Associate Professor, Public Health Sciences
Bioinformatics and Genomics, Biomedical Engineering, Cancer Biology, Computational Biology, Epigenetics
computational biology & bioinformatics; high performance computing; epigenomics & chromatin; pediatric cancer; computational regulatory genomics; machine learning
My research is at the interface of computation and biology, drawing on techniques in computer science, data science, bioinformatics, and statistics, and applying them to biological questions in cancer, epigenetics, development, and genomics.
My particular projects of interest include:
1. Computational cancer epigenomics. I am interested in understanding how cancers commandeer the normal regulatory machinery to create disease. As a model system, I use Ewing sarcoma, a rare pediatric tumor that is caused by a fusion protein. To explore how this fusion protein re-wires the cells to proliferate uncontrollably, I am examining genome-wide epigenetic profiles of Ewing sarcoma. These biological questions lead to computational challenges and opportunities inherent in dealing with large datasets.
2. Genome-scale analysis of gene regulation and chromatin structure. I am interested in how cells fold their DNA to enable complex regulatory patterns. Humans are made up of many different cell-types. Though these cell-types share a single genome, they have very different phenotypes and functions. The basis for these dynamics is regulatory DNA, which governs when and where different genes are expressed. I use computational approaches like machine learning, supercomputing, and software engineering to analyze data genome from high-throughput experiments to understand how cells change during development.
If you're interested in joining the group, please visit http://databio.org/join/