PhD Dissertation Proposal Defense

This thesis investigates the interplay between simulation and causal inference, focusing on how causal modeling can enhance simulation and vice versa.

This dissertation investigates how NLP methods can enable scalable educational assessment and AI systems across key components of the educational pipeline.

This thesis addresses these limitations by developing a comprehensive framework for the automated assessment of open-ended mathematical responses. 

This dissertation studies the algorithmic construction of threshold-logic circuits for Boolean functions.

In this dissertation, I explore how lightweight dynamic analysis can be used to improve the reliability of Python software.

This thesis investigates how to design high-throughput, cost-efficient inference serving systems that adapt to time-varying workloads.

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

This thesis proposes a series of methodologies designed to overcome data scarcity.

This thesis addresses IMU data scarcity and energy constraints.

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

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.