Note: This resume is also available as a PDF: resume.pdf
|Location: Golden, CO 80401|
|Phone: (508) 494-2474|
National Renewable Energy Lab (2015 - Present)
- Used machine learning techniques to develop a model to predict the tendency of a molecule to form soot from directly from its structure.
- Developed models of microbial metabolism to determine means of improving product yields through genetic engineering. Helped to develop and maintain python packages for such purposes, including
University of California, Santa Barbara (2010-2015)
Ph.D., Department of Chemical Engineering, Santa Barbara, California (GPA: 3.68)
- Thesis: Computational analysis of the mammalian circadian clock, with a focus on elucidating the functional design consequences of the underlying genetic regulatory network.
Tufts University (2006 - 2010)
BS, Chemical and Biological Engineering, Medford, Massachusetts (GPA: 3.79)
- Machine Learning: Neural networks, preprocessing methods, hyperparameter optimization
- Statistics: uncertainty analysis, bayesian methods, model selection
- Optimization: Linear programming, nonlinear programming, stochastic methods
- Nonlinear systems: Ordinary differential equations, collocation methods, sensitivity analysis
- Python: thorough familiarity with the PyData stack, including relational databases (
pandas), machine learning methods (
sklearn), and compiled extensions (
- Development: unittests, continuous integration, and helped to develop software for large open-source projects.
- Comfortable with unix environments, HPC, and front-end stack languages
Selected Peer-reviewed Publications
St. John, P. C., Crowley, M. F., & Bomble, Y. J. (2017). Efficient estimation of the maximum metabolic productivity of batch systems. Biotechnology for Biofuels, 10(1). doi:10.1186/s13068-017-0709-0
Abel, J.H., Meeker, K., Granados-Fuentes, D., St. John, P.C., Wang, T.J., Bales, B.B., Doyle F.J. III, Herzog, E.D., and L.R. Petzold. Functional network inference of the suprachiasmatic nucleus (2016) PNAS, 113 (16) pp. 4512-4517
St. John, P.C. and F.J. Doyle III. Quantifying stochastic noise in cultured circadian reporter cells (2015), PLoS Computational Biology 11(11): e1004451.
St. John, P.C., Taylor, S.R., Abel, J.H., and F.J. Doyle III. Amplitude metrics for cellular circadian bioluminescence reporters (2014) Biophysical Journal, 107 (11) pp. 2712-2722
St. John, P.C., Hirota, T., Kay, S.A. and F.J. Doyle III. Spatiotemporal separation of PER and CRY posttranslational regulation in the mammalian circadian clock (2014) PNAS, 111 (5) pp. 2040-2045.
Hirota, T., Lee, J.W., St. John, P.C., Sawa, M., Iwaisako, K., Noguchi, T., Pongsawakul, P.Y., Sonntag, T., Welsh, D.K., Brenner, D.A., Doyle, F.J. III, Schultz, P.G., Kay, S.A., Identification of small molecule activators of cryptochrome (2012) Science, 337 (6098) pp. 1094-1097.
|Google Scholar: https://scholar.google.com/citations?user=NdWzcVMAAAAJ|