Dr. Gengchen Mai is currently looking for 1-2 graduate students starting in Fall 2025 as well as 1-2 visiting scholars to join his SEAI Lab. The admitted graduate students will work closely with Gengchen in the Department of Geography and the Environment, University of Texas at Austin on Geospatial Artificial Intelligence (GeoAI) research. If you are interested in GeoAI, spatially explicit machine learning, deep learning, knowledge graphs, and spatial data mining, you are encouraged to apply. To get a taste of Dr. Mai’s research, you can read some of his publications:
10. SIGIR 2024 Zhongliang Zhou, Jielu Zhang, Zihan Guan, Mengxuan Hu, Ni Lao, Lan Mu, Sheng Li*, Gengchen Mai*. Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation, In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2024), July 14 – 18, 2024, Washington D.C., USA. [ArXiv] * Corresponding Author. * Top 1 Information Retrieval Conference
9. TSAS 2024 Gengchen Mai, Weiming Huang, Jin Sun, Suhang Song, Deepak Mishra, Ninghao Liu, Song Gao, Tianming Liu, Gao Cong, Yingjie Hu, Chris Cundy, Ziyuan Li, Rui Zhu, Ni Lao. On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence. ACM Transactions on Spatial Algorithms and Systems, 2024. [DOI] [ArXiv]
8. IJGIS 2023 Yingjie Hu*, Gengchen Mai*, Chris Cundy, Kristy Choi, Ni Lao, Wei Liu, Gaurish Lakhanpal, Ryan Zhou, Kenneth Joseph. Fusing geo-knowledge and GPT-3 for extracting location descriptions from disaster-related social media messages.. International Journal of Geographical Information Science. [DOI] * Co-first author
7. ISPRS PHOTO 2023 Gengchen Mai, Yao Xuan, Wenyun Zuo, Yutong He, Jiaming Song, Stefano Ermon, Krzysztof Janowicz, Ni Lao. Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions.. ISPRS Journal of Photogrammetry and Remote Sensing, 202 (2023): 439-462. [DOI] [Website] [Code] [ArXiv] [ResearchGate]
6. ICML 2023 Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon. CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations, In: Proceedings of the Fortieth International Conference on Machine Learning (ICML 2023), Jul 23 – 29, 2023, Honolulu, Hawaii, USA. [Website] [Code] [ArXiv] [Presentation] * Top 1 Machine Learning Conference
5. GEOI 2023 Gengchen Mai, Chiyu Max Jiang, Weiwei Sun, Rui Zhu, Yao Xuan, Ling Cai, Krzysztof Janowicz, Stefano Ermon, Ni Lao. Towards General-Purpose Representation Learning of Polygonal Geometries. GeoInformatica, 27(2), (2023): 289-340. DOI:10.1007/s10707-022-00481-2 [arxiv paper] * AAG 2023 J. Warren Nystrom Award (1 award recipient every year)
4. ICLR 2020 Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, Ni Lao. Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells, In: Proceedings of ICLR 2020, Apr. 26 – 30, 2020, Addis Ababa, ETHIOPIA . [OpenReview paper] [arxiv paper] [code] [slides] * Spotlight Paper (Acceptance Rate 6%, 156 out of 2594 submissions)
3. TGIS 2020 Gengchen Mai, Krzysztof Janowicz, Ling Cai, Rui Zhu, Blake Regalia, Bo Yan, Meilin Shi, Ni Lao. SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting. Transactions in GIS, 24.3 (2020): 623-655. DOI:10.1111/tgis.12629 [arxiv paper] [code] [slides] * Top Cited Papers in TGIS 2020-2021
2. TGIS 2020 Ling Cai, Krzysztof Janowicz, Gengchen Mai, Bo Yan, Rui Zhu. Traffic Transformer: Capturing the Continuity and Periodicity of Time Series for Traffic Forecasting. Transactions in GIS, 24.3 (2020): 736-755. DOI:10.1111/tgis.12644 * Top Cited Papers in TGIS 2020-2021
1. TGIS 2020 Gengchen Mai, Krzysztof Janowicz, Yingjie Hu, Song Gao. ADCN: An Anisotropic Density-Based Clustering Algorithm for Discovering Spatial Point Patterns with Noise. Transactions in GIS, 22(2018), 348-369. DOI:10.1111/tgis.12313 [code] * Top 10% Most Downloaded Papers in TGIS (01/2018-12/2019)
The University of Texas at Austin (UT) is one of the “Public Ivies” universities and is currently ranked #32 by US News and #66 by QS. Dr. Mai is an expert in Geospatial Artificial Intelligence, Geographic Knowledge Graphs, and Geo-Foundation Models. Successful applicants will have the opportunities to work with different industry sectors and potentially intern at high-tech companies such as Google, and Google DeepMind. For more information about Dr. Mai, please go to https://gengchenmai.github.io/.
By working with Dr. Mai, you will not only develop strong technical skills but also acquire important knowledge for scientific research. Successful applicants will be financially supported as a teaching assistant or a research assistant, contingent on satisfactory performance. Additionally, U.S. citizen PhD students with a master’s degree are eligible for two years of direct funding through the NSF NRT Fellowship on Ethical AI, along with the following TA or RA support. To become a successful applicant (and receive financial support), you are expected to have the following background and skills:
- A bachelor/master’s degree in GIScience, computer science, information science, or related field before the official enrollment (Aug 2025).
- Great passion for GIScience and GeoAI research.
- Familiar with one programming language, e.g., Java or Python.
- Familiar with one spatial analysis and mapping tool, e.g., ArcGIS or QGIS.
- Research experience in machine learning and AI would be a plus, but is not required.
- Fluent in spoken English.
If you are interested in this opportunity, please send your CV and a brief statement (no more than 300 words) to Dr. Mai at gengchen.mai AT austin.utexas.edu. If you have any questions, please feel free to contact Dr. Mai.
