Abstract

Classical interacting particle systems studied in statistical physics are intimately connected to constraint satisfaction problems studied in computer science. Much progress has been made in the recent decade in understanding the "computational" phase transition in Gibbs sampling, where a sharp transition in computational tractability coincides precisely with the underlying physical phase transition in many models. I'll give a survey of my research along this line, and also highlight how these developments also lead to new perspectives and applications in differentially private optimizations.

About the speaker

Jingcheng Liu is an Associate Professor in the Theory Group of the School of Computer Science at Nanjing University. He is broadly interested in theoretical computer science, which includes randomized algorithms, computational phase transition, and differential privacy. Before that, he completed undergrad at SJTU (ACM Honors class) and PhD at UC Berkeley, and he was a Wally Baer and Jeri Weiss postdoctoral scholar at Caltech.