Our on-going research concerns the reconstruction and analysis of biological and clinical 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.

			More reliable and interpretable causal discovery methods
			Information maximization in mixed-type data from medical records
			Robust reconstruction of causal, non-causal or mixed networks         MIIC server
			Membrane reshaping by Septin filaments
			Genome duplication in tumor resistance
			Ancient genome duplications and dominant genetic diseases
			Identification of ohnologs from whole genome duplication         Ohnologs server
			Evolution of large biomolecular networks
			Self-assembly of bacterial RNA
	Older projects (before 2009)
			Synthetic RNA / DNA switches and regulatory networks
			RNA folding including pseudoknots and "real knots"         Kinefold server   (>160,000 online simulations to date!)
			RNA mechanical unfolding simulations
			Membrane electro-undulation and electroporation
			Electrohydrodynamic instabilities
			Actin dynamics and mechanics