We gratefully acknowledge funding from the following grants:
- "Integration, Prediction, and Generation of Mixed Mode Information using Graphical Models, with Application to Protein-Protein Interactions", NSF IIS, to Naren Ramakrishnan, Chris Bailey-Kellogg (Dartmouth), Alan Friedman (Purdue), and Chris Langmead (CMU).
- "Formal Models, Algorithms, and Visualizations for Storytelling Analytics", NSF/DHS FODAVA, to Naren Ramakrishnan, Chris North, and Francis Quek.
- "Bayesian Analysis in Visual Analytics (BAVA)", NSF/DHS FODAVA, to Scotland Leman, Chris North, and Leanna House.
- "Temporal Data Mining Solutions for Sustainable IT Ecosystems", HP Labs, to Naren Ramakrishnan.
- "Deep Insights Anytime, Anywhere (DIA2)—Central Resource for
Characterizing the TUES Portfolio through Interactive Knowledge Mining
and Visualization", NSF TUES, to Aditya Johri, Naren Ramakrishnan, and G. Alan Wang, and others from Purdue, Stanford, and Arizona State University.
- "Novel Knowledge Discovery Techniques for Sustainable Energy Systems Modeling", NEC Labs, to Naren Ramakrishnan.
- "The Epidemiology of Information: Data Mining the 1918 Influenza Pandemic", NEH, to Tom Ewing, Bernice L. Hausman, Naren Ramakrishnan, Bruce Pencek, and Gunther Eysenbach.