In this paper we present a study of citation and co-authorship networks for articles from the ASME Design Automation Conference (DAC) during the years 2002-2015. We identify key authors, show that the co-authorship network exhibits the small world network property, and reveal other insights from network structure. Results from two topic modeling methods are presented. A frequency-based model was developed to explore DAC topic distribution and evolution. Citation analysis was also conducted for each core topic. A correlation matrix and association rule mining were used to discover topic relations and to gain insights for research gaps and recommendations. A recently developed unsupervised learning algorithm, propagation mergence (PM), was applied to the DAC citation network. Influential papers and major clusters were identified and visualizations are presented. The resulting insights may be beneficial to the engineering design research community, especially with respect to determining future directions and possible actions for improvement. The data set used here is limited. Expanding to include additional relevant conference proceedings and journal articles in the future would offer a more complete understanding of the engineering design research literature.