My research interest lies within the domain of developing methodologies to improve classifier stability for small sample data sets with high volumes of objects. Applications emerge naturally in functional gene detection in bioinformatics where the number of samples is small and the number of genes is large.
I have always had a passion for teaching, starting as an undergraduate at UC Berkeley teaching an adjunct and a summer bridge courses. Studying in UC Berkeley's statistics Ph.D. program allowed me to further explore the different aspects of teaching and I find it very rewarding to show students the beauty and power of data science. I also spent time in industry working at Amazon as a data scientist before joining Princeton.