Assistant Professor
Computer Science
Biomedical Sciences

208A Dryden Hall
Corvallis, OR 97331
(541) 737-5609
(541) 737-2730


  • Ph.D., Physics, University of Maryland
  • M.S., Physics, University of Maryland
  • Sc.B., Mathematical Physics, Brown University


Stephen Ramsey is a computational systems biologist with extensive experience in scientific computing in a variety of life sciences application domains. His undergraduate and graduate research in astrophysics and cosmology led to a career emphasis on scientific computing and on using computational approaches to solve large modeling and data analysis problems. Through his postdoctoral work on the Human Genome Project at the University of Washington, Ramsey became interested in genomics and in computational biology. This interest led to research projects focused on gene regulation at the Institute for Systems Biology and at the Seattle Biomedical Research Institute, where he developed his independent research program — focused on computational systems approaches to elucidate gene regulatory networks — through an NIH Career Development Award.


Research Interests

My research program combines both computational and experimental approaches to map and functionally characterize gene regulatory networks. My long-term aim is to develop data-driven approaches to “reverse engineer” the regulatory networks that control immune responses in host defense against pathogens and in chronic inflammatory diseases. A comprehensive understanding of these networks is a gateway to being able to predict how the immune system will respond to novel therapies, pathogens, and vaccines. On the computational side, I use integrative machine-learning methods to both identify the genomic regulatory elements that mediate transcriptional control in specific cell types, and to leverage information from genetic epidemiology and from molecular networks to uncover novel molecular regulators of inflammatory responses. I am particularly interested in applying state-of-the-art semi-supervised learning algorithms to identify candidate disease genes using features derived from each gene’s local interaction network neighborhood. On the experimental side, I have been studying the mammalian macrophage (a key constituent of the innate immune system) and its roles in atherosclerosis and in host defense, as both a primary application area and a “test-bed” for integrative methods development. My collaborators and I are also employing this computational systems biology approach in studies of gene regulation in other cell types such as smooth muscle cells and cancer cells.

Research areas:

Machine learning, Data mining, Systems biology, Gene regulation