PhD Thesis Defense

This thesis focuses on addressing the challenges of sleep monitoring at the community level by developing scalable, personalizable, and robust sleep detection..

I develop a Gumbel random process-based approach that improves the optimization  landscape, resulting in a more stable and expressive variant of Box Embedding.

This thesis develops scalable, accurate tools and benchmarks for factuality assessment that set higher standards for AI evaluation.

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

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

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

This thesis focuses on the development and application of adaptive machine-learning models.

In this work we produce verified code for a Seldonian algorithm validation test.

This thesis addresses these challenges through a series of innovations in multi-task learning.

This research advances methodologies for analyzing textual content from large-scale online datasets, interpreting message intent, retrieving  knowledge...

This thesis proposes an alternative path toward intelligent control by  drawing inspiration from biological systems and exploring the concept of time-aware...

This proposal addresses three problems in fair ML.