Computational Biology
About
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
Bekiranov, Stefan
Computational Biology; Bioinformatics; Precision Medicine; Machine Learning/AI; Quantum Computing
Bourne, Philip E
Data Science
Chen, Wei-Min
Statistical genetics and genomics.
Clark, Timothy
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
Manichaikul, Ani W.
Statistical Genetics, Genetic Epidemiology, Biostatistics, Network analysis
Papin, Jason A.
Systems biology, infectious disease, cancer, toxicology, metabolic engineering
Qi, Yanjun
“Machine Learning for Big Data Analytics In Biomedicine"
Ratan, Aakrosh
Genomics, Molecular Evolution, Algorithm Design and Analysis
Rich, Stephen S.
Genetic basis of common human disease, including type 1 diabetes, diabetic complications, ischemic stroke, atherosclerosis
Rohde, Gustavo Kunde
Objective and quantitative modeling of data from imaging and other types of sensors
Sheffield, Nathan
computational biology & bioinformatics; high performance computing; epigenomics & chromatin; pediatric cancer; computational regulatory genomics; machine learning
Sheynkman, Gloria M.
Proteoform Systems Biology: proteogenomic approaches to uncover the role of proteomic variation in human disease
Zang, Chongzhi
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
Zhang, Aidong
Data Mining, Machine Learning, Bioinformatics, Health Informatics