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About |
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My group works at the intersection of clinical research, molecular biology and informatics to understand the fetal origins of pediatric obesity. Most of the active projects in the lab are related to human milk metabolomics, analysis of electronic health records and the infant microbiome.
Making sense of BIG DATA is difficult and requires a dynamic and adaptive analytics.
Our conceptual work addresses information processing in the nervous system from two angles: (1) By analyzing and explaining electrophysiological data, we study what neurons do. (2) By analyzing and explaining human behavior, we study what all these neurons do together. Much of our work looks at these questions from a normative or causal viewpoint, asking what problems the nervous system should be solving. This often means taking a Bayesian approach. Bayesian decision theory is the systematic way of calculating how the nervous system may make good decisions in the presence of uncertainty. Causal inference from observational data promises to be a key enabler for progress in science.
We are an interdisciplinary group that span statistics, molecular biology, and many other disciplines. Visit our people page to see more information on each person who works in the lab (publications, contact information, photos).
For PDFs of our work, visit our publications page. Feel free to issue on Github if links don't work or are obsolete.
works