I am an industrial mathematician and signal processing professional. I translate real world issues into math problems, and develop software and algorithms to solve those problems. My expertise is at the cross section of numerical optimization, machine learning, time-frequency analysis, and sparse representation.
I was born and raised in Tulsa, OK, where I thought I would be a professional trumpet player until age 18. Then I went to Cleveland, OH for my undergraduate and masters education at Case Western Reserve University, in Applied Mathematics. There I mostly focused on parallel MRI reconstruction (i.e., compressive sensing), in terms of numerical optimization. I began my Ph.D. in EE at NYU Tandon in 2015, where we study optimization tools through convex analysis for various applications. I defended my thesis on combining variable splitting methods and deep learning, then graduated in May 2019. I am now doing signal processing research as a contractor to the US Department of Energy.
Education
New York University 2019
Doctorate of Philosophy in Electrical Engineering
Focus: digital signal processing, machine learning
Case Western Reserve University 2015
Master of Science, Applied Mathematics
Case Western Reserve University 2014
Bachelor of Science, Applied Mathematics
Minor in Physics