My graduate work is in numerical analysis, specifically in the development of machine learning algorithms for high dimensional regression(supervised learning) with few observations. I have interests in the development of these algorithms for application in chemistry, physics, biology and network recovery. These applications utilize the geometry of multilinear tensor spaces for efficient computation and implementation, along with methods from image and signal processing for the representation of data for these algorithms.More recent work has been focused in the area of data science and bioinformatics. Currently I have projects working to understand role of natural environments in the spread of antibiotic resistant pathogens, modeling of large mammal activity from camera trap data, to the classification of coral species from molecular signatures. In addition to work in data science, I have a passion for teaching and educating the next generation of scientists who will increasingly need to rely on larger data sets and more sophisticated tools for understanding those data sets.