The Computational Biology Track curriculum is designed to help students learn how to leverage mathematical and computational approaches to understand biological and chemical processes. For more details, review the Computational Biology Degree Plan.
Mathematical and computational concepts, methods, and algorithms are being applied to all areas of basic and clinical life sciences, which results in a variety of research topics. Examples include:
- Biophysics and Structural Biology – Protein structure and function prediction; analysis of biological sequences and 3D structures; macromolecular interactions and biological networks; molecular evolution
- Chemical Biology – Analysis of small organic molecules; design of materials and drugs; chemical dynamics
- Genetics and Genomics – Analysis of DNA/RNA sequences; genome association to cellular and organismal function; statistical genetics
- Imaging – Computer vision and pattern recognition applied to medical imaging and microscopy data, statistical and mathematical modeling of the spatiotemporal organization of image events from molecular to macroscopic scales
- Medical Informatics – Machine learning of patterns in multivariate clinical databases, predictive modeling of clinical outcomes by variable association
- Systems Biology – Computational reconstruction and analysis of biological networks; modeling of complex, nonlinear systems; spatiotemporal integration of chemical and mechanical processes across scales
Research Interests: Genomic, genetic, and molecular approaches to autism spectrum disorders
Technical Expertise: Genetic linkage and mapping, whole exome and genome sequence analysis
Research Interests: Somatic diversification of antigen receptor genes; repertoire characteristics as biomarkers of immune status and disease; host-pathogen interactions
Research Interests: Computational biology; analysis of protein sequences and structures
Research Interests: Statistical methods, clinical trials, epidemiology, health economics
Research Interests: Single-cell regulatory genomics, cell fate engineering, function of non-coding elements and genetic variants.
Technical Expertise: Single-cell genomics, genome engineering, gene regulation, cell state engineering
Research Interests: Quantitative spatiotemporal structure-function relationships of multi-molecular assemblies; linking molecular and cellular behavior across multiple scales; computational image analysis and mathematical modeling
Technical Expertise: Computer vision, stochastic modeling and model calibration, time series analysis
Research Interests: Artificial intelligence in medical imaging, radiotherapy treatment planning, and adaptive radiotherapy etc
Research Interests: Molecular recognition in protein folding, chaperone structural biology, and neurodegeneration
Technical Expertise: Biochemistry, biophysics, structural biology, chemical biology and protein modeling
Research Interests: DNA and RNA sequence alignment, graph alignment to population of genomes, genotyping, personalized medicine
Technical Expertise: Computer Algorithms, Commercial-grade Software Development, Deep Learning, Biostatistics, Medical Data Analysis
Research Interests: Comparative genomics, epigenetics, and alternative splicing analyses relevant to brain evolution and cognitive disorders
Technical Expertise: Transcriptomics, weighted gene co-expression network analyses, single-cell RNA-sequencing analyses
Research Interests: Computational genomics. Using molecular, genetic, genomic, and computational approaches to understanding signal-regulated gene expression. Integrating data from different genomic platforms using computational approaches
Research Interests: Cancer immunology; statistical modeling for cancer genomics data; data mining; tumor-infiltrating immune repertoire profiling
Technical Expertise: Immune receptor repertoire, computational prediction of cancer antigens, machine learning, cancer-immune interactions, hypervariable immune receptors, cancer biomarker identification
Research Interests: Structural and biochemical characterization of membrane proteins in cholesterol biosynthesis, metabolism and signaling
Research Interests: Optimization in conformational and network space.
Technical Expertise: Statistical mechanics, protein folding, non-equilibrium physics
Research Interests: Adaptive radiation therapy; treatment-plan optimization; radiation-dose calculations
Research Interests: Developing state-of-the-art machine learning approaches to extract radiological imaging and imaging-genomic biomarkers and to engender personalized prognostics in neuroscience and oncological applications
Technical Expertise: Advanced image analysis, neuroimaging, MRI, MEG/EEG, PET/SPECT
Research Interests: Methods development for cryo-electron microscopy, X-ray crystallography, and next-generation sequencing; Structural studies of macromolecules with X-ray crystallography and cryo-EM single particle reconstruction; Higher-order structure of eukaryotic chromatin.
Research Interests: Understanding tissue organization via machine learning; intra-tumor heterogeneity; computational image analysis and spatial statistics
Technical Expertise: Machine learning (both classical and deep). Image analysis specifically for microscopy
Research Interests: Statistical analysis and rational design of cellular systems
Technical Expertise: principal components analysis and matrix factorization, statistical analysis of protein sequences, python for large scale data analysis
Research Interests: Integrating structure, kinetics, and computation to understand the molecular determinants and regulatory mechanisms of microtubule dynamics
Research Interests: Cellular information processing; molecular mechanisms of G protein-mediated signaling: amplification, selectivity, response kinetics and signal integration
Research Interests: Antibiotic resistance and sensitivity; single molecule biophysics; synthetic biology
Research Interests: Biostatistics, Machine learning, Immunogenomics, and Tumor genomics
Research Interests: Improving treatments for cancer by applying computer science and statistical methodologies to analyzing high-throughput biological data. New models and tools are currently in development to assist the investigation of disease mechanisms and their related diagnostic innovations.
Research Interests: Biostatistics, bioinformatics, statistical genomics, clinical trial design, and biomarker studies
Technical Expertise: Developing and applying statistical and computational approaches to decipher genetic and genomic problems, in particular, human complex traits.
Research Interests: Statistical genetics, genetic epidemiology, bioinformatics, gene mapping for complex traits
Research Interests: Statistical genetics, forward genetics screening, statistical computation, next-generation sequencing, genetic association studies
Technical Expertise: Statistical genetics, microbiome, statistical computation, bioinformatics, deep learning
Research Interests: Computational Neuroscience
Research Interests: Machine Learning and Statistical Methods for Genomics, Sequence Basis of Genome Regulation, Statistical Methods for Single-cell Data Analysis, The Evolution of Noncoding Genome.
These faculty members do not accept graduate students. They participate in teaching, co-mentoring, exam and dissertation committees, and all other program responsibilities.
Research Interests: Development of methods to study heteroplasmy and the somatic evolution of cancers.
Research Interests: Active machine learning driven cancer drug discovery
Research Interests: Statistics, statistical genetics, genetic epidemiology, genome-wide association studies