Presenter Bio:

Brad Sturt is an assistant professor of Information and Decision Sciences at the University of Illinois Chicago. His research uses the methodologies of stochastic programming and robust optimization to develop solutions for operational problems in business and government. Recent applications have included election security, data-driven assortment planning, and high-dimensional options pricing. His research has received several recognitions, including the Roger J-B Wets Junior Researcher Best Paper Prize in Stochastic Programming, second place in the INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, and second place in the INFORMS George Nicholson Student Paper Competition. Outside of academia, he is a co-founder of BallotIQ, an elections administration startup based in Ann Arbor.

Abstract:

For more than a century, election officials across the United States have inspected voting machines before elections using a procedure called Logic and Accuracy Testing (LAT). This procedure consists of officials casting a test deck of ballots into each machine and confirming the machine produces the expected vote totals. In this talk, I will bring a scientific perspective to LAT by introducing a robust optimization approach to designing test decks with rigorous security guarantees. To facilitate deployment at scale, we develop a practically efficient custom algorithm for solving our robust optimization problems based on the cutting-plane method. In partnership with the Michigan Bureau of Elections, we retrospectively applied our approach to the November 2022 election, which revealed that our test decks would have required only 1.2% more ballots than the current practice. Our approach has since been deployed by Michigan in real-world elections to improve election security and increase public confidence.