Phase Transitions and Computation


October 6, 2010


Allan Sly


U.C. Berkeley


The last decade has seen a growing number of connections between statistical physics phase transitions and the theory of computation. Techniques from spin glasses have transformed the understanding of random constraint satisfaction problems while phase transitions play the central role in the efficiency of a wide class of MCMC algorithms. I will survey recent developments in these areas and describe new results on the complexity of counting independent sets.


Allan Sly

Allan Sly is a postdoc in the theory group of Microsoft Research, Redmond. He received his PhD in statistics at UC Berkeley in 2009 under the supervision of Elchanan Mossel. Allan’s research interests lie in probability theory and its applications in theoretical computer science, statistical physics, statistics and computational biology.