Bio 
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. I was also active in varsity lacrosse, soccer and indoor track in high school. I chose to enter the field of engineering where I was able to combine my interest in both math and science. This led to me receiving both a B.S. in Chemical Engineering and a B.S. in Mathematics from Rensselaer Polytechnic Institute.
After graduating from RPI, I decided to attend Cornell University and joined the research group of Dr. Jeffrey Varner. My graduate research in the chemical engineering department involves computational modeling of complex networks. Currently, I am looking at the development of androgen-independence in prostate cancer cells. Many of our modeling techniques can be applied to other systems, including the study of networks such as E. coli tolerance in green manufacturing and bio fuel production. Green manufacturing includes the development of bio fuels as well as other previous petroleum-based products, such as isoprene (synthetic rubber used in tires, shoes, etc) and 1,3 propanediol (used in polyester production).
I have been involved in many outreach activities in the past, including being an Outreach Liaison in SWE (Society of Women Engineers) and the Outreach Coordinator in the CBE Grad Women’s Group. Due to the fact that I was never exposed to engineering as a high school student, I like supporting younger students in learning more about engineering and science through outreach programs. Outside of research, I enjoy lacrosse, soccer, horseback riding, and traveling.
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 goal 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 necessary 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. Currently, the cost of producing fuels in E. coli is much higher than the price of crude-oil because of the low throughput. There are many factors controlling productivity, one of the most challenging being product tolerance. The goal product itself (e.g., ethanol, butanol) can act as a toxin to the E. coli, leading to cell death and 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, i.e., an organism’s resulting behavior is a product of a complex network of genes and proteins. There is not a single “tolerance operon” 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 be used to develop new hypotheses that could suggest targeted reprogramming strategies of organisms such as E. coli.