I am a Ph.D. candidate in Urban Science at MIT, working with Professor Andres Sevtsuk at the City Form Lab. My research develops computational methods for urban observation through visual data, drawing on urban design, computer vision, machine learning, and geospatial analysis. By integrating geo-referenced visual data, including street-level imagery, photographs, and videos, with machine learning and spatial analysis, I study sidewalk life, pedestrian behavior, and public life, enabling new approaches to urban design research, urban visual analysis, and planning.
My research has also been shaped by experiences across academia, industry, and professional practice. As an Applied Scientist Intern at Amazon, I worked on multimodal machine learning for reasoning evaluation at AGI and cross-modal video retrieval at AWS. As a Visiting Researcher at UCLA’s Vision and Autonomy Intelligence Lab, I contributed to perception systems for autonomous sidewalk robots. Before returning to MIT, I founded CitoryTech, an urban visual AI studio, where I led more than twenty research and consulting projects for organizations including the World Bank, Daimler, Tencent, and city governments.
My work has appeared in AAAI, Nature Cities, Cities, and ECCV, and has been cited over 1,600 times. I have led funded research as principal investigator, including projects supported by the U.S. ACCESS program and China’s National Key R&D Program, and my research was exhibited at the 2025 Venice Architecture Biennale. I have also served as a teaching assistant and guest lecturer at MIT, NYU, Tongji University, and Hong Kong University, teaching and supporting courses in urban design, GIS, spatial analysis, urban data science, and computer vision for urban studies.
