Ashwin Narayan

Ashwin has spent his career helping a wide array of interdisciplinary practitioners make principled sense of large, unstructured data sets. For his PhD, he worked primarily with biologists, developing dimensionality reduction algorithms to visualize and interpret high-dimensional RNA datasets. Since 2020, he has turned his attention to the advocacy space and has worked as a practitioner on political campaigns and other advocacy groups: building machine learning systems for powering fundraising operations; designing causal inference models for measuring efficacy of their programs; and helping organizations navigate the trade-offs of using the latest generative AI tools.

At Heinz College, he works with students who are similarly excited to sit between quantitative methods and domain experts. He teaches courses on applying AI tools to problems in the public policy space while thinking carefully about governance and risk.

Ashwin received his PhD in applied mathematics at MIT and his BA in mathematics and physics from Williams College.