Enhancing Learning-Enabled Software Systems to Address Environmental Uncertainty. - Michael Austin Langford; Betty H. C. Cheng (ICAC, 2019)

Applying evolution and novelty search to enhance the resilience of autonomous systems. - Michael Austin Langford; Glen A. Simon; Philip K. McKinley; Betty H. C. Cheng (SEAMS@ICSE, 2019)

AC-ROS: assurance case driven adaptation for the robot operating system. - Betty H. C. Cheng; Robert Jared Clark; Jonathon Emil Fleck; Michael Austin Langford; Philip K. McKinley (MoDELS, 2020)

MoDALAS: Model-Driven Assurance for Learning-Enabled Autonomous Systems. - Michael Austin Langford; Kenneth H. Chan; Jonathon Emil Fleck; Philip K. McKinley; Betty H. C. Cheng (MoDELS, 2021)

"Know What You Know": Predicting Behavior for Learning-Enabled Systems When Facing Uncertainty. - Michael Austin Langford; Betty H. C. Cheng (SEAMS@ICSE, 2021)

Enki: A Diversity-driven Approach to Test and Train Robust Learning-enabled Systems. - Michael Austin Langford; Betty H. C. Cheng (ACM Trans. Auton. Adapt. Syst., 2021)

A Modular and Composable Approach to Develop Trusted Artificial Intelligence. - Michael Austin Langford; Betty H. C. Cheng (ACSOS, 2022)

MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty. - Michael Austin Langford; Kenneth H. Chan; Jonathon Emil Fleck; Philip K. McKinley; Betty H. C. Cheng (Softw. Syst. Model., 2023)

Anunnaki: A Modular Framework for Developing Trusted Artificial Intelligence. - Michael Austin Langford; Sol Zilberman; Betty H. C. Cheng (ACM Trans. Auton. Adapt. Syst., 2024)