Takahiro Yabe, PhD. is an Assistant Professor at the Department of Technology Management and Innovation (TMI) and the Center for Urban Science + Progress (CUSP) at the Tandon School of Engineering, New York University. 

Taka's research develops tools and models for analyzing large-scale human behavior data to better understand collective social dynamics during disruptions, and to improve the resilience of communities and cities to urban shocks (e.g., disasters, pandemics, and disruptive technology). Taka's recent works have focused on modeling the resilience of complex urban sociotechnical systems to disasters [Nature Human Behavior, PNAS], human behavior analysis during and after disasters and pandemics [CEUS, Scientific Reports], quantifying the long-term dynamics of diversity in urban encounters in cities [Nature Communications, Nature Cities], and geospatial AI for transferring urban dynamics across cities [ACM KDD , Nature Machine Intelligence]. His research lies in the intersections of computational social science, network science, and civil engineering.

Prior to joining NYU, Taka was a Postdoctoral Associate at the MIT Institute for Data, Systems, and Society (IDSS) and the Media Lab Human Dynamics Group. He received his Ph.D. from Purdue University in the Civil Engineering. He was a Doctoral Fellow at the Purdue Systems Collaboratory. Prior to that, he obtained his Masters and Bachelor Degrees from the University of Tokyo in Civil Engineering (Urban Informatics). He is also passionate in transforming research to support policy making. He was a Data Science Consultant for the World Bank, working on human mobility data analytics for urban disaster risk management and transportation resilience modeling.

Please take a look at the Research page or Google Scholar for more.

Teaching at NYU: 

• Fall 2025: MG-GY 8863 "From Correlation to Causation: Data Science for Decision Making"

• Spring 2025: CUSP-GX 9113D Special Topics in Urban Science & Informatics “Urban Computing and AI"

• Fall 2024: MG-GY 9753 Special Topics in Management "From Correlation to Causation: Data Science for Decision Making"