Systems biology and protein modeling

Richard Bonneau
Professor of Biology and Computer Science
Education
- 2001 Ph.D. (Biochemistry), University of Washington
- 1997 B.A. (Biochemistry), Florida State University
Dr. Bonneau focuses on two main categories of computational biology: learning networks from functional genomics data and predicting and designing protein and peptoid structure. In both areas he has played key roles in achieving critical field-wide milestones. In the area of structure prediction he was one of the early authors on the Rosetta code, which was one of the first codes to demonstrate accurate and comprehensive ability to predict protein structure in the absence of sequence homology. His lab has also made key contributions to the areas of genomics data analysis. They focus on two main areas: 1) methods for network inference that learn dynamics and topology from data (the Inferelator) , and 2) methods that learn condition dependent co-regulated groups from integrations of different genomics data-types (integrative biclustering). His lab strives to develop new methods that let systems-biologists derive functional forms from relevant biology and parameters from data automatically. Dr. Bonneau has also helped to start a new project with political scientists and experimental psychologists to apply methods for learning network structure from time series to social media time series data (using Twitter, online blogs about politics, and Facebook as our initial data sources (recently funded by NSF INSPIRE, http://smapp.nyu.edu/).
Fellowships/Honors
- 1998-2001 Howard Hughes Medical Institute pre-doctoral Fellowship in the Biological Sciences
- 1996 Magna Cum Laude, Biochemistry, FSU
- 1996 American Cancer Society – James Jay Fisher Fellowship
- 1993 Florida Academic Scholars Award
- 1993 International Baccalaureate Degree
Contact Information
Richard Bonneau
Professor of Biology and Computer Science bonneau@nyu.edu Center for Genomics and Systems Biology12 Waverly Place
Room 406
New York, NY 10003
Phone: (212) 992-9516