Research Interests

  • Methods: developing mathematical, statistical, and machine learning methods to model the spatio-temporal patterns in single-cell and spatial genomics and epigenomics data, Bayesian statistics, functional data analysis, and deep neural networks.

  • Theoretical models: gene regulatory networks (Boolean networks) controllability and inference, probability, and optimization.

  • Collaboration and scientific research: glioblastoma, non-small cell lung cancer, head and neck squamous cell carcinoma, pancreatic cancer, immunology, developmental processes, infectious disease, obesity, and maternal and child health.

Grants

NIH Pathway to Independence Award (1K99HG011468), NIH/NHGRI, 3/2021 - 3/2026

Title: Computational Methods for Inferring Single-cell DNA Methylation and its Spatial Landscape

Role: principal investigator