I am a PhD student in City and Regional Planning at the University of North Carolina at Chapel Hill. Previously I completed an MA in Computational Social Science at the University of Chicago and studied architecture at UC Berkeley.
My research sits at the intersection of urban planning, machine perception of the built environment, and public health — using vision-language models to measure how people experience cities, and building LLM multi-agent frameworks that model planning decision-making, behavioral bias, negotiation, and intent alignment in urban contexts.
Research footprint — field sites mapped from published work. Drag to explore.
01
Urban Perception & Vision-Language Models
How people experience urban environments — via crowdsourced data, street-view imagery, and social media — and how AI systems represent those perceptions.
VLM / CLIPStreet ViewNLP
02
LLM Planning Simulation & Decision-Making
LLM multi-agent frameworks that model planning decision processes — behavioral bias, intent understanding, negotiation, and alignment in policy contexts.
Multi-Agent LLMPlanning AIIntent
03
Urban Health & Environmental Justice
Long-term environmental exposures — air pollution, heat, green-space inequality — and their disparate effects on health across demographic groups.
Env. HealthGISEquity
02 · Publications
Selected papers
Publications by year
JournalConference
2026
Desirable Bikeshare Routes: Nonlinear Impacts of Micro-Level Street Environments
Transportation Research Part A: Policy and Practice
Cai, Y.*, Song, Q., Cheng, Y., Chen, A., Wang, Y., Li, W., & Qiu, W.
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05 · Writing
Blog & tutorials
Notes, ideas, and hands-on tutorials on urban AI, urban perception, and computational methods — sharing work in progress and what I learn along the way. Newest first.
Blog
BlogJul 2025
Urban scene types & heat-island risk in NYC — insights from interpretable ML
A plain-language walkthrough of our Urban Climate paper: which street-level features shape land-surface temperature across New York City. Street greenery beyond roughly 28% canopy, building height, and neighborhood socioeconomic context emerge as the strongest drivers — and heat exposure falls unevenly, with already-vulnerable neighborhoods carrying disproportionate risk.
