatial Accuracy Research Association (2020) CPGIS Distinguished Scholar Award (2021) Gold Medals at both the 2021 and 2023 Geneva Invention Expos Smart 50 Awards (2021) Gold Medal in Asia International Innovative Invention Exhibition (2023) He is also listed among the world's top 2% most cited researchers according to Elsevier BV's standardized citation indicators. Professor Shi has been elected as an Academician of the International Eurasian Academy of Sciences and is a Fellow of the Academy of Social Sciences (UK), the Royal Institution of Chartered Surveyors, and the Hong Kong Institute of Surveyors.

Ali Mansourianمشاهده پروفایل

استاد

• Geospatial Artificial Intelligence
• Spatial Data Infrastructures
• Multi-Criteria Decision Analysis
• +۷ مورد دیگر

Ali Mansourian is a Professor of Geomatics at Lund University's Department of Physical Geography and Ecosystem Science, where he serves as Director of the Lund University GIS Centre and Coordinator of the GIS & RS Master Programme. He is actively involved with the United Nations Global Geospatial Information Management (UN-GGIM) Academic Network and previously served on the European Association of Geographic Information Laboratories in Europe (AGILE) council. His academic leadership spans large-scale international research initiatives and capacity-building projects funded by Erasmus+ and SIDA. Mansourian's research focuses on Geospatial Artificial Intelligence (GeoAI), Spatial Data Infrastructures (SDI), and Multi-Criteria Decision Analysis (MCDA) using multi-objective optimization techniques. His work extends to applying GIS in epidemiology and public health, disaster risk management, land-use planning, climate change, environmental management, and sustainability. His research portfolio demonstrates a strong interdisciplinary approach, bridging geospatial technology with critical societal challenges. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with geospatial analysis, particularly in health applications, environmental monitoring, and climate change impacts. The research shows increasing emphasis on spatial ensemble learning, remote sensing applications, and the development of GeoAI tools that make geospatial analysis more accessible through natural language interfaces. His work spans multiple continents, with significant contributions in Africa, Europe, and Asia. Mansourian has extensive experience supervising PhD students and postdoctoral researchers, though specific student names aren't listed in the provided materials. He has coordinated numerous large-scale international projects including Geo-Academy, INTEGRAL, CADEO, and SWEMENA, demonstrating significant grant acquisition and management expertise. His leadership extends to evaluating proposals for major European research grant programs and serving as an invited evaluator for PhD theses. As Director of the Lund University GIS Centre and active member of multiple international networks, Mansourian leads a dynamic research environment focused on advancing geospatial technologies and their applications. His teams work at the intersection of traditional GIScience and emerging artificial intelligence approaches, creating innovative solutions for complex spatial problems across multiple domains including public health, environmental management, and sustainable development.

Sonja Kivinenمشاهده پروفایل

عضو هیئت علمی

• Climate change impacts on Arctic ecosystems
• Remote sensing applications in biodiversity mapping
• Geoinformatics and environmental data analysis
• +۳ مورد دیگر

Sonja Kivinen is a University Lecturer in Geoinformatics at the Department of Geographical and Historical Studies, Faculty of Social Sciences and Business Studies, University of Eastern Finland. Her research focuses on the intersection of climate change impacts, remote sensing applications, and sustainable resource management in Arctic environments. University Lecturer in Geoinformatics Department of Geographical and Historical Studies Faculty of Social Sciences and Business Studies University of Eastern Finland Research Interests Climate change effects on Arctic ecosystems Remote sensing for biodiversity mapping Environmental justice in natural resource management Post-mining land use strategies Integration of practitioner knowledge with scientific data Geospatial analysis of environmental patterns Key Publication Trends Her recent publications (2017-2024) demonstrate expertise in combining remote sensing with ecological monitoring to address issues like biodiversity conservation in boreal forests, reindeer husbandry adaptation to changing climatic conditions, and mining conflict resolution . She frequently collaborates with multidisciplinary teams across Finland and Europe. Projects Kivinen contributes to research groups including CEMMS (Research Center for Mining, Minerals and Society) and Digital Geosciences , both active since 2020. Her work often addresses policy implications through reports for the University of Eastern Finland and Finnish Environment Institute.

Nina Küpperمشاهده پروفایل

پژوهشگر

• Satellite Remote Sensing
• Mining Engineering
• Machine Learning Applications in Geosciences
• +۳ مورد دیگر

Nina Küpper is a Senior Engineer at the Institute of Mineral Engineering, RWTH Aachen University, specializing in satellite remote sensing for mining residue characterization and digital education in mining. She leads the SatMine research project, integrating satellite imaging with environmental monitoring. Research Focus: Machine learning applications in mine waste identification, VR-based technical training, and copper sediment distribution analysis. Projects: SatMine, VR-Mine, MyScore mobility initiative. Technologies: Remote sensing, geospatial AI, holographic tools, and virtual mineralogical showrooms. Her work bridges environmental sustainability with mining education, emphasizing digital innovation and international collaboration.

• Health Services Research
• Systems Engineering in Healthcare
• Health Equity
• +۴ مورد دیگر

Inas S. Khayal serves as Associate Professor at The Dartmouth Institute and Biomedical Data Science at Dartmouth College's Geisel School of Medicine, with an adjunct appointment in Computer Science. She leads the Sustainable Health Lab focused on improving chronic care delivery through model-based systems engineering and data science. PhD, University of California, San Francisco PhD, University of California, Berkeley B.S., Boston University Dr. Khayal's research addresses multi-level interconnected healthcare systems, specializing in implementation science, quality measurement, health equity, and organizational behavior. Her work bridges data science with practical healthcare delivery challenges, particularly in palliative and end-of-life cancer care where she investigates racial disparities through clinically informed machine learning approaches. She develops dynamic visualization tools and heatmaps to translate complex data into actionable insights for healthcare systems. Her recent publications reveal a strong focus on identifying and addressing healthcare disparities, with innovative methodologies combining systems engineering and data science. The research consistently emphasizes actionable solutions for hospitals to tailor interventions based on local resources and patient population characteristics. American Cancer Society Health Equity and Access to Care Research Scholar Award (RSG-22-128-01-HOPS) NIH NIA P01AG019783 Core Leadership for Alzheimer's Disease research Dr. Khayal actively mentors PhD students and computer science senior thesis projects while teaching Health Informatics and Systems Thinking courses. Her Sustainable Health Lab develops process tools to help hospitals reduce healthcare disparities through tailored solutions. Current projects include a Web-Based Peer Support Network for care partners of seriously ill patients and research on hospital-level factors influencing palliative care disparities.

• Watershed Modeling
• Surface/Groundwater Interaction
• Water Quality
• +۳ مورد دیگر

Aaron Mittelstet is an Associate Professor in the Biological Systems Engineering Department at the University of Nebraska-Lincoln, with a courtesy appointment in the School of Natural Resources. His research focuses on watershed modeling, surface/groundwater interactions, water quality, and streambank erosion mitigation. Ph.D., Biosystems and Agricultural Engineering, Oklahoma State University (2015) M.S., Environmental Science, Oklahoma State University (2009) B.S., Zoology, Oklahoma State University (2000) His work integrates remote sensing, machine learning, and field data to address agricultural water sustainability and groundwater contamination. Recent publications emphasize groundwater nitrate remediation , streambed hydraulic conductivity , and climate change impacts on water resources . Scientific awards include: 2020 ASABE Superior Paper Award 2018 UNL College of Engineering Recognition for Highly Impactful Research Advised students winning multiple conference awards (2017, 2014) As Graduate Program Chair and Watershed Hydrologist, Mittelstet leads the UNL Fountain Wars Design Team and secures significant funding from USGS, NSF, and state agencies for projects like "Nitrate removal by floating treatment wetlands" and "Modeling groundwater transit time distributions" .

Craig Ramseyerمشاهده پروفایل

دانشیار

• Climate Modeling
• Hydroclimatology
• Tropical Climatology
• +۳ مورد دیگر

Craig Ramseyer is an Associate Professor in the Department of Geography at Virginia Tech, within the College of Natural Resources and Environment. He holds a Ph.D. from the University of Georgia (2016) and previously taught at Salisbury University (2016–2019). His research focuses on tropical rainfall dynamics, climate change impacts on drought and flooding, and AI/ML applications in atmospheric sciences. He leads the Ramseyer Climate Lab, emphasizing student-driven research in hydroclimatology and environmental resilience. Education: Ph.D. in Geography, University of Georgia (2016) M.S. in Geography, University of Georgia (2011) B.S. in Geographic Science, James Madison University (2009) Research Interests: Climate modeling, tropical climatology, and hydroclimatic extremes. His work investigates atmospheric rivers, Caribbean drought mechanisms, and climate change effects on ecosystems and agriculture. Recent projects include NOAA-funded studies on Caribbean drought early warning systems and NSF-funded analyses of Puerto Rico’s precipitation variability. Grants and Funding: $1.79M NSF grant (2024–2029): Examines socio-environmental resilience of Caribbean fisheries. $507K NOAA grant (2020–2023): Focuses on Caribbean drought early warning systems. $5,000 NSF Luquillo LTER grant (2017–2018): Studied El Yunque National Forest precipitation variability. Advising: Supervised M.S. students including Julia Finkhauser (2024), Tyler Gingrich (2022), Parker Henry (2022), and Tyler Stanfield (2022). Current students include Sophie Luraschi (Meteorology) and lab members focused on mountain wave dynamics and tropical climate analysis. Labs/Teams: Directs the Ramseyer Climate Lab, a student-centered group funded by NOAA, NSF, and UCAR. Research spans drought mechanisms, atmospheric river dynamics, and climate adaptation strategies in the tropics and mid-latitudes.

• Electromagnetic Theory
• Applied Mathematics
• Numerical Methods
• +۳ مورد دیگر

Dario Bojanjac is an Assistant Professor at the Department of Wireless Communications, Faculty of Electrical Engineering and Computing, University of Zagreb. His research focuses on mathematical modeling of electromagnetic processes, numerical methods for Maxwell's equations, and applied mathematics techniques like asymptotic analysis. He holds a PhD in mathematics (2015) and electrical engineering (2009), with extensive visiting scholar experience at institutions such as École Polytechnique Fédérale de Lausanne (EPFL) and the University of Michigan. Education: PhD in Mathematics (2015), Faculty of Science, University of Zagreb PhD in Electrical Engineering (2009), Faculty of Electrical Engineering and Computing His research interests include electromagnetic wave scattering, homogenization of Maxwell's equations, and computational electromagnetism. Recent work explores ground-based SAR systems, deep learning applications in radar data analysis, and flood classification using multivariate approaches. He has received awards including the Swiss Government Excellence Scholarship and Croatian Government Fellowship. His projects include satellite electromagnetic field measurements and metamaterial-based invisibility cloak realization. He participates in lab initiatives like AOLAB2 and coordinates projects such as BODYSEN and HybridAccess. Awards: Swiss Government Excellence Scholarships CIME Foundation Fellowship European Science Foundation Fellowship Bojanjac has advised on multiple research projects and contributed to educational initiatives like the MILE interactive learning system. His lab collaborations include work on sensor systems and optical communication technologies.

• Medical Informatics
• Public Health Surveillance
• Patient Matching
• +۵ مورد دیگر

Shaun J. Grannis is a leading Research Professor at the Regenstrief Institute and Indiana University School of Medicine , specializing in Medical Informatics . His work focuses on patient matching, health data interoperability, and public health surveillance. Research Interests : Patient identification strategies, privacy-preserving record linkage, synthetic data generation, and leveraging health information exchanges (HIE) for population health analytics. Articles : Over 86 publications (2002-2025) addressing real-time disease detection, data quality metrics, and EHR interoperability. Key trends include syndromic surveillance , social determinants of health , and machine learning in clinical data . Awards : Recognized for groundbreaking work in public health informatics and synthetic data applications. Collaborations : Frequent partnerships with institutions like AMIA , JAMIA , and BMC Medical Informatics on projects enhancing healthcare data systems. Grants : Involved in NIH-funded initiatives for national patient-centered research networks.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۸ مورد دیگر

Dr. Anisur Rahman is a Lecturer in Business Analytics at La Trobe Business School, La Trobe University (since July 2023). Previously, he held academic roles at Charles Sturt University, including Lecturer in Computing and Research Fellow. He holds a PhD in Computing from Charles Sturt University and a Bachelor of Computer Science and Engineering (Honours) from Rajshahi University of Engineering and Technology, Bangladesh. His research focuses on artificial intelligence, data science, machine learning, and their applications in healthcare, cybersecurity, smart cities, IoT, and agriculture. He has secured grants totaling over $22,500 for projects like 'Smart Monitoring of Rental Properties' and 'AI-Powered Clinical Decision Support System' (collaborating with St Vincent's Hospital Melbourne). He is an Associate Editor for journals including Humanities and Social Sciences Communications and Network Modeling Analysis in Health Informatics and Bioinformatics . Dr. Rahman has supervised 20 honours students and collaborates with institutions like Monash University, Swinburne University, and Wagga Wagga Base Hospital. His teaching philosophy emphasizes early student engagement, industry-relevant examples, and assessment design. He coordinates courses such as 'Artificial Intelligence and Business Automation' and 'Predictive Analytics' at La Trobe University. He has published over 50 peer-reviewed articles, with an h-index of 16 and 1,500+ citations. Key contributions include frameworks for weed species classification, lung infection detection, and cardiovascular disease prediction. He advocates for interdisciplinary research addressing healthcare, urban sustainability, and technological innovation.

Anaïs Couasnonمشاهده پروفایل

پژوهشگر

• Flood Risk Management
• Climate Change
• Environmental Science
• +۳ مورد دیگر

Anaïs Couasnon is a PhD researcher at the Department of Water and Climate Risk, Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, part of the Faculty of Science. Her research focuses on compound flood risk modeling, probabilistic methods, and global hazard assessment under climate change. She is supervised by Dr. Philip Ward and Dr. Hessel Winsemius as part of a VIDI project. Education: MSc in Hydraulic Engineering (TU Delft, 2017); BSc in Civil Engineering (McGill University, 2010). Research interests include probabilistic modeling, multivariate dependence analysis, flood risk management, and climate impacts on coastal-riverine interactions. She contributes to global datasets like COAST-RP and socio-hydrological benchmarking. Key activities include: Developing frameworks for compound flood risk assessment Modeling extreme sea-level events and storm surges Collaborating on global hazard frameworks and disaster risk reduction strategies Contributions to datasets include: COAST-RP dataset Panta Rhei socio-hydrological benchmark dataset

Charlotte Gerritsenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Sentiment Analysis
• Agent-Based Modeling
• +۷ مورد دیگر

Dr. Charlotte Gerritsen is an Associate Professor in Artificial Intelligence at the Faculty of Science, Vrije Universiteit Amsterdam (VU), and an affiliated member of the Network Institute. Her research focuses on agent-based modeling of social and emotional dynamics, emotion contagion in virtual environments, and applications of AI in crowd management, mental health, and criminology. She has published extensively on topics such as sentiment analysis, gamification, and the ethical implications of AI in public safety. Her work integrates computational methods with social science theories, addressing challenges like real-time violence detection, avatar-mediated emotion recognition, and serious gaming for resilience training. She has supervised 5 PhD theses and teaches courses on AI and Law, Artificial Intelligence, and Socially Aware Computing. Her research project, 'Integrating sentiment analysis in real-time crowd management,' highlights her commitment to bridging technology with societal needs. Education: PhD in Artificial Intelligence (implied by title 'dr.') Affiliations: Faculty of Science, Network Institute Key Research Areas: Emotion Contagion, Agent-Based Modeling, Crowd Behavior, Gamification Her articles analyze topics like emotion contagion in crowds, AI-driven crowd control, and virtual training systems. While no specific awards are listed, her prolific publication record underscores her academic impact. She advises students on criminological and computational social science projects and collaborates internationally in criminology, healthcare, and AI ethics.

Ling Changمشاهده پروفایل

دانشیار

• Remote Sensing
• Synthetic Aperture Radar (SAR)
• InSAR Techniques
• +۵ مورد دیگر

Ling Chang is an Associate Professor at the University of Twente, affiliated with the Faculty of Geo-Information Science and Earth Observation (ITC) and the Department of Earth Observation Science. She holds a Ph.D. from Delft University of Technology (2015) and an M.S.E. from Tongji University (2010). Her research focuses on statistical hypothesis testing, time series modeling, and change detection using satellite remote sensing, particularly InSAR techniques. Key projects include AlignSAR (ESA Open SAR library) and RailRadar (TUDelft-ProRail collaboration). Education: M.S.E. in Geodesy and Survey Engineering, Tongji University, China (2010) Ph.D. in Geodesy, Delft University of Technology, Netherlands (2015) Research interests emphasize InSAR applications in infrastructure monitoring, environmental geology, and disaster risk assessment. Notable achievements include the Prof. J.M. Tienstra Research Prize (2022) and leadership in the NCG talent program (2020). Teaching responsibilities include coordinating the Radar Remote Sensing course. Her work contributes to UN SDGs related to sustainable infrastructure and climate action.

Sean Smuklerمشاهده پروفایل

دانشیار

• Sustainable Agriculture
• Ecosystem Services
• Soil Health
• +۳ مورد دیگر

Sean Smukler is Associate Professor in the Faculty of Land and Food Systems at the University of British Columbia, where he directs the Centre for Sustainable Food Systems at UBC Farm and chairs Agriculture and the Environment. His research program works with farmers to develop methods for monitoring ecosystem services and enhancing biodiversity in agricultural landscapes. Research focuses on assessing farm management impacts on ecosystem functions including greenhouse gas mitigation, water quality, and soil health. Key interests include climate change adaptation/mitigation trade-offs, carbon sequestration verification, and nutrient cycling improvements across food systems. Work integrates field data with geospatial tools for landscape-scale assessments. Recent publications demonstrate consistent themes: optimizing phosphorus management in high-input agricultural systems, developing soil carbon monitoring frameworks, quantifying ecosystem services from diversified farming, and evaluating conservation practices like hedgerows. Research provides practical solutions for sustainable intensification.

Jumi Gogoiمشاهده پروفایل

پژوهشگر ارشد

• Climate-Smart Agriculture
• Remote Sensing
• Machine Learning Applications in Agriculture
• +۳ مورد دیگر

Jumi Gogoi is a Postdoctoral Research Fellow at the University of Waterloo in the Department of Earth and Environmental Sciences . She earned her PhD in February 2024 from the Institute for Resources, Environment and Sustainability at the University of British Columbia , supervised by Dr. Navin Ramankutty and Dr. Nathaniel K. Newlands. Her research bridges agricultural datasets, satellite imagery, and machine learning to develop climate-resilient farming strategies. PhD (2024): University of British Columbia (Supervisors: Dr. Navin Ramankutty, Dr. Nathaniel K. Newlands) MS Business Analytics (University of Dallas) MSc Economics (University of Bath) BA Business (Lady Shri Ram College, University of Delhi) Her work emphasizes satellite big-data and machine learning for crop yield prediction in Canada. Recent publications focus on model evaluation , data integration , and scale-dependent analytics to optimize agricultural strategies under climate change. While no awards are listed, her interdisciplinary background spans business, economics, and environmental sciences.