Find Academic Supervisors

Jianguo WangView profile

Professor

• Multisensor Integration
• GNSS Technology
• Precision Engineering Surveying
• +5 more

Dr. Jianguo Wang is a Professor in the Department of Earth and Space Science Engineering at York University's Lassonde School of Engineering. He has been a faculty member since 2006 and is a founding member of the Lassonde School. With over 35 years of academic and industrial experience, he specializes in multisensor integration, GNSS technology, and precision engineering surveying. He holds a Dr.-Ing. in Geomatics Engineering from Universität der Bundeswehr München, Germany, alongside Bachelor’s and Master’s degrees from Wuhan Technical University of Surveying and Mapping (WTUSM). His research focuses on advanced data processing methodologies, including Kalman filtering, error analysis, and LiDAR systems. He has authored/co-authored over 60 publications, including textbooks like Error Theory and Foundation of Surveying Adjustment and Foundation of Geodesy . He is a Fellow of Engineers Canada and licensed as a Professional Engineer in Ontario. Education: Dr.-Ing., Geomatics Engineering, Universität der Bundeswehr München (Germany) M.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping B.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping Dr. Wang teaches courses such as Advanced Optimization and Applications , GNSS , and Global Geophysics and Geodesy . He leads the Earth Observation Laboratory (PSE 432), focusing on multisensor integration for navigation and positioning. His work explores innovative solutions for sensor calibration, data fusion, and geospatial applications. Grants & Labs: Active in lab-based research with collaborators like Baoxin Hu, his laboratory integrates GNSS, IMUs, LiDAR, and cameras for precision navigation. His recent work addresses challenges in sensor error calibration, LiDAR point cloud accuracy, and Kalman filter enhancements.

Jiwon KimView profile

Associate Professor

• Transport Engineering
• Artificial Intelligence
• Machine Learning
• +6 more

Associate Professor Jiwon Kim is a leading researcher in Transport Engineering at the University of Queensland's School of Civil Engineering. She serves as Director of Higher Degree by Research and was a DECRA Fellow from 2019-2022. Holding degrees from Korea University and Northwestern University, she specializes in AI/ML applications for transportation systems. PhD, Northwestern University BS & MS, Korea University Her research focuses on Artificial Intelligence and Machine Learning applications in transportation, including: Deep learning for traffic management Reinforcement learning in mixed traffic environments Multi-agent systems for urban mobility optimization Spatiotemporal trajectory analysis Recent publications demonstrate expertise in: Eco-driving strategies Traffic incident prediction Queue length estimation Crash risk modeling Scientific recognition includes: ARC DECRA Fellowship (2019-2022) She supervises doctoral students in: Transportation data analytics Autonomous vehicle systems Intelligent traffic management Current projects explore real-time traffic monitoring, synthetic mobility data generation, and connected vehicle technologies.

Sai RavelaView profile

Researcher

• Nonlinear Stochastic Dynamics
• Coherent Fluids
• Uncertainty Quantification
• +6 more

Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.

Nicolas Federico MartinView profile

Associate Professor

• Precision Agriculture
• Crop Science
• Machine Learning Applications in Agriculture
• +7 more

Nicolas Federico Martin is an Associate Professor in the Department of Crop Sciences at the University of Illinois at Urbana-Champaign, with additional appointments as Associate Professor in the Center for Latin American and Caribbean Studies, Center for Digital Agriculture, and the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges traditional agricultural science with cutting-edge computational approaches. Dr. Martin's research focuses on the intersection of agriculture and data science, with particular emphasis on: Precision agriculture and on-farm experimentation methodologies Machine learning applications for crop management and yield prediction Nitrogen and nutrient management optimization Soybean and corn breeding and production systems Remote sensing and UAV applications in agriculture Sustainable agricultural practices including cover crop management His publication record demonstrates a clear trajectory toward increasingly sophisticated integration of artificial intelligence with agricultural science. Recent work shows heavy emphasis on using machine learning algorithms (particularly reinforcement learning, convolutional neural networks, and generalized additive models) to solve practical farming challenges related to crop management decisions, yield prediction, and resource optimization. This research has significant implications for both scientific understanding of crop-environment interactions and practical farm management. Dr. Martin actively collaborates across disciplines and institutions, as evidenced by his extensive co-authorship network spanning agronomy, computer science, environmental science, and economics. His work has garnered attention from numerous news outlets and social media platforms, indicating its relevance to current agricultural challenges. He is a key contributor to the Data-Intensive Farm Management project, which aims to transform agronomic research through on-farm precision experimentation. His affiliation with NCSA provides access to high-performance computing resources essential for processing large agricultural datasets. Additionally, his work in Latin American agriculture (particularly in Mexico and Argentina) reflects his commitment to addressing global food security challenges.

Esther RolfView profile

Assistant Professor

• Machine Learning
• Geospatial Machine Learning
• Environmental Monitoring
• +4 more

Esther Rolf is an Assistant Professor in the Department of Computer Science at the University of Colorado Boulder. Her research focuses on blending methodological and applied machine learning techniques to address social and environmental challenges, emphasizing usability, data-efficiency, and fairness. Her work includes developing algorithms for environmental monitoring with satellite imagery and advancing geospatial ML systems. Her research explores the multifaceted role of data representation in machine learning, particularly how spatial distribution and data acquisition impact model fairness and efficacy. Projects include formalizing representivity in training data and tackling evaluation challenges in geospatial ML applications. Recent publications highlight her expertise in satellite-based poverty mapping, multimodal data efficiency, and geospatial foundation models. She integrates specialized architectures and domain-specific benchmarks into her work. Best Paper Award, ICML (2018) Best Paper Award, NeurIPS Workshop on AI for Social Good (2019) NSF Graduate Research Fellowship Google Research Fellowship Esther's lab at CU Boulder recruits PhD students and postdocs interested in statistical/geospatial ML and context-driven research for real-world problems. She teaches graduate courses in machine learning and geospatial ML at CU Boulder.

Michael SmithView profile

Professor

• Ethics
• Moral Psychology
• Philosophy of Mind
• +3 more

Michael Smith is the McCosh Professor of Philosophy at Princeton University. He holds a DPhil from Oxford University (1989) and has been a faculty member since 2004, previously at the Australian National University. His research focuses on ethics, moral psychology, philosophy of mind, political philosophy, and philosophy of law. Smith’s work integrates constitutivist theories of practical reason with analyses of moral agency. He has contributed to debates on moral rationalism, the nature of reasons for action, and the relationship between rationality and normativity. Education: MA, Monash University (1980); BPhil (1983), DPhil (1989), University of Oxford Smith’s scholarship emphasizes the interplay between ethical theory and psychological explanations of agency. Recent publications explore topics like carbon capture technologies, cultural clashes in moral reasoning, and probabilistic forecasting in oceanography. His philosophical contributions address foundational questions in meta-ethics, including the ‘moral problem’ and the implications of constitutivism for normative frameworks. He advises on interdisciplinary projects at the intersection of philosophy and emerging technologies. Notable research trends include applying philosophical analysis to environmental ethics and developing frameworks for resolving moral dilemmas through rational agency models. His work often bridges analytic philosophy with empirical inquiries in psychology and social science.

• Visual Analytics
• Information Visualization
• Geographic Information Science
• +7 more

Jo Wood is Professor of Visual Analytics in the Department of Computer Science at City, University of London, where she has been employed since January 14, 2000. Her work bridges computer science, geographic information science, and human-computer interaction, focusing on innovative methods for visualizing complex spatial and behavioral data. Her research interests center on visual analytics , information visualization , and geovisualization , with applications in transportation, public health, crisis response, and citizen science. She investigates how interactive visual interfaces can support exploratory data analysis, decision-making, and storytelling, particularly through small multiples, faceted views, and sketch-based rendering techniques. The trends in her recent publications reflect a consistent focus on user-centered design , spatial data abstraction , and interactive exploration of multivariate datasets. Her work often integrates real-world behavioral data such as GPS tracks, cycling patterns, and crowd-sourced information to build meaningful visual narratives and support analytical reasoning. Throughout her career, Jo Wood has contributed significantly to the advancement of visual analytics through high-impact publications in top-tier venues such as IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum. Her collaborations with researchers like Jason Dykes and Aidan Slingsby highlight her role in a vibrant research community. She has supervised numerous research projects and mentored students in visualization and geospatial analytics, though specific names are not listed in the provided text. Her work has been supported by various research grants, particularly in domains involving urban mobility, energy modeling, and crisis informatics, though grant details are not specified here. Jo Wood has also contributed to the design of visual analytics systems for applications including disease spread modeling, bicycle-hire scheme monitoring, and persuasive technology for health and leisure, demonstrating a strong commitment to impactful, interdisciplinary research.

• Artificial Intelligence
• Marketing Analytics
• Digital Marketing
• +4 more

Natasha Zhang Foutz is a Research Associate Professor of Commerce at the McIntire School of Commerce, University of Virginia . Her work bridges Artificial Intelligence , Marketing Analytics , and Consumer Behavior , with a focus on Digital Content and Location-Based Services . She teaches Marketing Analytics , Entertainment Marketing , and Marketing Models across undergraduate to PhD programs. Ph.D. in Marketing, Cornell University M.S. in Statistics & Marketing, Cornell University B.S. in Economics, Fudan University Her research investigates: AI-Powered Entertainment Marketing : Using machine learning to analyze consumer behavior in digital content. Privacy and Ethical Data Use : Studying consumer trade-offs between privacy and public good, especially during crises like the COVID-19 pandemic . Location Analytics : Leveraging mobile data for urban economics, real estate, and emergency response modeling (e.g., MobiRescue for disaster logistics). Prosocial Consumer Behavior : Exploring how social capital and diversity drive innovation and policy compliance. Recent publications highlight trends in Reinforcement Learning for crisis management, Privacy-Preserving AI , and Freemium Pricing Models in digital markets. Her awards include the 2025 UVA Outstanding Researcher Award and the 2018 Mallen Award for motion picture studies. Natasha serves as an Area Editor for multiple journals, emphasizing Data Science in marketing. She has advised numerous PhD students and collaborated on projects analyzing Big Data in consumer mobility and platform economics.

• Transportation Engineering
• Road Safety
• Remote Sensing
• +5 more

Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.

Jan Dirk WegnerView profile

Associate Professor

• Machine Learning
• Computer Vision
• Remote Sensing
• +7 more

Jan Dirk Wegner is an Associate Professor at the University of Zurich, holding the chair in 'Data Science for Sciences' and leading the EcoVision Lab. He previously served as a Postdoc (2012–2016) and senior scientist (2017–2020) at ETH Zurich's Photogrammetry and Remote Sensing Group, following his PhD (with distinction) from Leibniz University Hannover (2011). His research bridges machine learning, computer vision, and remote sensing to address environmental and geoscience challenges, focusing on large-scale environmental data analysis, vegetation monitoring, and climate change mitigation. Education: PhD (with distinction) in Geodesy, Leibniz University Hannover (2011) Postdoc, ETH Zurich (2012–2016) Senior Scientist, ETH Zurich (2017–2020) Research Interests: Machine Learning, Computer Vision, Remote Sensing, Environmental Science, Climate Science, Geosciences, Explainable AI, Uncertainty Quantification, and Applications in Sustainability. The EcoVision Lab develops data-driven methods for global-scale environmental monitoring, including vegetation parameter mapping, flood prediction, forest degradation detection, and AI-driven ecological modeling. Awards: ETH Postdoctoral Fellowship (2012–2016) Science Prize of the German Geodetic Commission WEF Young Scientist Class 2020 (Top 25 globally under 40) Advising & Leadership: Director of the University of Zurich's Doctoral School in Data Science, leading the EcoVision Lab, and coordinating the CVPR EarthVision Workshops. His roles include Vice President of ISPRS Technical Commission II, member of the ETH AI Center, ELLIS, and UN-ETH Partnership. Labs/Teams: EcoVision Lab focuses on interdisciplinary AI applications for environmental challenges, collaborating with NGOs, governments, and industry to translate research into societal impact.

• Spatial Demography
• Urbanization Studies
• Climate Change Impacts
• +3 more

Deborah Balk is a Professor at the Marxe School of Public and International Affairs at Baruch College, part of the City University of New York (CUNY). She also serves as Director of the CUNY Institute for Demographic Research and holds appointments in the CUNY Graduate Center's Sociology and Economics programs, as well as the CUNY School of Public Health's Epidemiology Program. Her expertise lies in spatial demography, integrating earth and social science data to address policy challenges related to urbanization, climate change, and population dynamics. Dr. Balk has led significant roles in climate assessments, including Co-Chair of the New York City Panel on Climate Change’s 4th Assessment (2019–2024) and membership in the U.S. National Climate Assessment’s 6th Health Chapter (2025). She holds a PhD in Demography from UC Berkeley and degrees from the University of Michigan (MPP and AB in International Relations). Her research focuses on urbanization, migration, poverty, health, and environmental interactions, particularly climate adaptation and equity. Notable projects include analyzing population vulnerability in coastal zones and developing spatial demographic tools for global health and policy. Awards include the Andrew Carnegie Fellowship (2016–2018) and the William and Flora Hewlett Foundation Fellowship (1991). Dr. Balk has secured grants exceeding $6 million from NSF, NASA, and others, supporting work on urbanization, climate justice, and demographic data integration. She advises multiple institutions, including the U.S. Census Bureau and National Academy of Sciences. Her teaching spans spatial demography, urban policy, and statistical methods, reflecting her commitment to bridging demographic science and real-world applications.

Mei-Po KwanView profile

Professor

• Geographic Information Science (GIS)
• Environmental Health
• Urban Mobility
• +7 more

Mei-Po Kwan is a distinguished academic holding multiple roles at The Chinese University of Hong Kong (CUHK). She serves as the Head of Chung Chi College, Choh-Ming Li Professor of Geography and Resource Management, Director of the Institute of Space and Earth Information Science, and Professor at The Jockey Club School of Public Health and Primary Care. Her research focuses on geospatial health, urban mobility, environmental health, and innovative GIS methods, emphasizing individual-level environmental exposure and social disparities. Education: Ph.D. Geography, University of California, Santa Barbara (1994) M.A. Urban Planning, University of California, Los Angeles (1989) B.SoSci. Geography, The Chinese University of Hong Kong (1985) Research Interests: Dr. Kwan’s work bridges health, transport, and environmental geography, leveraging cutting-edge GIS and mixed-methods approaches. Key themes include: Uncertain Geographic Context Problem (UGCoP) and Neighborhood Effect Averaging Problem (NEAP) GIS-based analysis of greenspace, air/noise pollution, and mobility Geospatial AI and real-time sensing technologies Health disparities in urban environments Grants & Awards: She has secured over $68 million in research funding, including recent grants from the Hong Kong Research Grants Council and Innovation and Technology Commission. Notable awards include Fellowships from the American Association of Geographers, Royal Geographical Society, and multiple Highly Cited Researcher recognitions (2019, 2021). Professional Service: Editor of Annals of the American Association of American Geographers , founder of the International Geospatial Health Research Network, and member of prestigious editorial boards and advisory councils. Labs & Teams: Leads the Institute of Space and Earth Information Science and collaborates on projects like the 15-minute city framework, carbon monitoring systems, and geospatial health analytics.

Stefano GalelliView profile

Associate Professor

• Environmental and Water Resources Systems Engineering
• Water Systems
• Energy and the Environment
• +6 more

Stefano Galelli is a tenured Associate Professor in the School of Civil and Environmental Engineering at Cornell University, where he leads the Critical Infrastructure Systems Lab. He also holds an adjunct position as a Research Scientist at the Lamont-Doherty Earth Observatory, Columbia University. His career spans roles in Singapore, including a Postdoctoral Research Fellow at NUS (2011–2013) and faculty at the Singapore University of Technology and Design (2013–2023). Dr. Galelli earned his B.Sc. (2004), M.Sc. (2007), and Ph.D. (2011) in Environmental and Land Planning Engineering and Information Technology from Politecnico di Milano, Italy. His research focuses on the interactions between critical infrastructure systems and natural environments, emphasizing adaptive management solutions for water-energy systems. Techniques include process-based modeling, climatology, statistical learning, control theory, and optimization. He explores topics like hydro-climatic variability impacts, dam re-operation for environmental flows, and cyber-physical security in infrastructure. His contributions to journals such as Nature Sustainability, Earth’s Future, and Environmental Modelling & Software have earned him multiple awards, including the Early Career Research Excellence Award (2014) and SUTD Excellence in Research Award (2017). He has served as an editor for several journals and is recognized for advancing interdisciplinary approaches to water-energy nexus challenges. Teaching highlights include foundational mathematics courses and advanced topics in data analytics, optimization, and water-energy management. He is developing new courses on data-driven control of coupled human-natural systems and risk management for interconnected systems.

Luc AnselinView profile

Professor

• Spatial data science
• spatial econometrics
• computational social science
• +6 more

Luc Anselin is the Stein-Freiler Distinguished Service Professor of Sociology and the College at the University of Chicago. He serves as Director of the Center for Spatial Data Science and Senior Fellow at NORC, with affiliations in the Department of Sociology and the Committee on Quantitative Research Methods in Social, Behavioral, and Health Sciences. B.S., Vrije Universiteit Brussel, 1975 M.S., Vrije Universiteit Brussel, 1976 M.A., Cornell University, 1979 Ph.D., Cornell University, 1980 Anselin is a pioneer in spatial data science, spatial econometrics, and computational social science. His work bridges quantitative geography, regional science, and computer science, with applications in urban studies, economic analysis, and health research. His recent publications focus on spatial regimes, software ecosystems like GeoDa and PySAL, stigma analysis in public health, and computational methods for urban equity. These reflect interdisciplinary trends in spatial statistical modeling, open-source development, and geospatial policy evaluation. Scientific recognition includes: Fellow, Regional Science Association International (2004) Walter Isard Prize (2005) William Alonso Memorial Prize (2006) National Academy of Sciences (2008) American Academy of Arts and Sciences (2011) Anselin has led major grants and directed institutions like the GeoDa Center for Geospatial Analysis and Computation at Arizona State University. He has mentored interdisciplinary collaborations across sociology, economics, and data science. His laboratories and teams include the Center for Spatial Data Science at the University of Chicago and the GeoDa Center at Arizona State University, fostering innovation in geospatial computation and open-source spatial analytics.

Cathy WuView profile

Professor

• Computational Biology
• Bioinformatics
• Data Science
• +8 more

Cathy Wu is a distinguished academic holding the Unidel Edward G. Jefferson Chair in Engineering and Computer Science at the University of Delaware. She serves as Director of the Center for Bioinformatics & Computational Biology (CBCB), Data Science Institute (DSI), and Protein Information Resource (PIR). Her roles include professorships in the Departments of Computer & Information Sciences and Biological Sciences. Education: BS in Plant Pathology (National Taiwan University, 1978), MS and PhD in Plant Pathology (Purdue University, 1982–1984), and a second MS in Computer Science (University of Texas at Tyler, 1989). She completed postdoctoral training in Molecular Biology at Michigan State University (1985–1986). Research interests focus on computational biology, bioinformatics, and data science with emphasis on protein informatics, biological text mining, ontology development, gene-disease-drug networks, and machine learning applications. She leads initiatives in integrating FAIR principles into biological databases like UniProt and InterPro. Her work bridges computational methods with biomedical challenges, including cancer genomics, epigenetic regulation, and proteomic analyses. She has spearheaded educational programs such as the Online Graduate Certificates in Applied Bioinformatics and Biomedical Informatics and Data Science. Her contributions include over 290 peer-reviewed publications (48,000+ citations, h-index 71) and authored/co-authored four books on bioinformatics. She directs multidisciplinary research teams and collaborates internationally on projects like the HALO study on ovarian cancer genetics. Awards and recognition are implied through her leadership roles and academic appointments, though specific prizes are not listed here. Her grants and funding support large-scale initiatives in bioinformatics infrastructure and translational research.