Towards Usability, Transparency, and Trust for Data-Driven Discovery

This event will also be livestreamed on Zoom.

The abundance of data, coupled with cheap and widely-available computing and storage, has revolutionized science, industry and government alike. Now, to a large extent, the bottleneck to actionable insights lies with people. From a data management and systems perspective, this leads to several challenges including the need to build usable and scalable tools that support the interactivity requirements for exploratory analyses and guide users in the various steps of the data science lifecycle. I will present a set of techniques and systems we have developed which combine methods from multiple areas of computer science to address these challenges, and discuss how we applied them in the study of urban systems. I will also reflect on the importance of provenance in this context not only to support transparency and reproducibility, but also enable experts to debug and build trust in the insights they derive.

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). She served as a council member of the Computing Research Association’s Computing Community Consortium (CCC), and was the NYU lead investigator for the Moore-Sloan Data Science Environment, a $32.8 million grant awarded jointly to UW, NYU, and UC Berkeley. She develops methods and systems that enable a wide range of users to obtain trustworthy insights from data. This spans topics in large-scale data analysis and integration, visualization, machine learning, provenance management, and web information discovery, and different application areas, including urban analytics, predictive modeling, and computational reproducibility. Freire has co-authored over 200 technical papers (including 11 award-winning publications), several open-source systems, and is an inventor of 12 U.S. patents. According to Google Scholar, her h-index is 60 and her work has received over 15,400 citations. She is an ACM Fellow and a recipient of an NSF CAREER, two IBM Faculty awards, and a Google Faculty Research award.  She was awarded the ACM SIGMOD Contributions Award in 2020. 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 a B.S. degree in computer science from the Federal University of Ceara (Brazil), and M.Sc. and Ph.D. degrees in computer science from the State University of New York at Stony Brook.