Junbo Wang

I am a first-year M.S. student in the Department of Geography and Sustainability at the University of Tennessee, Knoxville, advised by Dr. Bing Zhou. I received my B.E. in Computer Science from China University Of Geosciences, Beijing in 2025, supervised by Dr. Shan Ye.

Currently, I am a member of the GRIND Lab (Geospatial Responsible AI for Nature–Human Dynamics Lab) and the GISense Lab (Geospatial Intelligent Sensing and Mapping Lab). My research interests lie in GeoAI (Geospatial Artificial Intelligence), with an emphasis on building foundation models and investigating their applications in natural hazard/disaster studies and human-centered representations of environmental perception.

I specialize in diffusion models and large language models (LLMs), and I am working to bring geospatial thinking into both model design and real-world applications.

Research

	[CVPR 2026] 🌍SounDiT: Geo-Contextual Soundscape-to-Landscape Generation Junbo Wang, Haofeng Tan, Bowen Liao, Albert Jiang, Teng Fei, Qixing Huang, Bing Zhou, Zhengzhong Tu, Shan Ye, Yuhao Kang, Paper / Code We introduce Geo-contextual Soundscape-to-Landscape (GeoS2L) generation, a task that emphasizes geographic consistency.
	[Appl Psychol Health Well-Being] 🤰 🤖Generative AI for thematic analysis in a maternal health study: coding semistructured interviews using large language models Shan Qiao, Xingyu Fang, Junbo Wang, Ran Zhang, Xiaoming Li, Yuhao Kang Paper We use large language models to code maternal-health interviews for thematic analysis.
	[Expert Systems With Applications] Meta-Tuner: Meta-Trained Node-Specific Transformations for Graph Few-Shot Class-Incremental Learning Zhengnan Li, Jun Fang, Junbo Wang, Xilong Cheng, Yuting Tan, Yunxiao Qin Paper We meta-train node-specific transformations to enable graph few-shot class-incremental learning.