Biography

I’m a Ph.D. candidate in the Genomic Signal Processing Lab at the Department of Electrical and Computer Engineering, Texas A&M University, advised by Prof. Xiaoning Qian. My research bridges Bayesian machine learning, generative models, and multi-modal data integration, with a focus on designing interpretable and scalable AI systems for complex, high-dimensional datasets. While much of my work is grounded in biomedical data (e.g., transcriptomics, proteomics), the core techniques extend broadly to any domain involving noisy, structured, and heterogeneous data.

I’ve developed and published several open-source ML tools and models, including:

Outside academia, I’ve collaborated in fast-paced research environments at Genentech and Mayo Clinic, where I built scalable pipelines for large-scale single-cell and gene-drug relationship analyses. I thrive in cross-functional teams and enjoy translating cutting-edge ML research into real-world tools for discovery, diagnostics, and decision-making.