RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Massive-Scale Models of Urban Infrastructure and Populations – Ben Intoy Full Stack Developer and Dan Baeder Data Scientist, Deloitte Consulting LLP
RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES
Full Stack Developer
Deloitte Consulting LLP
Massive-Scale Models of Urban Infrastructure and Populations
Friday, September 06, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
As the world becomes more dense, connected, and complex, it is increasingly difficult to answer “what-if” questions about our cities and populations. Most modeling and simulation tools struggle with scale and connectivity. We present a new method for creating digital twin simulations of city infrastructure and populations from open source and commercial data. We transform cellular location data into activity patterns for synthetic agents and use geospatial data to create the infrastructure and world in which these agents interact. We then leverage technologies and techniques intended for massive online gaming to create 1:1 scale simulations to answer these “what-if” questions about the future.
Ben Intoy is a full stack developer at Deloitte Consulting LLP. He received his PhD in Physics at Virginia Tech in 2015 where he used high throughput computing simulations to study stability properties of cyclically competing species in varying spatial dimensions. Ben then went to the University of Minnesota, Twin Cities campus, as a postdoctoral research associate where he used tools he learned in his PhD to abstractly study the origin of life on earth and the probability of finding life elsewhere in the universe. In fall 2018 Ben went to the Deloitte Arlington VA Office to work on the FutureScape project (www.futurescape.ai).
Dan Baeder is a data scientist at Deloitte Consulting LLP, and has been on the FutureScape project since joining the firm last year. While at Deloitte, Dan has focused on the use of cellular phone geolocation data for the development of synthetic traffic models, as well as the application of geospatial analysis techniques to human behavior modeling. He is a noted R-phile in a sea of Python users. Dan received an MS in Public Policy and Management with a focus on data analytics from Carnegie Mellon University in 2018.