Research

My research interests are broad and forming. I like thinking mathematically, and I've particularly enjoyed working on projects that have social impact.

Statistical Election Audits

Since 2020 I've been working on a team led by Professor Poorvi Vora on statistical risk-limiting audits (RLAs) which validate election results within rigorous error bounds. I've enjoyed working on both the theory and implementation, contributing to our open source audit software. My first paper for this work is listed below, and additional manuscripts presenting novel audits and related results are currently being drafted. In April 2022, I presented a poster on some of this work at the GW SEAS R+D Showcase.

  • [pdf] In review: Oliver Broadrick, Poorvi Vora, and Filip Zagórski, "PROVIDENCE: a Flexible Round-by-Round Risk-Limiting Audit". USENIX Security 2023.
  • [pdf, video, slides] Oliver Broadrick, Sarah Morin, Grant McClearn, Neal McBurnett, Poorvi L. Vora, and Filip Zagórski, "Simulations of Ballot Polling Risk-Limiting Audits". Seventh Workshop on Advances in Secure Electronic Voting, in association with Financial Cryptography 2022.

Glitter

Hold a sheet of a glitter and enjoy the sparkles. Now rotate it slightly. A whole new sparkle pattern has emerged! This high sensitivity of appearance to orientation make glitter sheets a useful object for single image camera calibration. Summer 2022, I am working with Professor Robert Pless and peer Addy Irankunda to test this hypothesis. Semiregular blog posts will document my work at a high level. So far I've made posts 1, 2, 3, 4, and 5.

A sheet of a glitter with fiducial markers in the corner for locating individual specs.
Scheduling

I designed and analyzed scheduling algorithms with the imprecise computation model for real-time AI tasks at the edge. Here is the software.

  • [ ] Hesham Fouad, Oliver Broadrick, Benjamin Harvey, Charles Peeke, Bhagirath Narahari, "Real-Time AI: Using AI on the Tactical Edge." In Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams. Elsevier, 2023.