I am a freshly minted mathematics Ph.D. recipient. I received my doctorate from the University of California, San Diego in March 2021. Rayan Saab was my adviser. I received my bachelor’s degree in mathematics at the University of Georgia in 2015 where I was advised by the one-and-only Ted Shifrin. For anyone who is interested, here is a copy of my resume.
I’m primarily interested in designing and analyzing algorithms in data science and signal processing. Most of the work I’ve done during my Ph.D. considered applications of quantization, or discretization, in both of these contexts. You can watch a recording of my Ph.D. defense here. I use tools from high dimensional probability, stochastic processes, frame theory, and dynamical systems to answer questions about provably stable and computationally efficient algorithms for quantizing pre-trained neural networks, robustly recovering “low complexity” signals from quantized compressed sensing measurements, and how to design recovery algorithms from quantized measurements when the underlying signal comes from an approximately known manifold structure. I’ve also worked on a lot of fun summer projects related to indoor localization, fraud detection, and modelling aperiodic neural noise from EEG measurements.
As an undergraduate, I worked with Jason Cantarella on computational knot theory.
I spent a lot of time as a teaching assistant at UCSD. Checkout my teaching evaluations here.
With the help of Patty Wagner and a few of my then colleagues I helped record and uploaded lectures of the MATH 3500/3510 course at UGA. That course was incredibly influential on my career as a mathematician. You may find those lectures at the following YouTube page.