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/).