The EMBERS project, sponsored by IARPA was featured in a major spread of the Virginia Tech Magazine.

Through the use of big data, Naren Ramakrishnan and his team from the computer science department’s Discovery Analytics Center (DAC) may make forecasting the future as commonplace as forecasting the weather.

The term “big data” refers to the use of algorithms and other tools to train computers to spot trends in collections of information that are too massive and complex to analyze with traditional methods. The proliferation of data has accelerated with the integration of computers into our daily lives, from social media on our phones to tracking buying habits at the grocery store.

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Prithwish Chakraborty, DAC/CS PhD student is helping organize the Flu Forecasting questions on the SciCast prediction market  (https://scicast.org/flu) this year. Participants are required to predict several flu season characteristics, at national and at regional levels (10 HHS regions). Read his analysis

Naren Ramakrishnan, director of the Discovery Analytics Center and Bryan Lewis, public health policy analyst, Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, presented research being done in their respective laboratories in a briefing to Virginia Secretary of Technology Karen Jackson. Senator Mark Warner’s staff were also in attendance. It was a great opportunity to brief them and present DAC’s cutting-edge research in forecasting and analytics.

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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.

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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.”

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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.

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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.

Congrats to Dhruv Batra for his Windows Azure for Research Award! Microsoft will provide one year of computing and storage support to CloudCV on their Azure cloud platform.

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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

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