The Sanghani Center is home to high-profile research, garnering recognition within and beyond the data analytics community.
Our talented team has been recognized with many competitive research awards and featured in major news and media outlets such as the Wall Street Journal, Newsweek, the Boston Globe and the Chronicle of Higher Education.
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.
An enormous gap exists between human abilities and machine performance when it comes to understanding the visual world from images and videos. Humans are still way out in front.
“People are the best vision systems we have,” said Devi Parikh assistant professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. “If we can figure out a way for people to effectively teach machines, machines will be much more intelligent than they are today.”
Analysts for the Central Intelligence Agency, the National Security Agency and more than a dozen other government organizations depend on their ability to forecast national and global events to help ward off various threats to the country, but old-style approaches can produce flawed results. Read more
When Dhruv Batra of the Virginia Tech College of Engineering travels in September to Zurich for the 2014 European Conference on Computer Vision, he will be a rising star in the growing field of vision and pattern recognition in computers.
The assistant professor with Virginia Tech’s Bradley Department of Electrical and Computer Engineering previously co-led a tutorial in the research field at another industry conference in Ohio this past June. On his way to Zurich, Batra will give talks on the same subject — creating software programs that help computers “see” and understand photographs just as humans can – at software giant Microsoft’s research lab at Cambridge University and then a separate event at Oxford University, both in the United Kingdom.
Lenwood Heath, DAC faculty member, is working with Boris Vinatzer, associate professor in the College of Agricultural and Life Sciences who has developed a new way to classify and name organisms based on their genome sequence and in doing so created a universal language that scientists can use to communicate with unprecedented specificity about all life on Earth. Heath oversaw the development of the bioinformatic pipeline to implement the system. He was interested in collaborating with Vinatzer because of the potential to empower scientists to communicate accurately with one another about biological systems. To read more about their collaboration click here.
The new methods of “big data” analysis can inform and expand historical analysis in ways that allow historians to redefine expectations regarding the nature of evidence, the stages of analysis, and the claims of interpretation.1 For historians accustomed to interpreting the multiple causes of events within a narrative context, exploring the complicated meaning of polyvalent texts, and assessing the extent to which selected evidence is representative of broader trends, the shift toward data mining (specifically text mining) requires a willingness to think in terms of correlations between actions, accept the “messiness” of large amounts of data, and recognize the value of identifying broad patterns in the flow of information.2
Congrats to C.T-Lu and his students whose paper on finding the breadcrumbs of civil unrest on Twitter has been picked as a Jan 2014 highlight by the IEEE Special Technical Community (STC) on Social Networking! For more details visit here
Congrats to Dhruv Batra who has received an Amazon Web Services in Education grant for developing CloudCV, a cloud-based computer vision platform for processing big visual data. CloudCV provides APIs for MATLAB and Python as well as a web front-end, and will benefit both experts and non-experts who desire to analyze image data. For more go to CloudCv