معرفی
Sarah Cannon is an Associate Professor in the Department of Mathematical Sciences at Claremont McKenna College (CMC), part of the Claremont Colleges consortium in Claremont, CA. Her research focuses on theoretical computer science, randomized algorithms, and Markov chains applied to problems in discrete geometry and statistical physics, with significant work on the mathematics of political redistricting. She holds an NSF CAREER Grant and has led projects supported by the Haynes Foundation and the Simons Institute.
Education:
- Ph.D. in Algorithms, Combinatorics, and Optimization (Georgia Tech, 2018)
- M.Sc. in Mathematics and Foundations of Computer Science (University of Oxford, 2013)
- B.A. in Mathematics (Tufts University, 2012)
Research Interests: Dr. Cannon’s work bridges theoretical computer science and applied mathematics, emphasizing algorithmic fairness, sampling methods for complex systems, and applications to social policy. Key areas include redistricting reform, emergent phenomena in programmable matter, and phase transitions in combinatorial structures.
Grants & Awards:
- NSF CAREER Grant (2025): Focuses on sampling algorithms for graph structures.
- NSF Postdoctoral Fellowship (2018–2019): Conducted research at UC Berkeley.
- Clare Boothe Luce Fellowship (Ph.D. support).
Teaching & Advising: Teaches courses on redistricting mathematics, data science, and discrete mathematics. Supervises interdisciplinary student research on redistricting, graph algorithms, and Markov chain analysis. Notable student projects include studies on LA city council representation and Arizona gerrymandering.
Professional Engagement: Serves as Secretary of the SIAM Activity Group on Discrete Mathematics. Active in the Voting Rights Data Institute and the Simons Institute’s Algorithms, Fairness, and Equity program. Frequently speaks at conferences like STOC and MSRI.
