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Research
Angluin's work helped establish the theoretical foundations of Machine Learning.

L* Algorithm
Angluin has written highly cited papers on computational learning theory, particularly in the context of learning regular language sets from membership and equivalence queries using the L* algorithm. This algorithm addresses the problem of identifying an unknown set. In essence, this algorithm is a way for programs to learn complex systems through the process of trial and error of educated guesses, to determine the behavior of the system. Through the responses, the algorithm can continue to refine its understanding of the system. This algorithm uses a minimally adequate Teacher (MAT) to pose questions about the unknown set. The MAT provides yes or no answers to membership queries, saying whether an input is a member of the unknown set, and equivalence queries, saying whether a description of the set is accurate or not. The Learner uses responses from the Teacher to refine its understanding of the set S in polynomial time. Though Angluin's paper was published in 1987, a 2017 article by computer science Professor Frits Vaandrager says "the most efficient learning algorithms that are being used today all follow Angluin's approach of a minimally adequate teacher".

Learning from Noisy Examples
Angluin's work on learning from noisy examples has also been very influential to the field of machine learning. Her work addresses the problem of adapting learning algorithms to cope with incorrect training examples (noisy data). Angluin's study demonstrates that algorithms exist for learning in the presence of errors in the data.

Other Achievements
Angluin is highly celebrated as an educator, having won "three of the most distinguished teaching prizes Yale College has to offer": the Dylan Hixon Prize for Teaching Excellence in the Sciences, The Bryne/Sewall Prize for distinguished undergraduate teaching, and the Phi Beta Kappa DeVane Medal.