Aditi Raghunathan works broadly in machine learning, and her goal is to make machine learning more reliable and robust. Raghunathan's work spans both theory and practice, and leverages tools and concepts from statistics, convex optimization, and algorithms to improve the robustness of modern systems based on deep learning.
Until recently, Raghunathan was a postdoc at Berkeley AI Research. She received her Ph.D. from Stanford University in 2021 where she was advised by Percy Liang. Her thesis won the Arthur Samuel Best Thesis Award at Stanford. Previously, she obtained her BTech in Computer Science from IIT Madras in 2016.