I am currently a VIGRE postdoctoral fellow in the Department of Statistics at Stanford University. I received my Ph.D. in Biomedical Sciences in August of 2007 from the University of Texas, Graduate School of Biomedical Sciences, Houston, with an area of focus in Biomathematics and Biostatistics. I conducted my work at M.D. Anderson Cancer Center in the Pharmaceutical Development Center under adviser Robert A Newman. My background is varied. Prior to my work in Texas, I obtained a BS in civil engineering from the University of Colorado, Boulder, and a masters degree in Oriental Medicine from Oregon College of Oriental Medicine, Portland, OR. After obtaining my master's degree I operated an acupuncture clinic in Portland for a couple of years. In my study of Traditional Chinese Medicine, I became interested in the use of plant compounds for treating diseases, cancer in particular. This interest eventually led to the publication of two books, which were essentially summaries and syntheses of published preclinical and clinical studies. Between books I met Dr. Newman, and he later invited me to M.D. Anderson to work on a Ph.D.

My primary interest is in developing statistical and dynamic models to predict the pharmacokinetic properties of plant-derived compounds, as well their pharmacological effects on cancer cells. The approach I have taken is systematic in that rather than studying a particular compound or a small set of similar compounds, I am interested in developing larger, more complex models to predict the properties of arbitrary compounds. Accurate predictive models will be useful to efficiently screen large libraries of compounds for promising drugs. Due to synergistic interactions, combinations of compounds can sometimes be more effective than single drugs. Thus I am also interested in modeling drug interactions, which can depend upon protein-drug binding affinity and protein-protein interactions. Specific research interests include:

• Quantitative structure-activity relationship (QSAR) models and other statistical models for predicting drug activity and absorption, distribution, metabolism, excretion (ADME) characteristics
• Statistical learning methods, including kernel learning methods (support vector machines, for example)
• Prediction of protein-drug binding affinity and protein-protein interactions using knowledge-based approaches
• Learning methods for relational data
• Systems biology, in general
• Methods to quantify drug interactions (synergism/antagonism/additivity)