Defining Smart City Digital Twins
08 July 2024
A Q&A with Frederick Law Olmsted Professor John E. Taylor and Senior Research Engineer Neda Mohammadi about how digital twin technology can be used to address community problems.
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Frederick Law Olmsted Professor
Associate Chair for Faculty Development and Research Innovation
Biography
John E. Taylor is the Frederick Law Olmsted Professor. Taylor studies the dynamics where human and engineered networks meet, making him an ideal fit for an endowed professorship named for the father of landscape architecture and a designer who believed engineered infrastructure should be both functional and aesthetically appealing, serving society’s needs while also creating more livable and healthy communities. Taylor has been an entrepreneur and worked as a project manager before starting his career in higher education. He taught most recently at Virginia Tech, where he was a dean’s faculty fellow in the College of Engineering and a Preston and Catharine White fellow in the College of Architecture and Urban Studies.
Research
Building-occupant network dynamics, Extreme event dynamics, Globalizing network dynamics, Information system integration dynamics, Network dynamics simulation
In the News
Protection From Drowning Through AI-Enabled Camera System
20 February 2024
CEE researchers, working with city officials from Columbus, Ga., have created a system that can identify people who might be in trouble on the Chattahoochee River. Using cameras and a computer algorithm, the system can identify people who might be in trouble on the river and give rescue workers precise information that will allow them to get people out of harm’s way or get them out of the water if they’ve fallen in.
Finding a Better Way to Use Cameras to Reduce Crime
14 December 2023
Areas of a middle Georgia city have experienced a 20% reduction in crime after deploying a system of mobile cameras guided by an algorithm developed by Georgia Tech researchers. The team, led by Professor John Taylor, uses artificial intelligence to sift through years of historical crime data to predict where future crimes are likely to happen.