PhD Dissertation Proposal Defense

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

This dissertation presents a new foundation for flexible and secure replication of blackbox stateful services.

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

This dissertation addresses how computer science instructors can redesign course structures and pedagogy in response to the widespread availability of LLMs.

In the first part of my thesis, I introduce split parallelism, a novel  abstraction that addresses the limitations of traditional data parallelism on GPUs.

This dissertation proposes integrating transactional memory directly into a WebAssembly runtime system for persistent memory programming.

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

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

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

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

In this thesis, Pruthi will propose a compositional framework for causal reasoning in modular, heterogeneous systems.

This thesis addresses IMU data scarcity and energy constraints.