PhD Thesis Defense

In this thesis, we study methods and tools for learning from uncertain and incomplete longitudinal data.

In this thesis, I present privacy-aware audio sensing systems for monitoring environmental and human health.

This thesis investigates how LLMs can assist, transform, and extend human work across three specific tasks: translation, editing, and literary interpretation.

This thesis explores automatic marginalization at different abstraction levels of probabilistic programming.

This dissertation categorizes skills that fall under computational thinking and describes our work towards improving student learning and engagement.

This thesis develops and analyzes algorithms for several key problems related to eigenvalue and eigenvector estimation.

This dissertation argues for a different approach to sorting and filtering social media feeds.

This research advances methodologies for analyzing content from online datasets, interpreting intent, retrieving knowledge, and leveraging these insights.

In this defense, I outline a program to move from post-hoc explanations toward a principled science of reverse-engineering LLM internals.

This dissertation investigates whether programmers can improve the energy efficiency of their software, and methods to achieve faster and greener programs.

This research investigates the design and implementation of ultrasonic sensing systems that allow everyday devices to perceive and interpret signals beyond the

This dissertation investigates how learned priors and neural computation can enable resource-efficient video streaming throughout the process.