For Jennifer Chayes, math is a 'native tongue'

September 4, 2026

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5 min read

For Jennifer Chayes, math is not a set of symbols on a page or a tool for calculation. It is a native tongue—a way of making sense of the world that she recognized before she could read. As a four-year-old who could not yet add fractions, she overheard neighbors talking about math and felt she had found a language she had been missing her whole life. That sense of math as a mother tongue has guided her through a career that spans mathematical physics, computer science, and the theoretical foundations of modern AI. She co-founded the field of graphons—a way of understanding the collective behavior of massive networks—and spent two decades at Microsoft building interdisciplinary research labs. Now, as dean of the College of Computing, Data Science and Society at UC Berkeley, she is focused on using AI to accelerate science and on teaching people how to be partners to AI rather than passive users.

Phase transitions in networks and knowledge

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What you'll learn

1 (02:50) **Math as a Mother Tongue & Sense-Making** - Chayes describes how she uses math to make sense of the world, not as a visual overlay.
2 (05:08) **The Core Idea: Networks & Phase Transitions** - Chayes explains the central concept that connects her work.
3 (07:09) **Explaining Phase Transitions in Networks** - Chayes provides a detailed mechanism for how phase transitions work in information networks.
4 (09:16) **The Problem Graphons Solve: Seeing the Forest** - Chayes defines the foundational mathematical concept of graphons.
5 (11:38) **Inventing Graphons: The Limit of a Network** - Chayes details the creation of graphons as a meaningful mathematical limit.
6 (15:24) **Warning Signs: A Stormy Childhood** - Chayes shares her personal background and the challenges she faced.
7 (17:38) **Recovery & Resilience: The Dropout School** - Chayes describes the turning point that led her back to education.

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Show Notes

Much of the code that describes the inner workings of social networks, helps apps predict your musical interests, and underlies innovations in AI traces back to advances in mathematical theory and data science that fundamentally describe how massive networks behave.

Jennifer Chayes helped create that field of math. She also spent 20 years at Microsoft building interdisciplinary research labs. Now, at UC Berkeley, her interests lie in using machine learning to work on topics like materials science, cancer immunotherapy, ethical decision-making, and climate change. She joins Flora for a wide-ranging discussion about math, networks, AI, academia, and her path between them.

Guest:

Dr. Jennifer Chayes is the dean of the College of Computing, Data Science, and Society at UC Berkeley.

Transcript will be available after the show airs on sciencefriday.com.

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