With the convergence of big biological data, machine learning advances, and accessible genome editing, we now have the tools to significantly enhance our ability to read DNA and interpret variation. David’s current research focus is developing machine learning methods, especially based on deep representation learning, to learn how cells regulate gene expression throughout their lifespans. He is also interested in computational methods for single cell genomics and profiling tissues and organisms at single cell resolution as they age.
- Postdoc, Laboratory of John Rinn, Harvard University
- Ph.D. in Computer Science, University of Maryland College Park
- B.S. in Computer Science, Syracuse University
- NIEHS K25 Quantitative Research Development Award - 2013