Leonard Pitt is Professor of Computer Science and University Distinguished Teacher/Scholar at the University of Illinois. He received his Ph.D. from Yale University in 1985, and joined the University of Illinois faculty in 1986 after a postdoctoral fellowship at Harvard University. His early research included foundational contributions to Computational Learning Theory, with focus on learning of patterns from examples and queries, and understanding the role that discrete representations play in determining the computational complexity of learning. He co-founded the first workshop on Computational Learning Theory, has served on numerous program committees in learning sciences including Computational Learning Theory, Grammatical Inference, and Algorithmic Learning Theory. His interests have also spanned across the areas of approximation algorithms, computational complexity, and applying learning techniques in the context of data query optimization. Professor Pitt has received awards at the department, college, and university levels for his excellence as an educator. He has taught and helped design all of the department's discrete foundational "theory" course (discrete math, theory of computation, algorithms) as well as courses in machine learning theory and data mining. For the last dozen years he has engaged in a broad spectrum of educational initiatives, specifically focused on discrete mathematics and the Data Sciences, and spanning levels from K-16, including courses for middle school students, state-funded teacher professional development workshops, summer camps for students in grades 9-12, Computer Science courses for non-majors. He is a key contributor to the forthcoming ACM K-12 Level II curricular standards, and is presently a driving force behind a campus-wide initiative to create a cross-disciplinary informatics minor. Professor Pitt is particularly interested in the success of the summer institute, and in knowledge and technology transfer to the K-12 level.