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.
Faculty & Affiliate Faculty
Modeling Molecular Motions, Protein Folding, Protein/Ligand Binding
Bioinformatics, Cancer Genomics, Cancer Phylogenetics, Phylodynamics, Phylogeography, Information Visualization
Mining Biological Text, Biological Named Entity and Relation Extraction
Variant Calling, Individualized Medicine, Health Data Analytics, Probabilistic Graphical Models, Neuroscience
Compressive Genomics, Information Theory
Bioinformatics, Protein Function and Structure, Systems Biology, Machine Learning and Optimization
Bioinformatics, Genomics, Modeling, Sequence Analysis, Machine Learning, Probabilistic Methods, Cancer, Behavior
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