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 group (http://www.databio.org) occupies wet and dry lab space in the Center for Public Health Genomics at UVA. We are an interdisciplinary and highly collaborative group, and therefore we are also affiliated with with several other entities at UVA, including the Department of Biomedical Engineering in the School of Engineering, and Departments of Biochemistry and Molecular Genetics and Public Health Sciences in the School of Medicine, as well as the Data Science Institute, the Cancer Center, and the Child Health Research Center.
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 for 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 cancer and development. 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 pediatric cancer, neuroimmunology, and other models to explore fundamental principles of regulatory DNA.
Teamwork is our foundation. We are trying to build a team of intelligent, creative people who are interested in working together to accomplish great things. We collaborate with other research groups extensively. We emphasize social coding, using GitHub to share code both within the group and so others can benefit from our work. Writing readable, reusable code pays off as we accumulate useful code and re-apply it to new biological systems. We challenge the norm in academic computational research of individual scientists writing isolated code, and instead push open, multi-author code development.