Machine Learning (ML), Partially Observable Markov Decision Processes (POMDP), Neural Networks (NN), Spiking Neural Networks (SNN). Optimal Decision Making Under Uncertainty (ODMUU), Configurable Digital Logic (CDL), Field Programmable Gate Arrays (FPGA), Computer Architecture (CA), applications of all of the above to robotics.
Dr. Pyeatt earned his BS and MS degrees at Texas Tech University, and then spent some time working in industry. He earned his doctorate in Computer Science, focusing on Artificial Intelligence, from Colorado State University in 1999. He spend 13 years as a professor at Texas Tech University before moving to the South Dakota School of Mines an Technology in 2012. Dr. Pyeatt has published many scientific papers in the field of artificial intelligence, and has published three textbooks on Assembly Language programming.
Dr. Pyeatt has taught all of the introductory and sophomore courses in Computer Science and Computer Engineering. He regularly teaches the Sophomore, Junior, and Senior level courses in Computer Engineering, and occasionally teaches graduate level requested courses in Artificial intelligence, Computer Science, and Computer Engineering.