Big data are now ubiquitous in genomics and biology, offering unprecedented opportunities from personalized medicine to building the tree of life. We excel in integrating mathematical and computational techniques into evolutionary biology and biomedicine. We develop mathematical methods, computational algorithms, software packages, and databases for analyzing contemporary and ancient mutations that are the causes of species divergence, genetic diseases, viral spreading, and tumor progression.

We pursue a holistic paradigm in which the first step is focused on discovering evolutionary and genomic patterns through the comparative analysis of big datasets of variation across populations, pathogens, tumors, or species. We use patterns discovered to reveal underlying biological processes and evolutionary knowledge to develop predictive models that translate fundamental knowledge into actionable information. Ultimately, we make our innovations widely accessible by developing industrial-strength and user-friendly products such as software and databases.

We have developed new computational methods and algorithms for scalable, efficient, and practical analysis of big data. Highlights include Bayesian methods, machine learning algorithms, and statistical approaches for inferring molecular phylogenies, divergence times, ancestral sequences, evolutionary distances, pathogenic mutations, tumor clones, and adaptive lineages. We have championed the application of molecular evolution to the growing fields of phylomedicine and cancer biology that tackle diseases via phylogenetic methods and make predictions informed by evolutionary biology. Our recent green computing efforts are aimed at democratizing the practice of science and making big data analytics more widely accessible and rigorous.

In addition to several significant discoveries through the analysis of empirical data sets, we have developed high-impact tools widely used by scientists, students, and the general public. Highlights include the MEGA software (www.megasoftware.net) for comparative analysis of molecular sequences from an evolutionary perspective and the TimeTree knowledge-base (www.timetree.org) that synthesizes and presents evolutionary knowledge on species divergence time published in the scientific literature.

Dr. Kumar's laboratory webpage