PhD Thesis Defense

The dissertation addresses the fundamental challenge of deploying machine learning models on resource-constrained autonomous systems

This thesis seeks to understand the extent of health inequity captured in EHR data and investigate how ML models can be redesigned.

This dissertation presents five systems that jointly optimize computation and communication across two workload regimes.

In this thesis, the author investigates the capabilities of LLMs to understand student behavior and provide appropriate feedback.

In this thesis, I address this problem by introducing multiple approaches for aligning LLMs with student behavior and knowledge.

This dissertation examines how introductory computer science courses can be designed to better support learner variability

This dissertation develops analytic pipelines and machine learning approaches to estimate ataxia severity.

This thesis investigates the interplay between simulation and causal inference, studying two very different uses of simulators as causal models.

This thesis contributes novel methods for differentially private statistical learning, with a focus on improving the usability of private inference.

This dissertation addresses emerging challenges, proposing innovative approaches to adapt A/B testing methodologies to modern constraints .

This thesis studies online resource allocation across different regimes of uncertainty, including uncertainty in demand, resources, or both.

This dissertation examines three manifestations of the TCAP.