Postdoctoral Research Fellow, UTHealth Houston

McWilliams School of Biomedical Informatics, Center for Precision Health
PI: Dr. Zhongming Zhao (BSML)

• Developing computational methods for multi-omics data integration and precision medicine.
• Applying graph neural networks and deep learning to genomic and imaging data analysis.
• Contributing to research in computational biology, cancer genomics, and biomedical informatics.

Internship, Merck

• Developed an AI-based automated PD-L1 CPS scoring model using deep learning for a project closely linked to Merck's KEYTRUDA® (pembrolizumab) anti-PD-1 therapy.
• Collaborated with the clinical team to devise test cases, refining the AI tool's accuracy, and assessed its clinical relevance by benchmarking its performance against pathologist scores using data from clinical studies.
• Utilized Python and PyTorch for the development of the AI tool, focusing on automatic data quality control pipeline, cell segmentation, and predictive modeling.

Lab Technician, MBB Lab & CBG Center, Tulane University

• Managed and maintained the lab's data collection and analysis system, including organizing, cleaning, and analyzing data to support research projects.
• Maintained the lab's website (MBB lab), including creating and posting content, troubleshooting issues, and ensuring that the site is up-to-date and accessible.
• Communicated with collaborators and invited speakers, managed the scheduling of center meetings, and coordinated with members to ensure that all are informed about project progress and deadlines.
• Trained new lab members on laboratory techniques, safety protocols, and software programs.

Research Assistant, Tulane University

• Developed innovative graph deep learning models to analyze fMRI data and predict phenotypes.
• Integrated multimodal data and examined relationships between different modalities to identify crucial biomarkers.
• Implemented cutting-edge machine learning and deep learning algorithms to ensure high precision in the results.
• Collaborated on neuroimaging and brain function studies with TReNDS Center (GSU/Gatech/Emory), DICoN Lab (Boys Town National Research Hospital), and Mind Research Network, supported by NIH and NSF grants totaling over $2 million.

Research Assistant, University of Florida

• Developed a comprehensive data preprocessing pipeline for whole-slide images that includes quality control, segmentation labeling, and data augmentation.
• Designed a rule-based CNN model (Nottingham Histologic Grade) for classifying SCC and ADC breast cancer cells, leveraging TensorFlow and PyTorch frameworks.