Prof. dr. Nico Van de Weghe is a full Professor of GIScience at the University of Ghent (UGent), affiliated with the CartoGIS research unit. His work bridges computer science, social science, and natural science through geospatial information studies, focusing on enabling machines to reason spatially (GeoAI). Since 2004, he has specialized in knowledge-based AI, particularly spatiotemporal reasoning and moving object analysis, with applications in animal behavior, criminology, healthcare, mobility, and sports. Van de Weghe's research emphasizes hybrid GeoAI systems combining knowledge-driven and data-driven approaches. Keywords include GeoAI, GIScience, Spatiotemporal Analysis, Moving Objects, and Data Mining. Recent publications highlight urban road network analysis, hybrid trajectory modeling, BIM semantic enrichment, and cycling safety studies using virtual reality.
Xi Gong is an Associate Professor in the Department of Biobehavioral Health and the Institute of Computing and Data Sciences at Pennsylvania State University, where he leads the Gong Lab. His research focuses on Geospatial Data Science, integrating GIScience, computational methods, and statistical analysis to study environmental health and social dynamics. He holds a PhD in Geographic Information Science from Texas State University, an M.Sc. from the University of Chinese Academy of Sciences, and a B.Eng. from Wuhan University. Dr. Gong's research interests include spatio-temporal data mining, environmental exposure modeling, and visual analytics for big data. His work bridges Environmental Health Science (EHS) and Spatially Integrated Social Science (SISS), addressing public health concerns through geospatial modeling and interdisciplinary collaboration. He currently accepts graduate students and postdoctoral scholars for projects in Geospatial Data Science and EHS. Education: PhD (2016, Texas State University), M.Sc. (2011, University of Chinese Academy of Sciences), B.Eng. (2008, Wuhan University) Labs/Teams: Director of Gong Lab, focused on geospatial big data analysis for health and environmental studies Advising: Mentors PhD and MS students in Biobehavioral Health and related fields
Agnieszka Leszczynski is an Associate Professor in the Department of Geography and Environment at Western University. Her research focuses on digital geographies, platform urbanism, and the intersections of technology with urban development. She leads a SSHRC-funded project examining the visual aesthetics of urban platformization across Canada, Poland, and South Africa. Additional projects include studying digital experimentation in small Canadian cities and analyzing spatial relationships between urban platforms and gentrification. She teaches courses on GIScience, digital technology, and spatial research methods. Leszczynski actively supervises graduate students exploring topics like smart cities, platform urbanism, and spatial equity. Her work bridges theoretical and applied GIScience with critical urban studies. Education details are not explicitly listed in the provided text, but her academic contributions include over two decades of research output, including co-editing Digital Geographies (SAGE, 2019) and publishing widely in leading journals. She collaborates internationally on projects like the Esri Canada GIS Centre of Excellence and engages with equity, diversity, and decolonization initiatives in academia. Her research methodologies emphasize digital-visual approaches, glitch studies, and critical analyses of spatial technologies. Recent work explores how cities use digital aesthetics to achieve 'world-class' status, while other projects assess micromobility equity and conservation mapping in Patagonia. Leszczynski’s teaching portfolio includes graduate supervision on topics such as dockless micromobility systems and smart home analysis. Key funding sources include SSHRC grants supporting comparative urban studies and technology experimentation. She mentors students through Western’s Geography People’s Society and collaborates with interdisciplinary teams on spatial equity and platform urbanism challenges.
Somayeh Dodge is an Associate Professor of Spatial Data Science in the Department of Geography at the University of California, Santa Barbara (UCSB). She leads the MOVE Lab and serves as Co-Associate Director of the UCSB Center for Spatial Studies and Data Science. Her research focuses on computational movement analysis, spatiotemporal data science, and the application of these tools to study human and ecological systems. She holds editorial roles in major journals including Journal of Spatial Information Science and Geographical Analysis. Education: PhD in GIScience (University of Zurich, 2011), MS in GIS Engineering (K.N.Toosi University of Technology, 2005), and BS in Geomatics Engineering (2003). Postdoctoral work at The Ohio State University and University of Zurich. Prior faculty positions include University of Minnesota (2016–2019) and University of Colorado, Colorado Springs (2013–2016). Research interests include wildfire impact analysis, mobility patterns during disasters, environmental vulnerability modeling, and geovisualization techniques. Her NSF CAREER project explores movement responses to environmental disruptions. Teaching focuses on GIScience, movement analytics, and spatial modeling courses. Awards: 2021 NSF CAREER Award and 2022 AAG Emerging Scholar Award. Active in editorial boards for multiple journals and serves on the Board of Directors for the University Consortium for Geographic Information Science (UCGIS). Advising: Supervises 7 graduate students in mobility analytics and environmental modeling. Labs/Teams: MOVE Lab (https://move.geog.ucsb.edu/) focuses on computational movement ecology and human mobility studies.
Professor Desheng Liu is affiliated with the Department of Geography at The Ohio State University , within the College of Arts and Sciences . His research focuses on developing spatial and statistical methodologies for environmental monitoring and ecological processes. He holds a Ph.D. in Environmental Science from UC Berkeley (2006) and additional degrees in Statistics, Environmental Science, and GIS. Education: Ph.D., 2006: Environmental Science, University of California, Berkeley M.A., 2004: Statistics, University of California, Berkeley M.S., 2003: Environmental Science, University of California, Berkeley B.E., 2001: GIS, Wuhan University, China Research interests include Remote Sensing , Spatial Statistics , GIScience , and Land Cover Change . His work emphasizes statistical modeling of spatial-temporal dynamics in environmental systems. Recent publications (2008–2012) explore topics like land-cover trajectory reconstruction, thermal infrared downscaling, and object-based classification techniques. No scientific awards are explicitly listed in the provided text. Advising and grants information is not detailed here, though his CV may contain additional details. He is involved in teaching advanced courses such as Quantitative Geographical Methods and Spatial Statistics.
Michela Bertolotto is a Professor in the School of Computer Science at University College Dublin (UCD). Her research focuses on spatio-temporal data modeling, GIScience, and applications of geospatial technologies in fields like urban planning and health informatics. She leads a research group and has supervised 19 PhD and 8 MSc students. Her work includes innovations in LiDAR-based flood risk visualization, semantic web quality assurance, and open-source spatial data analysis. Bertolotto has held roles including College Lecturer at UCD (2000–2006) and postdoctoral research positions at the University of Maine and University of Genoa. Education: BSc and PhD in Computer Science from the University of Genoa (1993, 1998). Professional achievements include over 100 publications, 24 grants (e.g., Science Foundation Ireland-funded Urban ARK project), and editorial roles at journals like the International Journal of Geographical Information Science. Awards include the UCD President's Research Award (2001) and NATO Postdoc Fellowship (1998–1999). Research interests span map personalization, volunteered geographic information (VGI), and geospatial data quality. Her lab develops tools like the LAMSkyCam (low-cost sky imaging system) and dynamic flood risk viewers. She chairs international conferences and serves on program committees for GIScience events.
May Yuan is the Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas (UT-Dallas), affiliated with the School of Economic, Political and Policy Sciences. She directs the Geospatial Analytics and Innovative Applications (GAIA) Lab. Her research focuses on space-time representation, GIS analytics, and environmental/social problem-solving (e.g., disaster risk, pollution, crime mapping). She holds a Ph.D. in Geography from SUNY Buffalo (1994) and B.S. from National Taiwan University (1987). Previously, she was Brandt Professor and Director of the Center for Spatial Analysis at the University of Oklahoma (1994–2014). Education: Ph.D. in Geography, State University of New York at Buffalo, 1994 M.A. in Geography, State University of New York at Buffalo, 1992 B.S. in Geography, National Taiwan University, 1987 Research Interests: Her work integrates space-time GIS databases with cognitive science, environmental modeling, and social dynamics. Key areas include: - Spatiotemporal query and analytics for geographic processes - GIS-based disaster risk assessment (wildfires, tornadoes) - Urban air quality modeling - Neurogeography and Alzheimer’s disease prediction using environmental complexity metrics - Deep mapping and spatial narratives. Grants & Partnerships: Supported by NSF, NASA, DoD, DHS, NOAA, EPA, and state agencies. Her GAIA Lab explores 'place' concepts in space-time analytics. Awards & Roles: Fellow, AAAS and AAG Editor-in-Chief, International Journal of Geographical Information Science (2017–present) Former President, Cartography and Geographic Information Society (2020–2021) and UCGIS (2011–2012) Member, NOAA Environmental Information Services Working Group (2016–2022) Labs/Teams: Leads the GAIA Lab, collaborating on geospatial AI, environmental health, and urban analytics.
Stephanie Rogers is an Assistant Professor of Geosciences at Auburn University's College of Sciences and Mathematics, specializing in geospatial technologies and environmental applications. She leads the GeoIDEA Lab, focusing on GIScience, water quality modeling, and environmental impacts on honey bee colonies. Her research integrates emerging technologies like drones for ecological monitoring and addresses interdisciplinary challenges such as groundwater management and pollution tracking. Education: PhD in Geosciences from the University of Fribourg, Switzerland. Research Interests: Rogers' work bridges geospatial innovation with real-world problem-solving. Key areas include: GIS-driven environmental monitoring and modeling Drone-based assessment of water quality and algal blooms Groundwater contamination dynamics and public health implications Honey bee colony health through spatial analysis Advising & Grants: Currently mentors two trainees (Bethany Foust and Mallory Jordan) and collaborates on projects funded by environmental agencies. Her grants focus on geospatial data integration for ecological decision-making. Labs/Teams: GeoIDEA Lab coordinates multidisciplinary efforts in environmental geoscience, with active projects in Alabama's Black Belt region and international glacial archaeology initiatives.
Dr. Michael Sinclair is a Lecturer in Urban Analytics at the University of Glasgow , affiliated with the Division of Urban Studies and Social Policy and the Urban Big Data Centre . He convenes the MSc in Urban Analytics and co-leads the Socio-Environmental Action research cluster . His research focuses on leveraging novel data sources (e.g., mobile apps, social media) to address societal and environmental challenges, particularly human-nature interactions and greenspace valuation. Sinclair leads projects funded by the UK Government’s Department for Culture, Media and Sport and the Economic and Social Research Council (ESRC). His work emphasizes ethical use and representativeness of big data, with applications in urban policy, ecosystem services, and environmental sustainability. Sinclair has secured grants totaling over £1M, including projects on greenspace accessibility and urban data dashboards. He supervises students on GIS and urban analytics topics and contributes to policy-relevant datasets like Tamoco Open Spaces . Notable achievements include developing methodologies for valuing nature-based recreation and advancing spatial interaction models for urban green spaces. His research bridges Human Geography and Environmental Science, with publications in journals like Ecosystem Services and Global Environmental Change .
Bing Zhou serves as an Assistant Teaching Professor in the Department of Geography and the John A. Dutton Institute for Teaching and Learning Excellence within the College of Earth & Mineral Sciences at The Pennsylvania State University, focusing on advancing geospatial education and research. His educational background includes: Ph.D. in Geography from Texas A&M University Dr. Zhou pioneers research in geospatial data science and GeoAI, developing advanced algorithms to extract insights from big geospatial data (crowd-sourced data, mobility data, social media) for climate resilience and disaster response. He founded the revolutionary field of Responsible GIScience, which integrates ethical principles, human values, and societal interests into geospatial research and education. His work specifically targets identifying location-based help information from social media to support disaster response in high-risk communities, aiming to build resilient, equitable, and healthy societies through responsible spatial thinking. He teaches core courses including GEOG 589: Emerging Trends in Remote Sensing and GEOG 480: Exploring Imagery and Elevation Data in GIS Applications. Information regarding scientific awards, student advising, grant funding, and laboratory teams is not documented in the provided materials.
Professor Qihao Weng is Chair Professor of Geomatics and Artificial Intelligence at The Hong Kong Polytechnic University, where he leads the Research Institute for Land and Space. A globally recognized scholar, he bridges geography, landscape ecology, and environmental science through innovative geospatial analytics, GeoAI, and big data methodologies. His work focuses on urban climatology, sustainability science, and human-environment interactions, with over 279 publications and 14 books. PhD, The University of Georgia MA, The University of Arizona MS, South China Normal University Professor Weng's research explores remote sensing applications for urban environmental challenges, including thermal comfort, heat islands, and land-use changes. He pioneered global-scale urban observation via the Group on Earth Observation (GEO) initiative and developed frameworks integrating geospatial technology with climate resilience strategies. Recent publications highlight advancements in GeoAI for urban thermal stress assessment, road extraction algorithms, and multi-temporal data fusion techniques. His work spans interdisciplinary domains, connecting remote sensing, urban science, and sustainability metrics across diverse climate zones. NASA Senior Fellowship (2008) Taylor & Francis Lifetime Achievements Award (2019) AAG Wilbanks Prize (2024) Lifetime Achievement in Remote Sensing Award (2024) Academia Europaea Foreign Member (2021) As Editor-in-Chief of the ISPRS Journal, Professor Weng has advanced global remote sensing discourse. His research has been supported by NSF, NASA, USAID, Microsoft, and Hong Kong Research Grant Council. He has delivered over 130 invited talks and established visiting professorships in Japan, France, and China.
Guofeng Cao is an Associate Professor in the Department of Geography at the University of Colorado . His research integrates GIScience , GeoAI , geostatistics , and remote sensing to develop advanced methodologies for analyzing heterogeneous geospatial data and modeling complex spatiotemporal patterns. Focus areas: Uncertainty-aware geographic knowledge discovery, land cover/land use dynamics, spatiotemporal bias analysis, geospatial cyberinfrastructure development Applications: Natural hazards, environmental science, public health, global change studies Recent publications emphasize generative adversarial networks for climate downscaling, neural processes for uncertainty modeling, and fusion transformers for disaster assessment. His work combines deep learning with Bayesian inference to address scalability challenges in geospatial data processing. Scientific Recognition : NASA and USDA grants for spatiotemporal research Advising : Mentoring graduate students in geospatial data science Laboratory : Leads the STAR lab (Spatiotemporal Pattern Analysis & Research)
Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.
Dr. Steven Manson is a Professor in the Department of Geography, Environment, and Society at the University of Minnesota's College of Liberal Arts, where he also served as Associate Dean for Research and Graduate Programs. He directs the Human-Environment Geographic Information Science (HEGIS) laboratory and leads major data science initiatives like the National Historical Geographic Information System (NHGIS) and IPUMS Terra. PhD in Geography, Clark University (2002) BA Honours in Geography, University of Victoria (1995) His research focuses on geographic information science and human-environment systems , using agent-based modeling and big data to analyze land use change, urban dynamics, and sustainability challenges. Recent work explores spatiotemporal data harmonization and geospatial cyberinfrastructure . The articles reveal trends in GIScience methodology , urbanization analysis , and data-intensive sustainability research . Key contributions include self-organizing map applications for health data and hybrid statistical-GIS techniques for environmental policy. Scientific accolades include: Ecological Society of America Sustainability Science Award NASA Earth System Science Fellow McKnight Land Grant Professorship As Principal Investigator for NHGIS and IPUMS Terra, he secured over $40M in NSF, NIH, and DOJ grants for spatiotemporal data infrastructure. Outreach initiatives include developing open geospatial textbooks adopted globally and collaborating with Twin Cities K-12 programs.
Jennifer Mason is an Associate Professor of Practice and Associate Director of the Geographic Information Science & Technology (GIST) Program at the University of Arizona. She holds a Ph.D. in Geography (GIScience) from Penn State University, an M.S. in GIScience from San Diego State University, and a B.A. in Geography from UCLA with a GIS&T minor. Her research focuses on GIScience, cartography, and geovisual analytics, particularly exploring uncertainty visualization in maps and spatial decision-making. She teaches courses such as Web GIS, Geovisualization, and Raster/Vector Spatial Analysis, emphasizing practical applications of geographic information systems. Jennifer's work bridges theoretical research and pedagogy, with a strong emphasis on improving cartographic design and usability for diverse audiences. Her publications consistently address spatial uncertainty representation, cognitive aspects of geographic visualization, and open-source tools for education. She has contributed to advancing methodologies for visualizing spatial data uncertainty through noise annotation lines and thematic mapping techniques.
