As featured in the Big Data Journal: “Forecasting has long been a mystic art with techniques shrouded in mystery. Approaches from big data and machine learning are now revolutionizing the science of predictive analytics. The EMBERS system has been producing early warnings of civil unrest across Latin America for over two years. In February 2014, EMBERS forecast the occurrence and spread of student-led protests in Venezuela days in advance. For more information, please see the article by Doyle and colleagues in this issue of Big Data.” Read more

Devi Parikh, assistant professor in the department of electrical and computer engineering and DAC faculty member received close to $1 million “to teach machines to use ‘common sense’ in image analysis.” Parikh, who leads the Computer Vision Lab at Virginia Tech, is the recipient of the Allen Distinguished Investigator Award from the Paul G. Allen Family Foundation. She’s using the money to help computers “read” complex images with the use of cartoon clip art scenes. To read more about Devi’s grant click here.

 

Devi Parikh, an assistant professor in the Bradley Department of Electrical and Computer Engineering and DAC faculty member at Virginia Tech, has received an Allen Distinguished Investigator Award for close to $1 million from the Paul G. Allen Family Foundation to teach machines to use “common sense” in image analysis. Parikh uses cartoon scenes crafted from clip art to help computers “read” complex images. “Humans interpreting visual scenes can take advantage of basic knowledge about how objects typically interact, but computers,” Parikh said, “don’t have the same skill”.

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Congratulations to Devi Parikh who has been named a 2014 Allen Distinguished Investigator! Devi’s work will impart common sense reasoning to computers to accomplish human-like visual recognition. She is in great company! Read More

Parang Saraf, a DAC/CS Ph.D. student in the National Capital Region, recently accepted the VAST Challenge 2014 Grand Challenge Award for Effective Analysis and Presentation in Paris, France. The VAST Challenge provides an opportunity for visual analytics researchers to test their innovative thoughts on approaching problems in a wide range of subject domains against realistic datasets and problem scenarios. The award was presented during the IEEE Vis Conference, where Saraf spoke for 30 minutes about the team’s solution to the challenge.

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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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