Michael Tierney

Michael Tierney is an associate staff member in the Artificial Intelligence (AI) Technology Group. His work includes developing neural networks for classifying sparse time-series data and applying reinforcement learning methods to study satellite maneuvers in contested space environments. He also focuses heavily on software engineering, developing simulation tools that are robust, well-tested, and scalable using Lincoln Laboratory Supercomputing Center resources. Tierney joined the Laboratory in 2014 and previously worked in both the cyber and space divisions on problems ranging from natural language processing to potentially hazardous asteroid detection.

Tierney received his SB degree in Physics (with a minor in Astronomy) from MIT in 2012 and his MS degree in astronomy from the University of Hawaiʻi at Mānoa in 2014. During his graduate studies, his research focused on observing and tracking asteroids, deriving asteroid shapes from visible-band light curves, and classifying ultracool brown dwarf stars using atmospheric models. He also interned with MIT Lincoln Laboratory's Cyber Systems and Operations group in 2014.