Research Link to heading

Quantum: Algorithms and Information Link to heading

I have the pleasure of working with Professor Alexandra Kolla’s Quantum Algorithms research group here in Santa Cruz. We’ve spent the last year or so working on approximation algorithms for different Mathematical spin glass systems. Check out our papers, “Approximation Algorithms for Quantum Max-d-Cut” and “Monogamy of Entanglement Bounds and Improved Approximation Algorithms for Qudit Hamiltonians”.

Recently, we’ve turned our attention to objects known as Quantum Graphs, and are working on new results for these interesting objects!

Quantifying Gerrymandering: Algorithms for Fairer Redistricting Link to heading

During my undergrad, I worked closely with Professor Eric Vigoda to complete an undergraduate thesis, titled Quantifying Gerrymandering with Simulated Annealing. I joined Professor Vigoda’s research group with the goal of implementing a redistricting markov chain to test Congressional districting fairness. We focused our efforts on generating a distribution of viable districting plans for the state of Texas, comparing the election results of the enacted plan to the results produced from our distribution for the 2020 US House Congressional vote. The goal was to quantify the fairness of any given districting plan, allowing for a better legal metric necessary to prove that a plan is gerrymandered. We came up with some pretty interesting results!

I am continuing this work as a technical mentor in UCSB’s Early Research Scholars Program, where we will be setting our sights on California. Our hope is to build and launch a public application that allows users to evaluate the fairness of proposed (or their own) districting plans, and come with some new algorithms and techniques along the way.