Dr. Thomas Li is a Research Scientist at the Biocomplexity Institute, University of Virginia. He specializes in discrete mathematics, computational biology and bioinformatics, information-theoretic and topological approaches to large datasets. His research focuses on RNA sequence, structure, evolution, and phylogenetic implications, with a particular interest in RNA secondary structures, pseudoknots, and RNA-RNA interactions.
His research brings analytic combinatorics and algebraic topology to provide a deep understanding of these discrete objects and facilitate further applications, such as structure prediction, Boltzmann sampling, and genotype-phenotype map. Additionally, he designed a novel information-theoretic framework to enhance the structure prediction for long non-coding RNAs by incorporating chemical probing data.
He also contributed to developing a new framework for topological data analysis based on weighted simplicial complexes, implementing it in the freely available Python software package WeightedSimplicialHomology. This theoretical advancement illuminates novel dependencies within data and provides an algebraic analysis perspective via weighted homology.
During the COVID-19 pandemic, Dr. Li and his colleagues developed a novel virus surveillance framework that enhances the prediction of emerging SARS-CoV-2 variants. Their framework takes genomic sequence data as input and issues timely alerts based on the linkages among key genetic sites identified via co-evolutionary relations. Their work serves as an early warning system for emerging virus threat detection, providing support for informed decision-making by officials in response to the pandemic.
He is open to collaborating with researchers across multiple disciplines and fields.