Statistical Machine Learning for Biomedical Data

We develop computational tools for the analysis of genomic data across spatio-temporal scales, with the goal of making sense of morphology and development. Our focus is understanding cell fate decision making, collective behavior and self-organization in biological systems through experimental design, multi-modal data analysis, and interpretable machine learning.