Andrei Rodin Lab

Research Lab Overview

The Andrei Rodin Lab conducts research in two interconnected areas. The first is computational systems biology, with an emphasis on interpretable artificial intelligence (AI) and machine learning (ML), probabilistic graphical models, information-theoretic methods and causal discovery under explicit modeling assumptions. We develop and apply these methods across diverse biomedical domains, including the molecular dynamics of proteins and protein complexes, spatial transcriptomics and spatial multi-omics, immune cell signaling and immuno-oncology, and multimodal data integration in cancer research, genetic epidemiology and clinical and translational research more broadly.

Our primary methodological focus is probabilistic AI/ML, particularly Bayesian network (BN) structure learning for high-dimensional, multiscale biomedical data. Current research directions include integrating BN structure learning with conventional statistical learning and deep learning (DL) approaches within hybrid AI frameworks and developing computationally scalable methods for modeling nonstationary network dependencies in temporally and spatially indexed data.

Our second longstanding research interest is evolutionary biology, including the origins of the genetic code and translation machinery, the evolution of protein networks, molecular evolution and phylogenetic analysis and the mathematical modeling of epigenetic regulatory mechanisms.

Our research is made possible in part by the generous support of the Dr. Susumu Ohno Endowed Chair in Theoretical Biology and the Susumu Ohno Distinguished Investigator Fellowship, for which we are deeply grateful.