Research

My current research centers on quantum computing methods, in particular the development of quantum machine learning algorithms for applications in biomedical and clinical research. This includes the development, validation, and application of quantum learning advantages, with the goal of extending the reach of quantum AI into cancer and rare diseases and, more broadly, enabling transformative outcomes in healthcare.

In addition to my quantum computing work, I’m also an investigator for AI-READI, a flagship data generation project of Bridge2AI, an NIH Common Fund program and, in addition, serve as a core developer for Fairhub.io, a FAIR (Findable, Accessible, Interoperable, Reproducible) biomedical data-sharing platform.

A central focus of my work with AI-READI has been the design and development of its data watermarking system — a suite of high throughput cryptographic methods that support the secure, ethical, and traceable use of sensitive biomedical data. This system, together with a novel AI data sharing license, has played a critical role in enabling the responsible dissemination of the AI-READI’s dataset, which is expected to reach over 8TB on completion. These tools are foundational to building socially-aligned AI models for diabetes research and beyond.

For an up-to-date look at my research, checkout my Google Scholar.