PhD Thesis Defense

This thesis studies questions from three complementary perspectives for real-world scenarios.

This dissertation develops artificial intelligence (AI) and clinical natural language processing (NLP) methods to transform these longitudinal narratives.

This dissertation argues that how people perceive and interpret bias in AI outputs is shaped by how the outputs are presented and who is interpreting them.

This dissertation investigates how NLP methods can enable scalable educational assessment and AI systems across key components of the educational pipeline.

This thesis significantly generalizes the scope of metareasoning.

This thesis develops and analyzes principled, efficient, and scalable algorithms for answering marginal queries.

This dissertation addresses challenges to encrypted databases through the following contributions: obliviousness and query expressiveness.

In this thesis, I focus on three connected challenges concerning information integrity.

This thesis advances fine-grained reasoning under limited human supervision through several complementary approaches.

This dissertation addresses potential failure points in artificial agents.

This dissertation examines techniques for learning structure and discusses how the resulting background knowledge is used in decision making under uncertainty.

This dissertation studies algorithmic methods for constructing and optimizing threshold-logic circuits.