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.<a href="https://www.sciencefriday.com/segments/jennifer-chayes/?utm_source=wnyc&utm_medium=podcast&utm_campaign=scifri" target="_blank" rel="noopener noreferrer">Jennifer Chayes helped create that field of math.</a> 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.&nbsp;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 <a href="https://www.sciencefriday.com/segments/jennifer-chayes/?utm_source=wnyc&utm_medium=podcast&utm_campaign=scifri" target="_blank" rel="noopener noreferrer">sciencefriday.com.</a>
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