Research interests

My primary research interest is quantitative modeling of dynamics and emergent properties of biological systems, especially cancer signaling networks and cancer ecology and evolution. I want to learn how to leverage biomedical big data to improve models of cancer cell behavior and personalize therapy, particularly in underserved fields like childhood cancer research. Network biology fascinates me and I want to learn how to model cell regulatory networks and the behavior of complex adaptive systems, particularly in cancer. I will channel my passion for biological networks and cell decision making into a career that can benefit others.

Topics that excite me include evolution of chemoresistance in cancer, time series modeling of treatment effects on cancer cell signaling, longitudinal analysis of patient tumors, and the impact of tumor heterogeneity on molecular circuitry, treatment response and tumor ecology. I want to learn how to analyze and integrate transcriptomic, proteomic, metabolomic data sets to form comprehensive models of cell behavior. These models can help predict how systems will behave, infer how network alterations lead to aberrant cell behavior, and suggest how treatments could manipulate molecular circuitry to halt or reverse disease processes and improve patient quality of life.

Quantitative systems biology promises to broadly advance understanding of complex diseases, from cancer to genetic disorders to neurodegeneration. Improvements in data analysis tools and skills ripple across biomedical research, so my quantitative cancer biology work can potentially impact a wide network of patients. Doctoral education will teach me to how integrate high-throughput data into actionable insights and effectively contribute to clinical research.

I value experience learning a broad range of computational biology skills not only so I can effectively integrate data into robust models and actionable discoveries, but also so I can share these skills with others through teaching.