PhD Dissertation Proposal Defense

This thesis first establishes and empirically validates a causal model of the relationship between programming languages and energy consumption.

This thesis aims to advance both scalable, pedagogically-grounded AI systems and empirical understanding of how students currently use AI tools.

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

This proposal examines safe decision-making under causal uncertainty.

In my dissertation, I propose to explore the vast landscape of quantum networks by devising analytical models for network performance.

This thesis develops a biologically- grounded machine learning framework that improves generalization and interpretability.

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

Overall, this thesis details how LLMs can support mnemonic generation for vocabulary learning through both sound-based and shape-based associations.

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

In this thesis, we study how LLMs can generate and assess educational content for open-ended learning of 21st century skills in alignment with human judgment.

This thesis studies patient education grounded in digital health records.

This dissertation aims to explore the different learning approaches on three domains.