My primary research interests lie at the interface of health decision science and mathematical biology. I am interested in the analysis of cancer epidemiology data using mechanistic models of carcinogenesis to reverse-engineer the biological mechanisms of cancer progression and to identify approaches for more effective cancer control strategies. Currently, I am investigating how different assumptions on the timing of the cancer cells spread and how the metastasis process is modeled may lead to biased estimates of the benefit of mammography and adjuvant treatment among early stage breast cancer patients. For this purpose, I have developed a multiscale model that integrates biological data and the observed epidemiological data. This modeling framework can facilitate the inclusion of recent advances in the field of cancer biology coupled with epidemiological data thereby permitting a more evidence-based and comprehensive evaluation of cancer control strategies.
I am also interested in developing novel methodologies in or porting existing methodologies from applied mathematics, machine learning, and bayesian statistics for application in decision modeling. These topics include computational problems in value of information analyses and the use of stochastic differential equation for decision modeling.
I have extensive modeling experiences in breast cancer.
My current reseach projects include:
I received my Ph.D. in health services research, policy, and administration with a focus on health decision science from the University of Minnesota, Twin-Cities.