I am an attending physicist with expertise in applying statistical modeling to the analysis of large, complex datasets in order to understand the relationship between treatment, patient, and disease characteristics and the probability of local control and normal tissue toxicity. My group’s research has focused specifically on algorithms that can be used to optimize treatment planning dose-distribution characteristics (calculating the best way to deliver increased radiation to the tumor while reducing radiation to surrounding tissue) and modeling the probability of treatment success (tumor eradication) and normal tissue complications as the radiation dose distribution varies.

My research group has published findings on a range of topics in medical physics, including image segmentation, proton beam treatment planning and optimization, intensity-modulated radiotherapy treatment planning, predictive modeling of tumor and normal tissue response based on multi-institutional datasets, Monte Carlo dose calculation methods, open-source research software development, and the identification of biomarkers to predict treatment response.