My current research often considers problems related to Randomized Numerical Linear Algebra (RandNLA). This area can be regarded as a fusion between more theoretically oriented fields, such as random matrix theory and matrix perturbation theory, with the more practice-oriented field of numerical linear algebra. This area thoroughly captures my goal of studying practical theory. I spend my time learning about mathematically elegant results, such as establishing matrix concentration inequalities via the matrix Laplace transform, and then use the consequences of these results to develop new approaches to ubiquitous computational problems, such as linear optimization.

Prior to starting my PhD, I carried out research in the field of Health Informatics, working with researchers at Regenstrief Institute. While doing so, I asked (and partially answered) questions related to the deployment of machine learning in healthcare. This was an excellent experience, as it emphasized the systems-level challenges (e.g. data privacy, bureaucratic inertia, formatting standards) to effectively using the algorithms we develop to solve end-use problems.

(**) Denotes alphabetical author order

Under Review/In Preparation

Health Informatics Research