I am a mathematical biologist interested in developing mathematically sound approaches to the analysis of high-throughput DNA sequencing data. To do this, I utilize and develop techniques from the fields of probability, compressed sensing, and optimization. I am particularly interested in developing methods to analyze genomic and metagenomic data.
Use the quick links to the right to access information such as my CV, or the links at the top of the page to access a list of publications, my Github repository, etc.
A gene regulatory network is basically a representation of how genes interact with each other. In this work, we develop the only (to date) method to assess the accuracy of so called "motif discovery algorithms" that seek to find important sub-networks of a given gene regulatory network. We develop a provably correct mathematical approach (based on a variety of metrics that say how close two matrices are to each other) and use this to assess the performance of a variety of motif discovery algorithms.
I'm a mathematical biologist who is interested in developing mathematically sound approaches to the analysis of high-throughput DNA sequencing data.
This image is formed by using the most frequent words used when considering all of my publications.
This is my PhD thesis from Penn State (advised by Manfred Denker).