I am an AI Researcher at Labelbox, working on model evaluation and AI safety.

I earned my PhD in Computer Science (AI/​ML) from the University of Arizona. My research interests center on Large Language Models (LLMs), focusing on designing datasets/​tasks that adversarially stress-test alignment. I am particularly interested in surfacing systematic misalignment and reasoning failure modes across frontier AI models.

My PhD dissertation is the first to systematically identify data contamination (data leakage) in LLMs, scenarios where training data overlaps with evaluation data. I developed several methods to detect and estimate contamination in fully black-box LLMs.

Previously, I was a research intern at Google Cloud AI Research, Walmart Global Tech, and Harvard Medical School.