Abstract
The use of machine learning components in safety-critical systems creates reliability concerns. My thesis focuses on developing algorithms to address these concerns. Because the assurance of a safety-critical system generally requires multiple types of validation, my research takes three directions: safe deep learning algorithms, formal verification of neural networks, and adaptive testing methods.
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