Natural language processing combines the power of artificial intelligence with linguistics to process and analyze language-based data. NLP considers the building blocks of language as data and analyzes these data segments, looking for latent structures and patterns in language.
Bradley Beth has 20 years' experience in computer science and education. Prior to joining EDUCAUSE, he worked for The University of Texas at Austin, securing $1.5 million in grants from the National Science Foundation to develop a secondary computer science curriculum and professional learning program now being implemented in hundreds of schools across the U.S. During his time at UT Austin, he also assisted institutions of higher education to develop teacher preparation pathways for computer science and advised states developing K–12 computer science standards, teacher preparation pathways, and legislative policies.
Prior to his work at the university level, he worked in the legal software industry as a programmer before earning teaching licensure in computer science and mathematics. As a middle and high school teacher, he received several national and state awards, including being named the teacher with the largest number of Latino students in the U.S. passing the AP Computer Science exam in 2007.
Bradley completed his B.A. in Linguistics and B.S. and M.S. degrees in Computer Science, all at The University of Texas at Austin. While a graduate student, his research interests centered on Machine Learning—particularly as applied to linguistic and education data, focusing on latent feature analysis.
He enjoys spending time with his wife and four children on their homestead in a remote mountain region of northeastern Vermont.