people   Jeremy is the sole member of the Purvis Lab. Born in Sarasota, Florida in 1979, he received a B.S. (2002) and M.S. (2004) in Microbiology from the University of Florida under the supervision of Lonnie Ingram. In a bold display of adventure, Jeremy left the golden shores of Florida to pursue a degree in Genomics and Computational Biology at the University of Pennsylvania in Philadelphia. Mentored by Scott Diamond (Chemical Engineering) and Ravi Radhakrishnan (Bioengineering), his doctoral work has focused on understanding cellular signaling from a systems perspective. Here are a few unpublished writing samples: On the Emerging Complexity of Gene Expression This piece was written for the Literature Review and Analysis portion of the 2007 Genomics and Computational Biology preliminary exam 2007. Penn has an especially high concentration of genome-wide association expertise and the focus of this analysis was the variability in gene expression — itself a heritable trait detectable by genetical genomics. A Critical Assessment of the Bayesian Approach to Causal Network Inference Written as a assignment for an independent study (Statistical Methods in Bioinformatics), this piece was intended mostly to humor our illustrious statistician and friend, Warren Ewens. A student of the frequentist old school, Warren was always suspicious of Bayesian analysis and once interrupted class to confront us each about our opinion of the Bayesian approach. Despite the biting tone of the critique, I am actually quite fond of the Sachs article as it has greatly influenced my thinking. Generalizability of Machine Learning Methods in Bioinformatics Also a class assignment (Principles in Computational Biology), this paper examines a recent collection of machine learning techniques and focuses on the generalizability of each method. Combined Statistical Methods for Describing Interacting Protein Networks A back-of-the-envelope method for generating probabilistic models of protein-protein interaction networks with spatial resolution.