Computational Genomics and Pangenomics
IBD detection, PBWT/GBWT/RLBWT indexing, pangenome graph analysis, local ancestry, and million-sample haplotype methods.
Zhi Lab · Yale Biomedical Informatics and Data Science
Computational Genomics and Biomedical AI
We build scalable algorithms and foundation models for population-scale genomics, electronic health records, and biomedical imaging.
Research Objective
Transform high-dimensional biomedical data into reusable infrastructure for precision medicine.
The lab develops statistical, algorithmic, and deep learning methods to connect genetic variation, clinical trajectories, and image-derived phenotypes with disease mechanisms and patient outcomes.
Research Programs
Clinical EHR Foundation Models
Med-BERT, CovRNN, multi-task fine-tuning, clinical trajectory modeling, benchmarking, and deployment-oriented validation across large-scale health systems.
AI-Powered Imaging Genetics
UDIP, image-derived phenotype GWAS, neuroimaging biomarkers, retinal imaging genetics, and multi-omics integration.
News
Latest updates
UDIP-FA accepted at Nature Communications
UDIP-FA extends unsupervised deep imaging phenotypes to diffusion MRI white matter FA maps, identifying 586 loci and 939 lead SNPs.
Dr. Degui Zhi to join Yale BIDS
Dr. Zhi will join Yale University as Professor and Director of Bioinformatics.
Postdoctoral researcher positions open
We are recruiting postdoctoral researchers in computational genomics, clinical AI, and imaging genetics.