About me

I am a Machine Learning Engineer at Person Health, where I work at the intersection of artificial intelligence and oncology. My current focus is developing machine learning systems that detect bio-molecular signatures of early-stage cancers in patient blood samples, aiming to improve survival outcomes through proactive, non-invasive diagnostics.

I hold a PhD in Electrical Engineering with a minor in Computer Science from the University of Minnesota - Twin Cities. My doctoral research was conducted under the supervision of Professor Vladimir Cherkassky and Professor Tony Low, and I also gained additional industrial experience through research internships at TSMC and Astrin Biosciences.

My research interests lie at bridging the artificial intelligence and the physical sciences, where I focus on advancing the foundations of machine learning to address complex challenges in scientific discovery and engineering design. Key topics I have worked on include:

• Detection of subtle cancer signatures in patient blood samples using infrared spectroscopy and advanced ML methods.
• Detection of circulating tumor cells from holographic microscopy images of blood samples using advanced ML methods.
• Acceleration of vdW heterostructure materials discovery through the integration of materials simulations, first-principles, and ML methods.
• Analysis of the double descent phenomenon and generalization capabilities of large over-parameterized ML models using Vapnik-Chervonenkis (VC) theory.
• Throughput enhancement by optimizing the multilayer mirrors in the Extreme Ultraviolet (EUV) lithography systems.