PhD Dissertation Proposal Defense

This dissertation explores machine learning techniques for molecular property prediction, with a focus on modeling and understanding mycomembrane permeability.

This thesis addresses both specification and scalability challenges in agentic system alignment.

This thesis studies patient education grounded in digital health records.

 This thesis addresses the need for better understanding of non-ME user-generated data.

This thesis examines such language-specific considerations through case studies on Latin.

This thesis presents a comprehensive framework for developing an autonomous quadruped guide dog robot while aligning with the practical needs of BLV users.

In this thesis, I address this challenge across several computer vision problems by developing methods that adapt standard models to the open world.

This proposal provides a unified framework for developing more accurate, scalable, and privacy-aware personalized LLMs.

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

This dissertation evaluates curriculum aware adaptive tutoring systems and computational learning platforms.

This thesis investigates motion planning and learning based control methods with the goal of developing robust controllers for dynamic legged robots.

This thesis develops datasets, methods and theory to support the peer review process in both the pre- and post-LLM paradigms.