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During the past decade, one of the most notable transformations in science has been the availability of large and
diverse sets of data. This trend has been accompanied by the increased use of machine learning and statistical
techniques to classify patients and optimize treatments in precision medicine. In this context several concomitant
factors can cause poor reproducibility levels, including unmeasured and heterogenous covariates' distributions across
studies, new technologies and ascertainment mechanisms. The focus of the workshop will be on statistical techniques
and applications to understand and prevent the most important and common causes of lapses of reproducibility.
Invited speakers include Keith Baggerly (The University of Texas MD Anderson Cancer Center), Bin Yu (University of
California, Berkeley), David Madigan (Columbia University), Edo Airoldi (Harvard University), Levi Waldron (City
University of New York).
As part of the workshop, we are hosting a student poster competition, with prizes for first, second and third place. We
invite students to present their work on novel statistical methods and computational approaches. A broad range of
contributions, from theoretical statistics to applications in a variety of biomedical fields will be evaluated. Students
with ongoing projects in robust statistics, reproducibility and replicablity are particularly encouraged to participate. To
submit your poster for consideration, click here: http://goo.gl/forms/WhdgrqDmTSkgHDxe2
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