Sean uses high-dimensional data to construct causal networks of interconnected molecular and phenotypic features. He is interested in understanding how perturbations propagate across these networks causing diseases and aging. He helps to develop Calico’s capabilities for computational mass spectrometry, both to cast a wide net in search of biomarkers, and also to build principled integrative-omic models.
During his Ph.D., Sean developed methods for understanding metabolism at the interface of fluxes, metabolites and enzymes using high-dimensional data. He devoted his postdoc to developing techniques for extracting additional information from such data modalities.
- Postdoc with John Storey, Princeton University
- Ph.D. in Quantitative and Computational Biology, Princeton University
- B.S. in Biological Sciences (Genetics and Development), Cornell University
- MIT Sloan Sports Analytics Conference Research Paper Finalist - 2017
- Department of Energy Office of Science Graduate Fellow - 2012