Sarah Cannon

Sarah Cannon earned a B.A. in mathematics at Tufts University in 2012 and an M.Sc. in mathematics and the foundations of computer science from the University of Oxford in 2013. Her current research as a Ph.D. student in the Algorithms, Combinatorics, and Optimization (ACO) program studies randomized algorithms, with a focus on establishing provable bounds for the mixing time of Markov chains. Sarah has published numerous articles, spanning graph theory, computational geometry, programmable matter and Markov chains, typically involving planar geometry. For example, a recent result (SODA15) proved the existence of a phase transition for the mixing time of a local Markov chain on weighted dyadic tilings, a family of rectangular dissections. Sarah received an NSF Graduate Research Fellowship, a Clare Boothe Luce Outstanding Graduate Fellowship and was previously named a Computing Research Association Outstanding Undergraduate Researcher.
Research Interests: 
Markov chains, sampling algorithms, random tilings, programmable matter, computational geometry
Expected Graduation Date: 
May, 2018
Advisor Name: 
Dana Randall