I have funding available for PhD students. Graduate students must be part of the UVA graduate programs in Biomedical Engineering or Biomedical Sciences. If you’re not yet at UVA, you should apply through one or both of those programs. For students in these programs, contact for rotations, advising, or collaborations. You’ll be likely to develop some of the skills listed on my page of skills and training materials.

About the lab

The Databio group (http://www.databio.org) is an interdisciplinary and collaborative computational biology research group located in the Center for Public Health Genomics at UVA. We are also affiliated with the Department of Biomedical Engineering, Department of Biochemistry and Molecular Genetics, Department of Public Health Sciences, the School of Data Science, the Cancer Center, and the Child Health Research Center at UVA.

Our research is at the interface of computation and biology, drawing on techniques in computer science, data science, bioinformatics, and machine learning, and applying them to biological questions in cancer, epigenetics, single-cell analysis, development, and genomics. We collect both novel data and public data and make use of UVA’s high-performance cluster for computational approaches to biological questions.

Our biological questions are focused on understanding gene regulation and epigenetics in development and disease, such as cancer, atherosclerosis, and kidney disease. How does DNA encode regulatory networks that enable cellular differentiation? We rely on experimental data from sequencing-based epigenome experiments like ATAC-seq, bisulfite-seq, and ChIP-seq, and we use these data to study fundamental principles of regulatory DNA in human health.

We are building a team of intelligent, creative people who are interested in working together to accomplish great things. We collaborate extensively. We emphasize social coding, using GitHub to share code both within the group and so others can benefit from our work. We seek to write readable, reusable code and apply it to new biological questions. We challenge the norm in academic computational research of individual scientists writing isolated code, and instead push open, multi-author code development. If these topics excite you, please read more about our research interests, recent publications, and philosophy of open data.