Larry Pyeatt is an associate professor and researcher with over 25 years in academia and 13 years at the South Dakota School of Mines and Technology focused on probabilistic AI, robotics, and reinforcement learning for partially observable, uncertain, and non-stationary environments. He blends theoretical work on POMDPs and function approximation with practical systems—mobile robot navigation, real-time embedded control, and even machine learning–driven sedation control in surgery. A seasoned educator and graduate advisor, he has a track record of growing and improving graduate programs and mentoring MS/PhD research. His background in embedded control and industry roles informs a pragmatic approach to deploying robust AI in constrained, safety-critical settings.
13 years of coding experience
26 years of employment as a software developer
Master of Science, Computer Science, Master of Science, Computer Science at Texas Tech University
Doctor of Philosophy, Computer Science, Doctor of Philosophy, Computer Science at Colorado State University
It draws pretty pictures of language syntax. Specifically, it is an optimizing compiler to convert (annotated) Extended Backus–Naur Form (EBNF) to railroad diagrams expressed as LaTeX TikZ commands.
Contributions:2 releases, 136 commits, 12 PRs in 11 months
Contributions:1 release, 1 PR, 41 pushes in 2 years 7 months
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