Juliana Freire (NYU) - Democratizing Urban Data ExplorationReturn to Full Calendar
- December 6, 2019 at 12:30pm - 1:30pm
- John Crerar Library, Room 390
- Event Audience:
Speaker: Juliana Freire Professor of Computer Science and Data Science, New York University
Juliana Freire is a Professor of Computer Science and Data Science at New York University. She is the elected chair of the ACM Special Interest Group on Management of Data (SIGMOD) and a council member of the Computing Research Association’s Computing Community Consortium (CCC). She was the lead investigator and executive director of the NYU Moore-Sloan Data Science Environment. Her research interests are in large-scale data analysis, curation and integration, visualization, provenance management, and web information discovery. She has made fundamental contributions to data management methods and tools that address problems introduced by emerging applications including urban analytics and computational reproducibility. Freire has published over 180 technical papers, several open-source systems, and is an inventor of 12 U.S. patents. She has co-authored 6 award-winning papers, including one that received the ACM SIGMOD Most Reproducible Paper Award. She is an ACM Fellow and a recipient of an NSF CAREER, two IBM Faculty awards, and a Google Faculty Research award. Her research has been funded by the National Science Foundation, DARPA, Department of Energy, National Institutes of Health, Sloan Foundation, Gordon and Betty Moore Foundation, W. M. Keck Foundation, Google, Amazon, AT&T Research, Microsoft Research, Yahoo! and IBM. She received M.Sc. and Ph.D. degrees in computer science from the State University of New York at Stony Brook.
Abstract: Democratizing Urban Data Exploration
The ability to collect data from urban environments through a variety of sensors, coupled with a push towards openness and transparency by governments, has resulted in the availability of a plethora of spatio-temporal datasets. By analyzing these data, we can better understand how different urban components behave and interact over space and time, and obtain insights to make city operations more efficient, inform policies and planning, and improve the quality of life for residents. While there have been successful efforts in this direction, they are few and far between. Analyzing urban data often requires a staggering amount of work, from identifying and wrangling relevant data, to carrying out exploratory analyses and creating predictive models — tasks that are often out of reach for domain experts that lack training in computing and data science. In this talk, I will discuss research we have done that aims to democratize data exploration. I will present methods and systems which combine data management, analytics, and visualization to increase the level of interactivity, scalability, and usability for spatio-temporal data analyses.
Sponsor: Center for Data and Computing