About Me

I am a Mathematician at the Federal Aviation Administration (FAA), where I develop algorithms and machine learning methods to analyze large-scale, near-real-time flight data from the National Airspace System. My work focuses on quantifying and improving system performance, enabling more predictable air traffic operations.

I received my Ph.D. in Mathematics from the University of Virginia in 2021, where I specialized in algebraic topology under Professor Nicholas Kuhn. My dissertation, Applications of Chromatic Fixed Point Theory, combined large-scale computation with pure algebraic topology. I designed new algorithms for computing spectral sequences, implemented them on high-performance clusters, and used the results to establish general theorems in chromatic homotopy theory.

Talks

• Departure Fix Throughput

  September 25, 2024

• Research Summary

  September 24, 2020

Papers

Machine Learning in Aviation

• An ASDE-X Based Tool for Surface Delay Measurements

  July 16, 2025 — with Benjamin Kurland et al.

  AIAA AVIATION FORUM AND ASCEND 2025

  DOI Publisher Link

• AIAA Operationalizing Machine Learning Models for Strategic Planning

  June 08, 2023 — with Kamala Shetty et al.

  AIAA AVIATION 2023 Forum — Machine Learning and AI I

  DOI Publisher Link

Algebraic Topology

• Chromatic fixed point theory and the Balmer spectrum for extraspecial 2-groups.

  June 01, 2024 — with Nicholas J. Kuhn

  American Journal of Mathematics

  arXiv:2008.00330 [math.RT] DOI

• Computing the Morava K–theory of real Grassmannians using chromatic fixed point theory.

  April 12, 2024 — with Nicholas J. Kuhn

  Algebraic & Geometric Topology

  arXiv:2008.00330 [math.RT] DOI