My professional interests are somewhat diverse. I am interested in scientific programming, pure math (particularly differential/Riemannian geometry), bioinformatics, data mining and machine-learning algorithms, applied mathematics (with a focus on nonlinear dynamical systems) and anything else that intrigues me. In my career, I have alternated between software development, teaching, and conducting research. At present, I am working on research and development of ISIS software.
B.A. (Pure Mathematics) UC Santa Cruz, Santa Cruz CA (1996)
M.A. (Applied Mathematics) California State University, Fullerton (2004)
Ph.d (Computational Science and Statistics) South Dakota State University, Brookings SD (2011)
In the past, my area of research was in the field of bioinformatics and genomics, with a focus on studying
microarray experiments conducted on Arabidopsis thaliana (AKA thale cress). This is a small flowering plant native to Eurasia which fulfills the same role in the plant science world that Drosophila melanogaster (the fruit fly) fulfills in the realm of animal science. It has a small genome which is easy to study, and more importantly, the functional role of many of the genes in Arabidopsis have similar analogues in higher-level plants.
BibliographyArraySearch: A Web-Based Genomic Search Engine
Journal: Comparative and Functional Genomics (March 4, 2012)
ArraySearch finds statistical correlations between newly observed gene expression profiles and the huge source of well-characterized expression signatures deposited in the public domain. A search query of a list of genes will return experiments on which the genes are significantly up- or downregulated collectively. Searches can also be conducted using gene expression signatures from new experiments. This resource will empower biological researchers with a statistical method to explore expression data from their own research by comparing it with expression signatures from a large public archive.
Identification of metagenes and their interactions through large-scale analysis of Arabidopsis gene expression data.BMC Genomics (June 13, 2012)link: http://www.ncbi.nlm.nih.gov/pubmed/22694750
BACKGROUND: Many plant genes have been identified through whole genome and deep transcriptome sequencing and other methods; yet our knowledge on the function of many of these genes remains limited. The integration and analysis of large gene-expression datasets gives researchers the ability to formalize hypotheses concerning the functionality and interaction between different groups of correlated genes.