Jennifer T. Chayes

University of California, Berkeley


Election Year: 2019
Primary Section: 32, Applied Mathematical Sciences
Secondary Section: 34, Computer and Information Sciences
Membership Type: Member

Biosketch

Jennifer Tour Chayes is an applied mathematician and computer scientist recognized for her contributions to phase transitions, mathematical modeling of networks, and algorithms on networks. She is particularly known for her work on graph limits, a field she co-founded, which is widely studied in graph theory and combinatorics, and is used for machine learning of large-scale networks. Chayes was born in New York City and raised in White Plains, NY. She earned a BA in physics and biology at Wesleyan University, graduating first in her class. Chayes earned her PhD in mathematical physics at Princeton University. She did post-doctoral fellowships in physics and mathematics at Harvard and Cornell. Chayes was Professor of Mathematics at UCLA for 10 years before co-founding the Microsoft Research Theory Group, bringing together math, physics, and computer science research. Chayes went on to co-found and lead three noted interdisciplinary Microsoft Research laboratories in Cambridge, MA, NYC and Montreal, bringing together computer science, math, physics, social sciences, and biological science research. She is a Member of the National Academy of Sciences and the American Academy of Arts and Sciences, as well as a Fellow of the American Association for the Advancement of Science, the American Mathematical Society, and the Association of Computing Machinery. She is the recipient of the 2015 John von Neumann Prize, the highest honor of the Society for Industrial and Applied Mathematics. She is the recipient of many scientific leadership awards. Chayes received an honorary doctorate from Leiden University in 2016.

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 non-parametric machine learning of large-scale networks. More recently, Chayes has studied machine learning broadly defined, including applications of machine learning to biomedicine, algorithmic fairness, privacy, and climate change.

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