Christina Selby is a senior professional staff member and section supervisor at JHU/APL, with expertise in developing and analyzing mathematical methodologies to solve critical problems that are not well understood. Much of her current work focuses on the development and application of uncertainty estimation algorithms in the areas of computer vision and deep reinforcement learning.
Selby was a senior professional staff member of JHU/APL from 2006–2012, where she worked primarily on calibration, validation, and analysis tasks for space science applications. After that, Selby was a member of the mathematics faculty at Rose-Hulman Institute of Technology. She also served as the chair of the Mathematical Association of America’s Mathematics in Business, Industry, and Government committee.
Education & Industry Experience
Selby received her MS and PhD in mathematics from Purdue University, where she focused on partial differential equations and differential geometry.