Faculty and Student Authors Awarded Best Paper at VDA 2013-Silicon Valley Campus - Carnegie Mellon University

Wednesday, February 6, 2013

Faculty and Student Authors Awarded Best Paper at VDA 2013

The team of CMU-SV authors who won Best Paper at VDA 2013.
The team of CMU-SV authors who won Best Paper at VDA 2013.

Carnegie Mellon University Silicon Valley (CMU-SV) Campus professors Dr. Ole J. Mengshoel and Dr. Ted Selker along with Ph.D. student Priya Sundararajan were awarded Best Paper at the 2013 Conference on Visualization and Data Analysis. Their paper, “Multi-Focus and Multi-Window Techniques for Interactive Network Exploration,” was recognized along with four other papers at the conference in Burlingame, CA.

The three-day conference focused on research, development and application in visualization and visual analytics with sessions on topics such as high dimensional and multi-focus visualization, high performance computing and biomedical analysis, exploratory data analysis, among others.

The CMU-SV paper addressed the issue of comparing nodes in different parts of a network. When networks are scaled to fit a computer screen, their detailed structure and node labels can become difficult to read or even invisible. The paper presents multi-focus and multi-window techniques that improve interactive exploration of networks so that detailed data associated with the zoomed-in nodes can be more easily accessed and inspected.

VDA 2013’s qualifications for Best Paper included the scientific quality of the work as well as the potential of expanding the paper into a journal article. The CMU-SV authors have been invited to submit an article based on their winning paper to the Information Visualization journal. Lead student authors also received a monetary award at the conference.

“We're very pleased to have been awarded this Best Paper award,” said Mengshoel. “We hope that the concepts we put forth in this paper will improve the capability of analysts to visualize, understand, and debug complex networks - for example electrical power networks, biological networks, and Bayesian networks.”