Biostatistics and Medical Informatics
Machine learning methods; the structure, function and evolution of regulatory networks; predictive models
- 3168 Wisconsin Institutes for Discovery
- Ph.D., University of New Mexico, (2009), Postdoctoral Research: Broad Institute
- Lab Website
- Biostatistics and Medical Informatics
- Research Interests
- Machine learning methods to study the structure, function and evolution of regulatory networks and build predictive models.
- Research Fields
- Computational, Systems & Synthetic Biology, Evolutionary & Population Genetics, Gene Expression, Drosophila, Fungi, Human, mouse & rat, Plants
Our research focuses on developing statistical computational methods to identify the networks driving cellular functions by integrating different types of genome-wide datasets, that measure different aspects of the cellular state. We are interested in identifying networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and constructing predictive models from these networks. This can help us understand (1) how environmental information is processed in cells to mount appropriate condition-specific responses, (2) how these networks change across different contexts such as environmental stresses, cell-types, tissues, diseases, and, (3) how these networks have evolved to suit organism life-style and habitat. Most important, by comparing such networks across many different contexts, we can identify the general organizational principles as well as notable exceptions that underlie condition-specific and organism-specific behavior.