Séminaire "Exploring Big Urban Data" avec Juliana Freire
Juliana Freire, professeur d'informatique et de sciences des données à l'Université de New York, invitée par DigiCosme et par Inria dans l'équipe Cedar, animera le séminaire "Exploring Big Urban Data"
lundi 18 mars de 10h30 à 15h00 au centre de recherche Inria Saclay - Île-de-France.
Elle y présentera des méthodes et des systèmes qui combinent la gestion, l'analyse et la visualisation des données urbaine afin d'accroître le niveau d'interactivité, d'évolutivité et d'utilisabilité pour les analyses spatio-temporelles des données.
- Date : 18/03/2019
- Lieu : Centre de recherche Inria Saclay - Île-de-France - Salle Gilles Kahn
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.
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.