I am a Ph.D. student in Industrial Engineering and Management Systems at the University of Central Florida, working at the intersection of machine learning, biomedical data science, computational biology, and healthcare AI.

My research is driven by a central question: how can we build machine learning methods that remain reliable when labels are weak, noisy, incomplete, or collected under changing conditions? This question is especially important in biomedical and healthcare settings, where data often shifts across populations, laboratories, timepoints, technologies, and clinical contexts.

My current work focuses on reliable machine learning for biomedical and transcriptomic data, including weak supervision, distribution shift, foundation models for gene regulation, single-cell transcriptomics, and decision-support methods for high-stakes prediction. I am particularly interested in methods that perform reliably beyond controlled benchmarks and remain interpretable and useful in real-world settings.

Before joining UCF, I completed my M.S. in Information Technology at Worcester Polytechnic Institute, where I worked on AI-assisted healthcare applications, systematic reviews, and chronic wound image annotation. I also have a background in industrial engineering, optimization, project control, and operations research, which shapes how I think about reliability, evaluation, and decision-making in applied AI systems.