Can we Trust Data-Driven Scientific Discoveries?

As more and more scientific domains are collecting vast troves of data, we rely on machine learning techniques to analyze the data and help make data-driven scientific discoveries. In this public lecture, Genevera Allen will discuss how machine learning has been used to advance science and ask: "are these data-driven discoveries reproducible?" and "how can we use machine learning to draw reliable scientific conclusions?" Genevera will discuss these questions by giving examples from her own research, including an extended example on clustering. Additionally, she will outline both new research directions and offer practical advice for improving the reliability and reproducibility of data-driven discoveries.

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Genevera Allen is an Associate Professor of Electrical and Computer Engineering, Statistics and Computer Science at Rice University and an investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital and Baylor College of Medicine. She is also the Founder and Faculty Director of the Rice Center for Transforming Data to Knowledge, informally called the Rice D2K Lab. Dr. Allen's research focuses on developing statistical machine learning tools to help scientists make reproducible data-driven discoveries. Her work lies in the areas of interpretable machine learning, optimization, data integration, modern multivariate analysis, and graphical models with applications in neuroscience and bioinformatics.