Our on-going research concerns the reconstruction and analysis of biological and biomedical networks. We develop information-theoretic methods  and  machine  learning  tools  to infer interpretable causal graphical  models  from large scale genomic  data  (single  cell multi-omic data), live-cell imaging data (tumor-on-chip experiments) as well as medical records of patients. We also have a keen interest in the striking consequences of whole genome duplication in evolution and disease.