Gengchen Mai
Google (United States), The University of Texas at Austin, Stanford University, University of California, Santa Barbara, University of Georgia, Wuhan University
Research areas: Computer Science · Artificial Intelligence · Geographic Information Systems Studies · Topic Modeling · Semantic Web and Ontologies
0000-0002-7818-7309
About
Dr. Gengchen Mai is an Tenure-Track Assistant Professor (since June 1st, 2024) at the Department of Geography and the Environment at UT Austin. His research areas are Spatially Explicit Artificial Intelligence, Spatial Representation Learning, Geo-Foundation Models, Geographic Knowledge Graphs, Geographic Question Answering, and so on. He is the receipt of many prestigious awards including AAG 2021 Dissertation Research Grants, AAG 2022 William L. Garrison Award for Best Dissertation in Computational Geography, AAG 2023 J. Warren Nystrom Dissertation Award, Top 10 WGDC 2022 Global Young Scientist Award, the The Jack and Laura Dangermond Graduate Fellowship, and so on.
Employment
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Google (United States) Visiting Faculty Researcher2025 - Present
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The University of Texas at Austin Tenure-Track Assistant Professor2024 - Present
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University of Georgia Assistant Professor2022 - 2024
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Stanford University Postdoc Fellow2021 - 2022
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Alphabet Inc AI Resident2020 - 2020
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Apple Inc Cartographic Engineer Intern2019 - 2019
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SayMosaic Inc. Machine Learning & NLP Research Intern2018 - 2018
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Esri Inc Software Development Intern2017 - 2017
Education
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Stanford University Postdoc2021 - 2022
- 2018 - 2021
- 2015 - 2018
- 2011 - 2015
Projects & Funding
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A Statistics-Based Geographic Bias Quantification and Debiasing Framework for GeoAI and Foundation Models Directorate for Social, Behavioral & Economic Sciences2025 - 2028
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Developing a Smart Electric Vehicle Knowledge Management System with A Cross-Domain Interlinked Electric Vehicle Knowledge Graph US Department of Transportation -- Center for Climate-Smart Transportation(CCST), Johns Hopkins University2025 - 2025
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Map2Loc: Predicting the Georeference of Map Images using Multimodality Foundation Models and Retrieval-Augmented Generation UTL Map & Geospatial Collections Explorer Fellowship2024 - 2025
Publications (115)
- From Large Language Models to Agentic Systems: A Systematic Review of Generative AI in GIScience Save
- Generative Artificial Intelligence for Planning Scenario Visualization: A Framework and Benchmark for Controllable Street‐Level Image Synthesis Save
- SpatialCausal : a spatially-aware causal inference deep learning model for out-of-hospital cardiac arrest survival prediction Save
- SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome Save
- GAIR: Location-aware self-supervised contrastive pre-training with geo-aligned implicit representations Save
- Street semantic tree: a knowledge-driven GeoAI framework for urban e-scooter ridership classification Save
- Mathematical Foundations and Challenges for GeoAI Save
- On the Ethics of Generative GeoAI: Explainability, Bias, Hallucination, Accountability, Privacy, and Trust Save
- AVKG: an interlinked cross-domain knowledge graph for smart autonomous vehicle decision making Save
- Annotation-free cloud masking for PlanetScope images in the Arctic via cross-platform ability transfer using deep learning and foundation models Save
- Bridging data gaps for EV demand estimation: A transfer learning approach Save
- LocDiff: Identifying Locations on Earth by Diffusing in the Hilbert Space Save
- Scenario-Based Evaluation of Probabilistic Time Series Forecasting for Solar Energy Save
- Whose Truth? Pluralistic Geo-Alignment for (Agentic) AI Save
- Machine-learning-enabled spatial pattern mining: evaluating the impact of imperfect inputs Save
- Representation learning for geospatial data Save
- GeoFM: how will geo-foundation models reshape spatial data science and GeoAI? Save
- Artificial intelligence in environmental remote sensing: Progress, way forward and key considerations Save
- ZooplanktonBench: A Geo-Aware Zooplankton Recognition and Classification Dataset from Marine Observations Save
- Geography for AI sustainability and sustainability for GeoAI Save