Chiara Sabatti |
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Professor of Biomedical Data Science and Statistics
Stanford University |
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Tech Vision Talk: Replication, Robustness and Interpretability: Improving How We Communicate Scientific Findings
Abstract: In a world where large comprehensive datasets are readily available in digital form, scientists engage in data analysis before formulating precise hypotheses, with the goal of exploring and identifying tantalizing patterns. What is the difference between one of these initial findings and a “scientific discovery”? How do we communicate the level of uncertainty associated with each finding? How do we quantify its level of corroboration and replication? What can we say about its generalizability and robustness? We will consider these challenges from the vantage point of genome-wide association studies. We will review some classical approaches to quantifying the strength of evidence, identify some of their limitations, and explore novel proposals. We will underscore the connections between clear, precise reporting of scientific evidence and “social good”.
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Biography
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Chiara grew up in Italy, where she attended Liceo Classico Arnaldo in Brescia, and obtained a master degree in "Economics and Social Sciences" (DES) from the Luigi Bocconi University in Milan in 1993. Her final research project was on applications of finite exchangeability to population sampling, and was supervised by Eugenio Regazzini. In the same year, she was awarded a Bocconi Fellowship for advanced studies. She came to Stanford in 1994 to pursue a PhD in Statistics, and worked with Jun Liu on multiscale MCMC methods. Between 1998 and 2000 she was a post-doctoral scholar with Neil Risch in the Department of Genetics, also at Stanford. In 2000 she joined the faculty at UCLA in the newly established departments of Human Genetics and Statistics. She received the NSF Career award in 2003.
Chiara's research is centered on the development of statistical methods that enable the exploration of high dimensional data. This entails both reducing computational barriers and ensuring that the results obtained by sifting through a large number of variables are reliable, reproducible, and robust. Her work is by nature interdisciplinary: she has enjoyed collaborating with neuroscientists, engineers, chemists, psychiatrists, oncologists, and more in her home institutions and around the globe. She is grateful that her background prepared her for this. |