Juliana Freire, New York University, will present “Towards Usability, Transparency, and Trust for Data-Intensive Computations,” part of the National Science Foundation (NSF) Computer & Information Science & Engineering (CISE) Distinguished Lecture Series on January 28th, 2020, from 11:00AM to 12:00PM ET.
Juliana Freire is a Professor of Computer Science and Data Science at New York University. Previously, she was a faculty member at the University of Utah and Oregon Health & Sciences University, and a Research Staff Member at the Database Systems Research group at Bell Labs Research (Lucent Technologies). 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). She was the lead investigator and executive director of the NYU Moore-Sloan Data Science Environment. 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. 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.
Abstract: 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. To extract knowledge from data, complex computations need to be assembled; these are often out of reach for domain experts that do not have training in computing. In addition, in the path from data to decisions there is much room for error, from problems with the data and computations to human mistakes. In this talk, I will present a set of projects that combine methods from multiple areas of computer science and systems we have built to empower domain experts to explore data. I will also discuss the importance of having provenance of the data exploration process to not only support transparency and reproducibility, but also enable experts to build trust in the insights they derive.
To attend in person (NSF Room E3410), NSF visitors must contact Appolinaire A. Abo (aabo@nsf.gov) so that a visitor pass can be arranged. To attend virtually, join via WebEx.
