My research centers on advancing and applying artificial intelligence techniques—particularly computer vision, deep learning, and large multimodal models (LMMs)—to analyze the urban built environment and its social implications. I am especially interested in developing AI models that can detect and classify urban changes from high-resolution imagery, such as building footprint transformations and street-level activity patterns. My work explores the use of Siamese networks, YOLO-based object detection, and LMMs to extract spatial and semantic information from satellite and street view data. I am also interested in explainable AI and spatially aware machine learning methods to better understand the relationship between urban form, policy interventions, and demographic diversity. Ultimately, my goal is to enhance the scalability, interpretability, and policy relevance of AI applications in urban planning.

I’m interested in all kinds of AI research, Please reach out to collaborate 😃