Stodden to deliver keynote on reproducibility at IEEE Data Science Workshop

Associate Professor Victoria Stodden will be a keynote speaker at the 2018 IEEE Data Science Workshop, which will be held June 4-6 in Lausanne, Switzerland. The workshop will bring together researchers from the academic disciplines of data science, including signal processing, statistics, machine learning, data mining, and computer science, along with industry experts from fields such as personalized health and medicine, earth and environmental science, applied physics, finance and economics, and intelligent manufacturing. 

Stodden will give the keynote, "Reproducibility and Generalizability in Data-enabled Discovery."

Abstract: As computation becomes central to scientific research and discovery – bringing us the field of Data Science – new questions arise regarding the implementation, dissemination, and evaluation of methods that underlie scientific claims. I present a framework for conceptualizing the affordances that support Data Science including computational reproducibility, transparency, and generalizability of findings. For example, reproducibility in computational research can be interpreted most narrowly as a simple trace of computational steps that generate scientific findings, and most expansively as an entirely independent implementation of an experiment that tests the same hypothesis as previously published work. Standards for determining a scientific finding are necessarily adapting to computationally- and data-enabled research.  Finally, the social context for these innovations raises important questions regarding incentives to engage in new research practices and the ethics of these practices themselves.

Stodden's research addresses a wide range of topics, including standards of openness for data and code sharing, legal and policy barriers to disseminating reproducible research, robustness in replicated findings, cyberinfrastructure to enable reproducibility, and scientific publishing practices. She serves as an associate editor for reproducibility for the Journal of the American Statistical Society and serves on the National Academy of Sciences (NAS) Committee on Reproducibility and Replicability in Science and the NAS Roundtable on Data Science Postsecondary Education.

At Illinois, she holds affiliate appointments at the National Center for Supercomputing Applications (NCSA), College of Law, Department of Statistics, and Department of Computer Science. Stodden earned both her PhD in statistics and her law degree from Stanford University.

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