Juliana Freire will give a seminar Monday March 18, from 10.30am to 3pm
in Gilles Kahn room.
Title: Exploring Big Urban Data
Abstract:
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 numerous
spatio-temporal datasets containing information about diverse
components of the cities, including their residents, infrastructure,
and the environment. By analyzing the data exhaust from these
components, we have the opportunity to better understand how they
interact and obtain insights to help address important challenges
brought about by urbanization with respect to transportation, resource
consumption, housing affordability, and inadequate or aging
infrastructure. While there have been successful efforts where data
was used to improve operations, policies, and the quality of life for
residents, these have been few and far between, because analyzing
urban data often requires a staggering amount of work, from
identifying relevant data sets, cleaning and integrating them, to
performing exploratory analyses over complex, spatio-temporal data.
Our long-term research goal is to enable domain experts to crack the
code of cities by freely exploring the vast amounts of urban data. In
this talk, 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.
This work was supported in part by the National Science Foundation,
DARPA, a Google Faculty Research award, the Moore-Sloan Data Science
Environment at NYU, IBM Faculty Awards, NYU School of Engineering and
Center for Urban Science and Progress.
Bio:
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). 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 5 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.
