PhD Dissertation Proposal Defense

This dissertation addresses key aspects of TCAP by proposing novel methodologies that enhance value estimation...

This thesis advances fine-grained recognition under limited supervision  through several complementary approaches.

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

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.

Hamza Elhamdadi presents their PhD Dissertation Proposal Defense, "Calibrating Trust in Visualization through the Manipulation of Visual Complexity."

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

This thesis will use topological information captured by bonds to compare crystal networks.

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

While's dissertation work lays the groundwork for this new subfield of visualization research called GerontoVis.

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