Colloquium d’Informatique de Sorbonne Université
Cláudio T. Silva, New York University

Tuesday 11 June 2019 18:00
Amphi Charpak Sorbonne University - Faculté des Sciences

Urban Data Science

Cláudio T. Silva

Cláudio T. Silva is Professor of Computer Science and Engineering and Data Science at New York University. His research has focused on data science, visualization, graphics, and geometry processing. Recently he has been particularly interested in urban and sports applications. He received his BS in mathematics from Universidade Federal do Ceará (Brazil), and his MS and PhD in computer science at SUNY-Stony Brook. Claudio is a Fellow of the IEEE and has received the IEEE Visualization Technical Achievement Award. The's Statcast player tracking system, which he helped develop, won the Alpha Award for Best Analytics Innovation/Technology at the 2015 MIT Sloan Sports Analytics Conference, and more recently a 2018 Technology & Engineering Emmy Award from the National Academy of Television Arts & Sciences. Our lab’s work has been covered in The New York Times, The Economist, ESPN, and other major news media.


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 take into account spatio-temporal processes. Visual analytics systems can greatly help in the analysis of urban data allowing domain experts from academia and city governments better understand cities. In this talk, we discuss our work in building a visual analytics framework to interactively explore large spatio- temporal data sets and give an overview of our research that combines visualization and data management to tackle these challenges.

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