My work focuses on developing novel deep learning and statistical methods for analyzing large-scale multi-omics data, including but not limited to spatial omics, single-cell genomics, epigenomics and transcriptomics data with particular focus on Alzheimer’s disease and cancer studies, such as glioblastoma and lung cancer.
The exciting part about this is that with the developed computational methods models, we can discover novel biomarkers that can potentially be treatment targets for diseases. I am particularly intrigued by integrating histology image data and spatial omics data to improve the disease prognosis.
I was able to publish a high-impact journal paper and secure National Institutes of Health funding from multiple institutes by collaborating with UF colleagues. One example is my collaboration with Dr. Ramon Sun in the UF College of Medicine. We published a paper in Nature Metabolism, which is about developing an AI-driven computational approaches to align spatial omics data across brain slice tissues for reconstructing the whole mouse brain.
Currently, I am developing novel AI methods for spatial omics analysis in a large cohort of patients with glioblastoma and lung cancer. If effective, the pilot data from these studies can be used to produce high-impact journal papers and grant proposals for funding from the National Cancer Institute and the American Cancer Society.