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National Institutes of Health

National Institutes of Health logo

A part of the U.S. Department of Health and Human Services, National Institutes of Health (NIH) is the largest biomedical research agency in the world. Approximately 83 percent of NIH’s $30.3 billion FY 2015 budget goes to more than 300,000 research personnel at over 3,000 universities, medical schools, and other research institutions in every state […]

Department of Homeland Security

Department of Homeland Security logo

The Department of Homeland Security (DHS) distributes grant funds to enhance the ability of regional authorities to prepare, prevent, and respond to terrorist attacks and other disasters. The Department’s Science and Technology Directorate invests in scientific research leading to the development of new and innovative technologies. More about DHS

Locations

The Virginia Tech Research Center – Arlington 900 N. Glebe Rd. Arlington, VA 22203 0.3 miles from the Ballston-MU Metro Station   The Northern Virginia Center 7054 Haycock Rd. Falls Church, VA 22043 0.4 miles from the West Falls Church Metro Station Torgersen Hall, Suite 3160 620 Drillfield Dr. Virginia Tech Blacksburg, VA 24060 Kelly […]

Scalable Learning of Complex Structured Models

Complex phenomena underlie important targets for data analysis such as online and offline human social behavior, medicine, microbiology and ecology, and city operations. Analysis in these domains concerns variables embedded in natural networks of influence and dependence, so models for these variables need to consider their relational structure. Currently understood algorithms for training relational models […]

Visual Question Answering (VQA)

Given an image and a free-form, natural language question about the image (e.g., “What kind of store is this?”, “How many people are waiting in the queue?”, “Is it safe to cross the street?”), the machine’s task is to automatically produce a concise, accurate, free-form, natural language answer (“bakery”, “5”, “Yes”). Answering any possible question […]

Learning Common Sense via Visual Abstractions

Machines today can perform certain sophisticated but niche tasks quite well (e.g. play chess, play Jeopardy, vacuum our floors, drive autonomously). However, they are far from being sapient intelligent entities.  This is partly because they lack “common sense” knowledge — basic knowledge like birds fly, children are afraid of bears, kicking a ball makes it move. […]

Semi-Supervised Learning of Cyberbullying and Harassment Patterns in Social Media

The goal of this research project is to develop algorithms for identifying detrimental online social behavior on the Internet. A growing majority of human communication occurs over Internet services, and advances in mobile and connected technology have amplified individuals’ abilities to connect and stay connected to each other. Moreover, the digital nature of these services […]

Summarizing Beliefs of Intelligent Systems via Diverse Predictions

This project has proposed theory, algorithms, and implementations for intelligent systems that convey their beliefs about the world by producing a small set of diverse plausible hypotheses or guesses about the state of the world (e.g. multiple segmentations for objects in an image, or human body key point locations). Before this project, intelligent systems either produced […]

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timeline-entry 1283