Senior Scientist, DOE contractor
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.
Research Interests: Numerical optimization, deep learning, time-frequency analysis, sparse signal representation, convex analysis, uncertainty propagation
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
Ernst Weber Fellowship Recipient.