Thomas G. Dietterich is one of the founders of the field of Machine Learning. Among his research contributions was the application of error-correcting output coding to multiclass classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models (including conditional random fields and latent variable models). He served as Executive Editor of Machine Learning (1992-98), helped co-found the Journal of Machine Learning Research, and is currently the editor of the MIT Press series on Adaptive Computation and Machine Learning. He is also a fellow of the ACM, AAAI, and AAAS.