CV
Experience Complex Systems Summer School
Santa Fe Institute
2026 (upcoming)
Research Fellow
MIT Senseable City Lab
2023 - 2026
Summer Intern
Complexity Science Hub
2024
GIS and Mapping Specialist
Data Services, NYU Division of Libraries
2022 - 2023
EducationNew York University
MS in Applied Urban Science and Informatics
2022
Tianjin University
BEng in Urban Planning
2020
ExhibitionMetropolitan Cuneiform
Data Through Design (DxD) 2026, echo{logies}, BRIC, NYC
Street Scores
Interactive Installation & Performance, MIT Open Space
2025
Eyes on the Street
19th International Architecture Exhibition, La Biennale di Venezia 2025
Re-Leaf
19th International Architecture Exhibition, La Biennale di Venezia 2025
Word as Image
Shanghai Library
2023
Talks Visual Empathy in the Age of Data
Data | Art Symposium, Harvard University
2025
Visualizing Seshat: Unveiling Patterns in Human History with Seshat Databank
Complexity Science Hub
2024
The Electric Commute: Envisioning 100% Electrified Mobility in NYC
NYC Open Data Week
2023
Services
NYC Open Data Ambassador Trainee
Jingrong Zhang | 张镜荣
Jingrong Zhang is a researcher and creative practitioner working across urbanism, data, design, and art. At MIT, she uses AI, computer vision, and visualization to study social behavior in public space, urban equity, and the relationship between cities and nature. Her work spans research and installation — from geospatial modeling to exhibitions at the Venice Biennale — exploring how data can function as both evidence and cultural expression. She holds a Master’s degree in Applied Urban Science and Informatics from New York University. Her work has been supported by the Council for the Arts at MIT and recognized by the World Economic Forum, Dezeen, Esri, and NYC Open Data.
< Home >
Email: jingrong.zhang@nyu.edu
[Street Scores]
About Street Scores uses computer vision to analyze changes in pedestrian behavior over a 30-year period in four public spaces located in New York, Boston, and Philadelphia. Building on William Whyte’s observational work from 1980, where he manually recorded pedestrian behaviors, we employ computer vision and deep learning techniques to examine video footage from 1980 and 2010.
Explore at https://senseable.mit.edu/street-scores/
Contribution: creative direction, visualization, film editing, documentation
Dialogue We envision this project as an ongoing dialogue between audience, art, and city.
Street Scores at Kendall/MIT Open Space
Street Scores is an interactive performance and installation by MIT Senseable City Lab, with music by Tod Machover, supported by the Council for the Arts at MIT (CAMIT). Inspired by urbanist William Whyte’s studies of public space, researchers use computer vision to compare archival footage from the 1980s in New York, Boston, and Philadelphia with scenes from the 2010s. The findings suggest a shift—pedestrians today walk faster and linger less, raising questions about the future of social life on city streets.
In this performance, pedestrian behavior is translated into a musical score and choreographic dance, inviting you to hear and see how the rhythms of public life have changed over time. Come experience the evolving tempo of our cities and reflect on what it means for public space to be truly shared.
Team:
MIT Senseable City Lab
Director: Carlo Ratti
Creative direction: Jingrong Zhang, Fábio Duarte
Music: Tod Machover
Choreography: Izzi Waitz
Dance: Kaelyn Dunnell, Shalini Jayarama, Caitlin Peeler, Crystal Tang
Photography: Sabrina Tian
MIT Open Space Programming
Jessie Smith, Rachel Watts, Anna Richardson, Noah Phoenix, Maddy Stratton
MIT Audiovisual Services
Douglas Linford