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Computational Biology

Biomedical Sciences Graduate Program

About

Computational Biology cover photo
Computational biology is an interdisciplinary field that includes development and application of computational methods to make predictions or discover new biology from large collections of biological data, such as genetic sequences, cell populations, or protein samples. As our ability to generate biological data has increased, the study of biology increasingly requires deep computational skill for analysis and discovery. Computational approaches include analytical methods, mathematical modeling, and simulation. UVA's Ph.D. Program in Computational Biology equips students with the knowledge and skills to conduct advanced analysis of large biological data sets. It prepares students to develop and apply sophisticated computational approaches to key biological and biomedical questions, and also trains them with deep understanding of the biology behind the data. The program emphasizes the principles of open science: transparency, scientific reproducibility, data sharing, and collaborative research.  For more information, please see PhD Program in Computational Biology.

Program Faculty

Computational Biology; Bioinformatics; Precision Medicine; Machine Learning/AI; Quantum Computing

Statistical genetics and genomics.

Computational genomics and bioinformatics; long-read sequencing; structural variation; genome assembly and pangenomics; human genetic variation and disease genomics

Computational Biology; Machine Learning; Statistical Modeling; Single Cell Biology; Spatial Biology; Liquid Biopsy

Biomedical informatics, neuroscience, Alzheimer Disease and disorders of cognition, knowledge representation and integration, digital commons frameworks, ontologies, FAIR data, FAIR software, FAIR computation, argumentation frameworks, and evidence graphs

Statistical Genetics, Genetic Epidemiology, Biostatistics, Network analysis

Systems biology, infectious disease, cancer, toxicology, metabolic engineering

“Machine Learning for Big Data Analytics In Biomedicine"

Genomics, Molecular Evolution, Algorithm Design and Analysis

Genetic basis of common human disease, including type 1 diabetes, diabetic complications, ischemic stroke, atherosclerosis

Objective and quantitative modeling of data from imaging and other types of sensors

computational biology & bioinformatics; high performance computing; epigenomics & chromatin; pediatric cancer; computational regulatory genomics; machine learning

Proteoform Systems Biology: proteogenomic approaches to uncover the role of proteomic variation in human disease

Researching fundamental roles of the epigenome in control of the context-specific transcriptional programs.

Bioinformatics methodology development; Epigenetics and chromatin biology; Transcriptional regulation; Cancer genomics and epigenomics; Statistical methods for biomedical data integration; Advanced machine learning; Theoretical and computational biophysics

Data Mining, Machine Learning, Bioinformatics, Health Informatics