I study spatially resolved gene regulatory networks and transcriptional programs using spatial multi-omics data. My work connects computational methods for regulatory network inference with the study of tissue organization and human brain development.

I develop AI methods for analyzing biological data and studying human health. My work includes meta-learning for disease diagnosis and prediction from plasma proteomics, and tools for bulk and single-cell omics analysis. I am interested in combining biological knowledge with machine learning to improve interpretability and adaptation to new research questions.

Another focus is the infrastructure that enables AI agents to use biological analysis tools. OmicVerse provides reusable omics methods, while OmicOS connects this infrastructure with an agent system. Across these projects, I explore how analytical capabilities and agent-assisted workflows can make computational research easier to inspect and extend.

My collaborative research uses bioinformatics to investigate biological processes and disease mechanisms. This work connects molecular data analysis with biological questions, including tumor immunosurveillance and cancer immunotherapy in glioblastoma.