Name Dana Angluin | Fields Computer Science | |
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Known for L* AlgorithmQuery learningExact learning Education | ||
Dana Angluin is a professor of computer science at Yale University. She contributed to the foundations of computational learning theory.
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Education
Angluin received her B.S. and Ph.D at University of California, Berkeley. Her thesis, entitled "An application of the theory of computational complexity to the study of inductive inference" was one of the first works to apply complexity theory to the field of inductive inference.
Career
Anluin joined the faculty at Yale in 1979.
Dana Angluin has authored many papers and been a pioneer in many fields specifically learning regular sets from queries and counterexamples, robot navigation with distance queries, self-stabilizing universal algorithms and query learning of regular tree languages. A lot of Angluin’s work involves queries, a field in which she has made many great contributions. Angluin also has worked in the field of robotics as well dealing with navigation with distance queries.
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 computational 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 (COLT) 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 Yale's 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.
Angluin has also published works on Ada Lovelace and her involvement with the Analytical Engine. Angluin is highly regarded as one of the best women in her field of Computer Science. Angluin continues to make more progress in her chosen field of queries at Yale.
Work
Representative Publications: