Los Angeles, California, United States
Researching applications of spatial transcriptomics and deep learning in neuroimaging (MRI) for biomarker discovery and clinical translation. Biomedical Imaging Group in the Signal and Image Processing Institute under the advisement of Dr. Richard Leahy.
• Developed computational frameworks linking cortical gene expression with MRI-derived measures of gray-white contrast to uncover cellular and molecular drivers of Alzheimer’s disease vulnerability.
• Conducted large-scale spatial transcriptomic analyses across multi-cohort datasets to map cellular determinants of cortical organization and validate regional gene-imaging associations.
• Designed and implemented deep learning pipelines for Alzheimer’s disease classification using structural MRI, benchmarking performance against traditional machine learning models and evaluating interpretability for clinical utility.
• Created reproducible analysis workflows integrating multimodal data (genomics, imaging, demographics, clinical features) with rigorous statistical methods for validation.
• Applied advanced saliency methods (IG, SHAP, Grad-CAM) to deep learning models for MRI-based prediction of Alzheimer’s disease and traumatic brain injury outcomes, achieving anatomically interpretable and clinically trustworthy result.
• Conducted complementary anlayses on gray-white contrast, PET imaging, and genetic risk factors (APOE, diabetes) to broaden biomarker discovery pipelines and strengthen translational relevance across Alzheimer’s disease and TBI.
