Professor of Applied Computational Mathematics & Statistics
University of Notre Dame
Biostatistics, applied statistics, and bioinformatics
Dr. Li has a mixed background in statistics, computer science, and biology. He is interested in developing statistical and computational methods for problems raised in biology, medicine, genetics, biochemistry, and many other fields.
High-throughput techniques like next-generation sequencing and microarrays have largely changed the way that biologists think and work. At the same time, these big and high-dimensional data have posed huge challenges for statisticians. Examples like how to choose important variables (e.g. genes, pathways that are related to diseases) among thousands and how to build an interpretable, simple yet powerful-enough models (e.g. using a small set of genes to predict disease status) based on them. Dr. Li has been working on these data, and he has proposed methods for modeling, fitting, testing, and mining on them.
Dr. Li also has experience in working on methylation, SNP data, RNAi, gene network construction, environmental-genetic factor interaction, and other topics. He has a strong interest in collaborating with people with different backgrounds and working on both problems that he is familiar with and those that are new to him.