In our research, we combine computational methods with an experimental component in a unified effort to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems.

The goal of our research is to bring the capabilities of computer science and statistics to the study of gene function and regulation in the biological networks through integrated analysis of high-throughput biological data from diverse data sources. We are designing systematic and accurate computational and statistical algorithms for biological signal detection in high-throughput data sets and integrating them with targeted experimentation in S. cerevisiae (baker's yeast). We are also researching novel visualization methods for large-scale biological data in order to facilitate biological discovery through effective data presentation. Through this combination of cutting-edge computation and integrated experimentation, we aim to achieve highly accurate analysis and modeling of biological data.