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

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

This proposal addresses three problems in fair ML.

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.