Bio 
I received a B.S. in Chemical Engineering and a B.S. in Mathematics from Rensselaer Polytechnic Institute. Prior to college, I attended Grand Island Senior High School near Buffalo, NY. Although I had always been interested in math, it was not until high school that I really began to enjoy science classes. Great teachers in my Chemistry, AP Physics, and AP Biology classes inspired me to consider studying science in college. By entering the field of engineering I was able to combine my interest in both math and science. My graduate research in the chemical engineering department at Cornell University involves computational modeling of cancer networks, my current project being prostate cancer. Many of our techniques can be applied elsewhere, including the study of other complex networks such as E. coli tolerance in green manufacturing and biofuel production. I am interested in looking at alternative forms of energy production through biotechnology. I have been involved in many past outreach activities including being an Outreach Liaison in SWE and the Outreach Coordinator in the CBE Grad Women's Group. Outside of research I enjoy playing lacrosse, soccer, and horseback riding.
Research
The development of sustainable manufacturing practices using renewable feedstocks has become a global priority. In the past 15 years, we have made great strides toward this priority with successes such as 1,3 Propanediol (3G) production in E. coli (used in polyester) or more recently the production of isoprene (synthetic rubber used in tires, shoes, etc) in E. coli, both using glucose as the feedstock. E. coli is commonly used to manufacture products in biotechnology because it is well understood and relatively easy to manipulate compared to other organisms. However, while the metabolic engineering to manufacture 3G or isoprene in E. coli was a great technical achievement, the attractiveness of these processes remains strongly dependent on the price of crude-oil because of the productivity of the production host. Currently the cost of producing fuels in E. coli, because of the low production, is much higher than the price of crude-oil. There are many factors controlling productivity, one of the most challenging is product tolerance. The byproduct itself, e.g. ethanol, butanol, etc., can act as a toxin to the E. coli and lead to cell death, lowering production. Thus engineering greater tolerance of E. coli to toxins can lead to higher production. Unfortunately, tolerance is an example of a complex phenotype. There is not a single "tolerance operon" or program that could be rationally manipulated to make a robust production host. Rather the ability of the production host to grow and maintain high productivity in the presence of high levels of intermediates or end-products is controlled by many interacting intracellular subsystems. This complexity naturally lends itself to mathematical analysis using a variety of network and mathematical modeling tools. We propose that physiochemical models of signaling architectures can serve as hypotheses generation engines which could suggest targeted reprogramming strategies of organisms such as E. coli.