Bioinformatics and Computational Biology
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.
Relevant Seminars
- Illinois Computer Science Speaker Series: brings prominent leaders and experts to campus to share their ideas and promote conversations about important challenges and topics in the discipline.
Faculty & Affiliate Faculty
Modeling Molecular Motions, Protein Folding, Protein/Ligand Binding, Neuroinformatics
Cancer genomics, B cell phylogenetics, infection genomics, RNA secondary structure
Parallel algorithms in computational biology
Biomedical literature search and mining
Individualized Medicine, Health Data Analytics, Probabilistic, Graphical Models, Multi-omics, Neuroscience, Pharmacogenomics
AI for bioengineering, cell language model, protein language model
Application for medical insights
Generative molecule design, protein language model, sequence-structure co-design
Applied machine learning methods in Bioinformatics; Algorithm development for big data analysis; Data visualization; Integrative bio-medical data analysis; Single cell dynamic biological data analysis; High throughput genomic profiling data analysis.
Compressive Genomics, Information Theory
AI/Machine Learning, Computational Biology, and Health Informatics
Bio-informatics, Computational Genomics
AI for life science, drug discovery and development
Graph Algorithms
Phylogenomics, metagenomics
Genomics, Computational Phylogenetics, High-Performance Computing
Intelligent Biomedical Decision Support Systems, Analysis of Electronic Medical Records, Biomedical Literature Retrieval and Mining