- Ph.D., University of California at Berkeley
- B.S., University of California at Berkeley
Professor Angluin is interested in machine learning and computational learning theory. Algorithmic modeling and analysis of learning tasks gives insight into the phenomena of learning, and suggests avenues for the creation of tools to help people learn, and for the design of "smarter" software and artificial agents that flexibly adapt their behavior. Professor Angluin's thesis was among the first work to apply complexity theory to the field of inductive inference. Her work on learning from positive data reversed a previous dismissal of that topic, and established a flourishing line of research. Her work on learning with queries established the models and the foundational results for learning with membership queries. Recently, her work has focused on the areas of coping with errors in the answers to queries, map-learning by mobile robots, and fundamental questions in modeling the interaction of a teacher and a learner.
Professor Angluin helped found the Computational Learning Theory conference, and has served on program committees for COLT and on the COLT Steering committee. She served as an area editor for Information and Computation from 1989-1992. She organized the Computer Science Department's Perlis Symposium in April 2001: "From Statistics to Chat: Trends in Machine Learning." She is a member of the Association for Computing Machinery and the Association for Women in Mathematics.
- "Queries revisited," Proc. ALT 2001, 12-31,2001.
- "Robot navigation with distance queries," with J. Westbrook and W. Zhu, SIAM Computing, 30:110-144, 2000.
- "Learning regular sets from queries and counterexamples," Information and Computation, 75:87-106, 1989.
- "Finding patterns common to a set of strings," Journal of Computer and System Sciences, 21:46-62, 1980.