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
Bioinformatics, Cancer Genomics, Cancer Phylogenetics, Phylodynamics, Phylogeography, Information Visualization
Parallel Algorithms, Genomics, Computational Phylogenetics, High-Performance Computing
Mining Biological Text, Biological Named Entity and Relation Extraction
Individualized Medicine, Health Data Analytics, Probabilistic, Graphical Models, Multi-omics, Neuroscience, Pharmacogenomics
Bioinformatics and Computational Biology
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
Bioinformatics, Genomics, Modeling, Sequence Analysis, Machine Learning, Probabilistic Methods, Cancer, Behavior
Computational Genomics
Deep Learning for Drug Discovery, Molecule Property Prediction and Generation, Genomic and Phenotypic Modeling
Graph Algorithms, Statistical Estimation, Heuristics for NP-Hard Optimization Problems, Phylogenomics, Metagenomics, Multiple Sequence Alignment
Genomics, Computational Phylogenetics, High-Performance Computing
Intelligent Biomedical Decision Support Systems, Analysis of Electronic Medical Records, Biomedical Literature Retrieval and Mining