CV
Experience Research Fellow
MIT Senseable City Lab
2023 - present
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.
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Email: jingrong.zhang@nyu.edu
[In Search of Authenticity]
About In Search of Authenticity
Locals, Tourists, and the Visual Politics of Chinatown
Chinatown is often invoked as an “authentic” ethnic enclave, yet what counts as authenticity is unevenly perceived, represented, and circulated. Drawing on geotagged Flickr photographs as a vernacular visual archive, this project examines how locals and tourists construct distinct visual geographies of San Francisco’s Chinatown. Using a computational framework that combines temporal posting patterns, spatial analysis, and image classification, I distinguish between local and tourist users and analyze how different forms of attention cluster around streets, landmarks, businesses, and everyday social life. While machine learning methods reveal patterned differences in where and when images are taken, the project treats photographs not as neutral records but as cultural artifacts shaped by memory, mobility, and expectation. Tourists tend to reproduce a narrow corridor of highly visible landmarks and symbolic scenes, while locals document a broader, more fragmented landscape of social activity, routine, and informal space. Read through theories of place-making, the tourist gaze, and visual anthropology, these patterns suggest that “authenticity” emerges not as a fixed property of place but as a relational and contested construct, produced through representational practices and platform infrastructures. In this sense, social media images function as a distributed ethnographic record—one that reveals how visibility, cultural meaning, and urban identity are continually negotiated in a neighborhood shaped by tourism, migration, and structural inequality.