Developing novel graph-based deep learning frameworks for analyzing brain functional connectivity from fMRI data. This includes the Gated Graph Transformer, which implements prior spatial knowledge and random-walk diffusion strategies to simultaneously capture complex structural and functional relationships between brain regions. By applying attention mechanisms for learning multi-view node feature embeddings and dynamically assigning propagation weights, these methods enable identification of significant functional brain network biomarkers with enhanced interpretability.

Key publications:

• Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks — Medical Image Analysis, 2025
• Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks — IEEE Trans. Medical Imaging, 2024
• Brain Functional Connectivity Analysis via Graphical Deep Learning — IEEE Trans. Biomedical Engineering, 2022
• Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction — IEEE Trans. Biomedical Engineering, 2021

Developing computational methods for integrating multi-omics data (genomics, transcriptomics, epigenomics) to understand disease mechanisms and advance precision medicine. This includes graph-based approaches for modeling complex biological networks and identifying disease-associated molecular signatures.

Key publications:

• BrainGeneBot: A Framework for Variant Prioritization and Generative Pretrained Transformer-Informed Interpretation Across Polygenic Risk Score Studies — Briefings in Bioinformatics, 2025

Current focus at UTHealth Houston (BSML, PI: Dr. Zhongming Zhao):

• Multi-omics data integration for cancer genomics
• Graph neural networks for biological network analysis
• Computational approaches for biomarker discovery

Integrating multimodal fMRI data with genomic information to investigate associations among different data paradigms and identify key biomarkers that bridge brain function and genetic variation. This work leverages manifold learning and multi-modal graph convolutional networks to produce highly accurate and interpretable results for neuroscience research.

Key publications:

• Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction — IEEE Trans. Biomedical Engineering, 2021

Collaborations: TReNDS Center (GSU/Gatech/Emory), DICoN Lab (Boys Town National Research Hospital), Mind Research Network

Developing AI-driven solutions for clinical applications including automated digital pathology analysis. This includes deep learning systems for whole-slide image analysis, automated scoring systems (PD-L1 CPS), and histopathology image classification using semi-supervised learning approaches.

Key projects:

• AI-based automated PD-L1 CPS scoring (Merck internship, 2022) — Linked to KEYTRUDA® anti-PD-1 therapy, achieving AUROC > 0.98
• Graph Temporal Ensembling Based Semi-supervised CNN for Histopathology — Medical Image Analysis, 2020 (University of Florida)