PhD Thesis Defense

This dissertation develops a comprehensive framework for high-fidelity motion characterization, addressing challenges of sensitivity, robustness, and privacy.

This dissertation advances the design of practical privacy-preserving machine learning systems.

This thesis develops control frameworks for legged robots that bridge model-based trajectory optimization and data-driven learning

 This thesis examines three complementary roles of motion in computer vision.

This thesis develops and analyzes compact formulations for decision-making problems characterized by large action and large state sets.

This dissertation advances conversational AI by showing how multi-agent chatbots, thoughtful avatar design, and personalized interactions enhance engagement...

The central task developed in this work is to learn a global probabilistic model, often a graphical model, that is consistent with these observed marginals.

In this thesis, we revisit the task of data selection through the lens of social-oriented metrics like diversity and fairness.

In this thesis I present computational methods for transforming large-scale, unstructured data into rich semantic structures to support human decision-making.

In this thesis, we identify and rectify some limitations of such cryptographic constructions and their proofs of security.

This dissertation studies the fundamental limits of covert activity across communication, network attack, and sequential detection settings.

This thesis addresses challenges of dataset scarcity and energy efficiency.