Juliana Freire: Provenance and Computational Reproducibility

Juliana Freire, Professor at NYU, will give a two-parts seminar (with
a lunch break)
on Monday, February 11, 2019 – From 10.30am to 3pm, titled:

Provenance and Computational Reproducibility

Place: Inria Saclay/LIX, Turing building, 1 rue Estienne d’Honoré
d’Orves, 91120 Palaiseau – Room Gilles Kahn


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.

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.

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