Trustworthy Spatial Intelligence: Learning, Calibrating, and Reasoning Toward World Models of Cities
Cities are complex systems shaped by rich patterns across space and time. Understanding and predicting these patterns requires more than accurate models—it requires trustworthy spatial intelligence. In this talk, Dingyi Zhuang will present his research agenda along three complementary directions. First, he develops spatiotemporal and multimodal modeling frameworks with graph neural networks and tensor learning to capture and predict correlations across space and time. Second, he designs uncertainty quantification and calibration techniques to ensure that the relationships learned by these models are reliable, interpretable, and fair. Third, he extends toward spatial reasoning, leveraging large language models and vision-language models to embed physical constraints and social norms into learned structures, advancing toward richer world models of cities. Together, these directions outline a coherent vision: from learning relationships in spatiotemporal data, to validating their trustworthiness, to reasoning about the rules that govern urban environments.
Speaker: Mr. Dingyi ZHUANG
Date: 15 Dec 2025 (Monday)
Time: 2:15pm – 3:15pm
Venue: LAU 6-209
Poster: Click here
Biography
Mr. Dingyi ZHUANG is a final-year Ph.D. candidate in Transportation at MIT's JTL Transit Lab, supervised by Prof. Jinhua Zhao. He received his M.Eng. from McGill University and B.Sc. from Shanghai Jiao Tong University. His research develops trustworthy Al and machine learning methods for transportation and urban systems, spanning spatiotemporal data modeling, uncertainty quantification, intelligent transportation systems, and generative Al for spatial reasoning and planning. Dingyi has also worked as a machine learning intern at the Bosch Center for Al, Morgan Stanley's ML research group, and the Chicago Transit Agency. He received the UPS Fellowship at MIT, and won four workshop Best Paper Awards, with publications bridging both leading transportation journals (TR-C, AAP, IEEE T-ITS) and top computer science venues (IEEE TPAMI, ICLR (Spotlight), NeurlPS, AAAI, KDD, and EMNLP). Looking ahead, he envisions spatial intelligence as a foundation for trustworthy world models that integrate data with physical and social reasoning for urban decision-making.