Members of the staff of the Discovery Analytics Center. Left to right are Nathan Self, Patrick Butler, and Naren Ramakrishnan.
DAC and director, Naren Ramakrishnan, are featured as this month’s Virginia Tech Inventors of the Month by the Office of Research and Innovation for work in Early Model Based Event Recognition using Surrogates (EMBERS) software project.
EMBERS is a fully automated system for forecasting significant societal events, such as influenza-like illness case counts, rare disease outbreaks, civil unrest, domestic political crises, and elections, from open source surrogates. To read more about EMBERS click here.
Congratulations to Scotland Leman, DAC faculty member and associate professor in the department of statistics, on receiving the W.J. Youden Award in Interlaboratory Testing. Dr. Leman was presented with the award at the 2016 Fall American Statistical Association Technical Conference. The award recognizes the authors of publications that make outstanding contributions to the design and/or analysis of interlaboratory tests or describe ingenious approaching to the planning and evaluation of data from such tests. Click here to read more about the award.
DAC will create and administer a new interdisciplinary Ph.D. certificate program called UrbComp, which is set to launch in spring 2016. The UrbComp Ph.D. certificate is focused on big data and urbanization through a $3 grant over five years from the National Science Foundation Research Traineeship Program. UrbComp will be open to students from both the Blackburg and National Capital Region campuses who are pursuing a Ph.D. in one of eight departments: computer science, mathematics, statistics, electrical and computer engineering, population health sciences, urban affairs and planning, civil and environmental engineering, or sociology. To read more about the program click here.
Members of the Visual Analytics team include (from left) Xinran Hu, Chris North, Leanna House, Scotland Leman, Lauren Bradel, Jessica Zeitz Self, and Ian Crandell.
Big Data: Everyone wants to use it; but few can. A team of researchers at Virginia Tech is trying to change that.
In an effort to make Big Data analytics usable and accessible to nonspecialist, professional, and student users, the team is fusing human-computer interaction research with complex statistical methods to create something that is both scalable and interactive.
“Gaining big insight from big data requires big analytics, which poses big usability problems,” said Chris North, a professor of computer science and associate director of the Institute for Critical Technology and Applied Science’s Discovery Analytics Center.
With a $1 million from the National Science Foundation, North and his team are working to make vast amounts of data usable by changing the way people see it.
Yong Cao, an assistant professor with the Department of Computer Science in the College of Engineering, along with Leanna House, an assistant professor, and Scotland Leman, an associate professor, both with the Department of Statistics of the College of Science, are working with North to bring large data clouds down to manageable working sets. Read more.