Our researchers work on core computational biology-related problems, including genomics, proteomics, metagenomics, and phylogenomics. We develop novel techniques that combine ideas from mathematics, computer science, probability, statistics, and physics, and we help identify and formalize computational challenges in the biological domain, while experimentally validating novel hypotheses generated by our analyses.
We are developing algorithms with improved accuracy for large-scale and complex estimation problems in phylogenomics (genome-scale phylogeny estimation), multiple sequence alignment, and metagenomics. We are exploring gene regulation—developing advanced techniques to predict the diverse function of noncoding parts of DNA and to relate interspecies and interpersonal differences in DNA to differences in the organism’s form and function. We work broadly in the development of machine learning techniques for computational biology, with research spanning the areas of molecular and structural biology; networks and systems biology; and molecular mechanisms of human disease.
|Jiawei Han||data mining for genomics and medical informatics|
|Jian Peng||bioinformatics, protein function & structure, systems biology, machine learning and optimization|
|Saurabh Sinha||bioinformatics, gene regulation, comparative genomics, sequence analysis|
|Tandy Warnow||bioinformatics, multiple sequence alignment, phylogenomics, metagenomics, and historical linguistics|
|ChengXiang Zhai||information retrieval, natural language processing for medical informatics|