PhD Dissertation Proposal Defense

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

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

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

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

In this thesis, we first focus on discrimination in supervised learning.

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

Cloud computing and rapid data growth have driven many organizations to outsource large datasets to cloud databases in order to reduce management costs.

This thesis studies patient education grounded in digital health records.

This thesis proposes resource management techniques that mitigate performance degradation caused by such real-world dynamics to ensure robust performance. 

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

In this thesis, we show that changing current adoption trends could increase CO2 reduction by 40%, using multi-objective evolutionary learning.

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