Graduation Semester and Year
Spring 2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
Allison Sullivan
Second Advisor
Jacob Luber
Abstract
This dissertation combines insights across text, code, and image modalities to advance the robustness, efficiency, and adaptability of machine learning models. Specifically, we address challenges like adversarial vulnerability in text, the impact of test strategies on code generation, and dimensionality in image representation in unsupervised learning domain. These efforts highlight pipelines for designing machine learning systems that are not only efficient, but also adaptable to complex environments. In addition, these efforts together help form the basis for a multimodal AI capable of thriving in medical applications that this dissertation prototypes for future efforts.
Keywords
Machine Learning, Multimodal LLM, TDD, Adversarial Example, Unsupervised Learning, Medical Image Processing, NLP
Disciplines
Computer Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Samadi, Anahita, "Advancing Machine Learning Approaches Through Robust Methodologies in LLM Code Generation, Adversarial Text Classification, and Unsupervised Learning" (2025). Computer Science and Engineering Dissertations. 405.
https://mavmatrix.uta.edu/cse_dissertations/405