PhD in Machine Learning researching code intelligence, program semantics, and the alignment and interpretability of large language models for code. My work studies how large language models represent, reason about, and interact with code, with a focus on code understanding, instruction and model alignment, interpretability, efficient adaptation, and the verification and evaluation of model capabilities in software engineering settings. I am interested in improving these capabilities through post-training and alignment methods, while developing approaches to better understand, verify, and rigorously evaluate model reasoning and correctness. More broadly, my research lies at the intersection of machine learning and AI for Software Engineering (AI4SE), with the goal of building coding models whose capabilities are strong, interpretable, reliable, and verifiable.

I earned a Master of Science in Computer Engineering, specializing in Artificial Intelligence and Machine Learning, from Northeastern University in 2025, followed by a Graduate Certificate in Applied Mathematics in 2026. Prior to that, I completed a Bachelor of Science in Software Engineering at Iowa State University in 2022.