Research

My research sits at the intersection of quantum computing, high-performance systems engineering, and biomedical data infrastructure.

On the quantum side, I develop and evaluate quantum machine learning algorithms for biomedical and clinical applications — including variational quantum algorithms, quantum neural networks, and kernel-based methods with a specific interest in quantum learning advantages in the small-data context. My dissertation work, partially conducted in collaboration with Intel Labs, was among the first systematic explorations of quantum advantages and their relevance to applications in biomedicine. This work in particular was published in the Journal of the Royal Society Interface.

On the systems side, my current focus centers on a cryptographic data watermarking system I architected and built for the AI-READI consortium. To date, the system protects over 1.9 PB of sensitive multimodal biomedical data across 160M+ files. This work sits at the boundary of information security, high-throughput systems design, responsible dissemination of sensitive biomedical data, and ethical data governance.

I also serve as a core developer for FAIRhub.io, an NIH-funded platform for FAIR (Findable, Accessible, Interoperable, Reproducible) biomedical data harmonization and dissemination, developed in partnership with the AI-READI consortium.

For an up-to-date view of my publications, see my Google Scholar.