My research interests lie broadly in the fields of applied harmonic analysis and machine learning. One of the main ideas that I exploit is the representation of data (for example, human voice, social media trends, or, more related to my work, images) into relatively simple building blocks that harness hidden structures in the data to extract relevant information and often reduce the computational cost of algorithms. A few subareas of applied harmonic analysis and machine learning included in my past and current work are diffusion geometry, analysis on graphs, composite wavelets, and image processing. I have enjoyed applying tools from these domains on projects such as early diagnosis of autism spectrum disorder via extraction of biological markers present in placenta images, detection of age-related macular degeneration via identification of anomalies in retinal images, motion detection in animated images for Pixar.

I have taught a wide range of courses, from calculus and differential equations to more advanced courses, Fourier and wavelet analysis, numerical analysis, and recently, machine learning in the EDGE Program. I enjoy teaching mathematics courses at all levels as they offer varied audiences and, thus, exciting pedagogical challenges and rewards. In each class, my goal is to make students reach a competency level that yields confidence, genuine enjoyment, and deep appreciation for the mathematical thought process. Ideally, this is accompanied by curiosity about mathematics and its incredible applications. Whenever possible, I draw on my applied mathematics background to introduce certain concepts or make them more relevant through carefully picked examples.