Status: PhD Student: Biology (Biology & Society) Profile: http://gradinfo.cbs.asu.edu/?page_id=497 E-mail: Deryc.Painter@asu.edu Computational approaches to the history of science offer opportunities to ask new and interesting questions. Framing questions using computational thinking allows for solutions to come from “big data” otherwise too unwieldy for practical research. Bibliometric data, such as citation histories and collaboration patterns, allows for large-scale analysis of the nature of collaboration and the spread of ideas within science. By networking incoming and outgoing citations alongside co-authorship and geographic data, a model of the professional working environment can be constructed and analyzed for patterns surrounding predominate publications in a given scientific field. If we can achieve a better understanding of the environment in which professional scientists work will allow for the possibility to understand the circumstances leading up to a perceived innovation. In much the same way biologists use life history traits to predict the traits of organisms found within specific ecosystem, an understanding of the professional ecosystem can provide similar insights for ideas contained within publications. Publications: Bliss, N. T., Peirson, B. E., Painter, D., & Laubichler, M. D. (2014, November). Anomalous subgraph detection in publication networks: Leveraging truth. In Signals, Systems and Computers, 2014 48th Asilomar Conference on (pp. 2005-2009). IEEE.