Resume (Jan 2022), mjragone (at) ucdavis.edu

My name is Michael Ragone, and I’m a fourth year math PhD student at UC Davis with advisor Bruno Nachtergaele. I am currently working on the theory of quantum phases: these are the quantum extensions of solids, liquids, and gases from elementary school, and they’re fascinating! I care especially about symmetry-protected topological phases, a class of phases which includes topological insulators and could theoretically allow us to build quantum computers with built-in error correction.

More generally, we’re interested in a class of models known as “quantum spin systems”, a fascinating framework arising from condensed matter physics that models a variety of systems, for example ions in a crystal lattice or electrons in a semiconductor. The mathematics is rich and vibrant, pulling from varied fields like functional analysis and representation theory, and has tight connections to applied fields like quantum information theory and quantum field theory. I’m generally fond of math inspired by the natural world–the universe asks some pretty great questions!

I am also in the nascent phases of a project with Isaac Kim on quantum circuits for noisy intermediate-scale quantum (NISQ) devices.

Old Stuff: Computational Neuroscience

For a good chunk of my college career at the University of Arizona, I researched computational neuroscience in the Computational and Experimental Neuroscience Lab (CENL) under Dr. Jean-Marc Fellous. We developed a biophysical model of the rat’s spatial navigation system, and we collaborated with the Laboratory for Information Processing Systems (LIPS) to investigate coding theoretic properties of place cell networks and sharp-wave ripple events. Here’s our abstract from SFN 2016. There’s lots of other interesting work happening in both labs—check them out!

Old Stuff: Engineering Senior Design

My engineering senior design team at the University of Arizona created a machine learning denoiser for General Dynamics for the Coast Guard. You see, the Coast Guard regularly receives distress calls from boats that are…well, distressed. These radio signals are often made noisy by atmospheric interference, so the Coast Guard manually filters these signals until they are listenable. We were tasked with finding a better solution using machine learning. So, we created a framework wherein noisy audio signals are processed, converted into a form that highlights vocal features, and fed to a specially trained autoencoder. Our results show promise, and I suspect that following some refinement at General Dynamics, the Coast Guard may have a powerful new tool for incoming calls.

Random stuff

I’m also a huge coffee and food nerd. I’ve scattered pictures of stuff I’ve made around the website—if you have coffee/food/music suggestions, I’d love to hear them!

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