Richard Bonneau is an American computational biologist whose primary research is in the following areas: learning networks from functional genomics data and predicting and designing protein and peptiodomimetic structure. An associate professor at New York University, he holds appointments in the Department of Biology and the Courant Institute of Mathematical Sciences. In 2008, Bonneau was selected as one of the top 20 scientists under 40 by Discover magazine.
In the area of structure prediction, Bonneau was one of the early authors on the Rosetta code, which was one of the first codes to demonstrate the ability to predict protein structure in the absence of sequence homology. Using IBM's world community grid to carry out folding of whole proteomes, his group has also applied structure prediction to the problem of genome and proteome annotation. Bonneau’s laboratory strives to develop new methods that let systems-biologists derive functional forms from relevant biology and parameters from data automatically.
It has made key contributions to the areas of genomics data analysis, focusing on two primary areas: 1. methods for network inference that uncover dynamics and topology from data and 2. methods that learn condition dependent co-regulated groups from integrations of different genomics data-types.
In 2013, he and his colleagues at NYU started a project to examine the impact of social media use on political attitudes and participation by applying methods from a range of academic disciplines. While social media's influence on political participation and attitudes remains in question, Twitter, Facebook, and Instagram undoubtedly offer amounts of data that far exceed earlier research methods. The project-- Social Media and Political Participation (SMaPP) --relies on bothsurvey data and publicly available social media data such as "Tweets"to address a range of questions concerning the causal processes that shape political participation.
Along with Vestienn Thorsson, David Reiss and Nitin Baliga he developed the Inferelator and cMonkey, two algorithms that were critical to an effort to learn a genome-wide model of the Halobacterium regulatory network. Dr. Nitin Baliga and Dr. Bonneau demonstrated that their model was capable of predicting the genome-wide transcriptional dynamics of the cell’s response to new environments (a work that resulted in publication in Cell in December 2007). This work represents the first fully data driven reconstruction of a cells regulatory network to include learning of kinetic/dynamical parameters as well as network topology.