Name 
Chayes, Jennifer T. 
Location

University of California, Berkeley 
Primary Field

Applied Mathematical Sciences 
Secondary Field

Computer and Information Sciences 
Election Citation

Chayes cofounded the field of graph limits and has made critical contributions to statistical physics, network science, and machine learning.

Research Interests

Chayes' early work focused on the mathematics of phase transitions, both in physical systems like spin glasses, and in combinatorics and computer science. Some of Chayes' later work uses statistical physics approaches to explain the effectiveness of deep learning. Chayes is best known for her work on network science, from mathematical modeling of networks, to algorithms on networks, to machine learning of networks, and finally to applications of network models and algorithms to economic, social, and biological processes. Much of Chayes' work concerns graph limits, a field she cofounded; these are continuum limits of graphs or networks, similar to thermodynamics as a limit of statistical physics, or differential equations as a limit of interacting particle systems. Graph limits are now widely used for nonparametric machine learning of largescale networks. More recently, Chayes has studied machine learning broadly defined, including applications of machine learning to biomedicine, algorithmic fairness, privacy, and climate change.



