Juliana Freire is a Professor of Computer Science and Engineering and Data Science at New York University. She holds an appointment at the Courant Institute for Mathematical Science, is a faculty member at the NYU Center for Urban Science and at the NYU Center of Data Science. She is the executive director of the NYU Moore-Sloan Data Science Environment, chair of the ACM SIGMOD and a council member of the Computing Community Consortium (CCC). Her recent research has focused on big-data analysis and visualization, large-scale information integration, web crawling and domain discovery, provenance management, and computational reproducibility. Prof. Freire is an active member of the database and Web research communities, with over 170 technical papers, several open-source systems, and 12 U.S. patents. She is an ACM Fellow and a recipient of an NSF CAREER, two IBM Faculty awards, and a Google Faculty Research award. She has chaired or co-chaired workshops and conferences, and participated as a program committee member in over 70 events. Her research grants are from 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, the University of Utah, New York University, Microsoft Research, Yahoo! and IBM.
Her program :
- Thursday 13 september - JDSE2018 - Plan accès LAL - Orsay
- Title:Democratizing Urban Data Exploration
- Abstract: The large volumes of urban data, along with vastly increased computing power, open up new opportunities to better understand cities. Encouraging success stories show that data can be leveraged to make operations more efficient, inform policies and planning, and improve the quality of life for residents. However, analyzing urban data often requires a staggering amount of work, from identifying relevant data sets, cleaning and integrating them, to performing exploratory analyses and creating predictive models that must take into account spatio-temporal processes. Our long-term goal is to enable domain experts to crack the code of cities by freely exploring the vast amounts of urban data. In this talk, we will present methods and systems that combine data management, analytics, and visualization to increase the level of interactivity, scalability, and usability for urban data exploration.