Juliana Freire 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.

Her program :

  • Monday, March 18, 2019 - From 10.30am to 3pm - On site meal on charge - Directions
    • Title: Exploring Big Urban Data
    • Place : Inria Saclay/LIX, Turing building, 1 rue Estienne d'Honoré d'Orves, 91120 Palaiseau - Room Gilles Kahn
    • 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.

  • Monday, February 11, 2019 - From 10.30am to 3pm - On site meal on charge - Directions
    • Title: Provenance and Computational Reproducibility
    • Place : Inria Saclay/LIX, Turing building, 1 rue Estienne d'Honoré d'Orves, 91120 Palaiseau - Room Gilles Kahn
    • Abstract:
Data-driven exploration has revolutionized science, industry and government alike. The abundance of data coupled with cheap and widely-available computing and storage resources has created a perfect storm that enabled this revolution. Now, the main bottleneck lies with people. To extract actionable insight from data, complex computational processes are required that are not only hard to assemble but that can also behave (and break) in unforeseen ways. Thus, when results are derived, an important question is whether you can trust them.

In this talk, I discuss the importance of maintaining detailed provenance (also referred to as lineage and pedigree) for data and computations. I will give an overview of techniques for capturing, managing, and re-using provenance information, and describe emerging applications and novel uses of provenance in collaborative data analysis, teaching science, and publishing reproducible results. Through concrete examples, I will also show that, besides providing important documentation that is key to preserve data, to determine the data's quality, reproduce and validate results, provenance can also be used to streamline the data exploration process.

  • Thursday, September 13, 2018 - 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.