Dana Angluin received her B.A. in Mathematics (1969) and her Ph. D. in Engineering Science (1976) under the direction of Manuel Blum, both from U.C. Berkeley. Her dissertation applied the then-new theory of computational complexity to problems in learning finite automata and regular expressions. During postdocs with Leslie Valiant (Leeds and Edinburgh Universities) and Ronald Book (U.C. Santa Barbara), she developed new techniques for the analysis of randomized algorithms and learning formal languages from positive examples. From 1979 to 2021 she was a member of the Yale Computer Science Department in a number of roles: Gibbs Instructor, Assistant Professor, Associate Professor on Term, Research Scientist, Senior Research Scientist, and Professor. At Yale, her main focus was algorithmic approaches to learning, including query models, errors and omissions, robot localization and map learning, learning languages of infinite words, and forays into learning with artificial neural networks. She helped start the influential Computational Learning Theory (COLT) conference. A research side trip with Yale Computer Science colleagues won the 2020 Dijkstra Prize in Distributed Computing. Her teaching of Yale undergraduates was recognized by the award of the Dylan Hixon ’88 Prize for Teaching Excellence in the Natural Sciences (2011), the Harwood F. Byrnes/Richard B. Sewall Teaching Prize (2020), and the Yale Phi Beta Kappa chapter’s William C. Clyde DeVane Medal (2020).