CV  

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Experience  Research Fellow
MIT Senseable City Lab 
2023 - present

GIS and Mapping Specialist
Data Services, NYU Division of Libraries 
2022 - 2023

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EducationNew York University
MS in Applied Urban Science and Informatics
2022

Tianjin University
BEng in Urban Planning 
2020  

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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  

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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

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Services  
NYC Open Data Ambassador Trainee

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Jingrong Zhang | 张镜荣

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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 >

• + Street Scores 
  
• + Fleeting Emissions
  
• + Dust Bloom 
  
• + Tree-D Fusion  
  
• + Beijing Inside Out 
  
• + Senseable Global Labs 
• 
  

• + Metropolitan Cuneiform

• + The Electric Commute 
  
• + Word as Image 
  
• + In Search of Authenticity 
  

Email: jingrong.zhang@nyu.edu 

 [Fleeting Emissions]

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About Urban traffic emissions change constantly, shaped by vehicles’ speed, acceleration, interruption—what fixed sensors cannot capture. In Fleeting Emissions we integrate visual data from widely present traffic cameras and dashboard cameras with AI-driven vehicle recognition and network-scale modeling, to capture fine-grained variations in fleet composition, vehicular rhythms, and acceleration behavior. We tested the Fleeting Emissions model using dashcams in Amsterdam and Abu Dhabi, and grew it up to city scale in New York using extensive traffic cameras and data fusion to uncover hidden emission hotspots and temporal spikes that conventional models overlook. As demonstrated in Manhattan, such high-resolution insight enables real-time evaluation of policies—from congestion pricing to demand shifts—transforming emissions from an abstract metric into a visible, actionable layer of the city.

Explore at https://senseable.mit.edu/fleeting-emissions/

Contribution: visualization, storytelling 



Visuals 
Microscopic data is precise but unscalable.
Macroscopic data is scalable but blind.
Mesoscopic vision reveals structure without surveillance.



Cameras are everywhere. 
Powered by AI, we can identify each vehicle model and their precise motion.