About Me

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About Me

I am an interdisciplinary researcher specialized in Geospatial Artificial Intelligence (GeoAI) and Urban Computing, dedicated to solving complex urban governance and resource allocation challenges through cutting-edge technologies including Multi-Agent Reinforcement Learning (MARL), deep learning, and Large Language Models (LLMs).

Regarding research interests, I primarily focus on the co-optimization of spatial equity and efficiency. My research centers on utilizing Agent-Based Modeling (ABM) and spatiotemporal big data analysis to investigate resource accessibility, social equity in public service distribution (such as the “10-minute living circle” concept and inequality issues), and socioeconomic biases in urban sensing. Recently, I have developed particular interest in applying generative AI for urban analysis and mining public perceptions and emotional responses toward emerging transportation technologies like autonomous vehicles through social media data.

In terms of technical capabilities, I possess a solid background in full-stack development and data science, enabling me to independently complete the entire research cycle from data acquisition and algorithm modeling to system deployment:

  • Spatial Data Analysis and Modeling: Proficient in Python (GeoPandas, PyTorch) and R, with expertise in complex network analysis and multi-objective optimization.

  • Engineering Development and Deployment: Extensive experience in cloud computing and DevOps (AWS, Docker, Linux), skilled in WebGIS full-stack development (Vue.js, Leaflet, Flask, PostGIS), capable of transforming theoretical models into visualized decision support platforms.

  • Big Data Processing: Rich experience in data cleaning and web crawling, adept at handling multi-source heterogeneous data ranging from social media text to infrared camera imagery.

I am committed to integrating the precision of computational science with the humanistic concerns of social science, driving smarter, more inclusive, and sustainable urban development through data-driven approaches.