The NCDS has issued a Call for Proposals for 2017 – 2018 Data Fellows Program. The Data Fellows program provides funding to faculty members from NCDS academic member institutions to support research on novel and innovative data science and data-focused business problems. Proposals are due April 7, 2017.
Data Fellows 2016 – 2017
The NCDS will provide each Data Fellow with $50,000 to support work that addresses data science research issues in novel and innovative ways. Their work will be expected to advance the mission and vision of the NCDS, which formed in early 2013. Fellowships begin July 1 and last one year.
Data Fellow positions are open to faculty members at NCDS member institutions, which includes universities in the University of North Carolina system and Drexel University. A record 30 researchers from six member universities applied for Fellowships this year. Their proposals addressed many of the hot research topics in data science, including data mining, business analytics, cybersecurity, the Internet of Things, and using data for informed decision-making.
“The scope and breadth of the proposals we reviewed illustrates how important data science has become and how data can be used to solve problems in a broad range of fields,” said Steve Gustafson, director of the Knowledge Discovery Lab at GE Global Reseach in Niskayuna, NY, and one of the NCDS members who served on the Data Fellows review committee. “With so many outstanding proposals, choosing the winners was difficult, but I firmly believe these incoming Fellows are conducting research that is relevant to the business needs of our members and that contributes to solving important societal problems.”
A review committee representing seven NCDS member institutions reviewed the proposals and chose the three 2016 Data Fellows.
View this year’s Data Fellows presenting at our September 2016 Data Fellows Showcase.
This year’s Data Fellows are:
Marcello Balduccini, PhD, assistant research professor, College of Computing and Informatics, Drexel University
Project title: Action-centered Information Retrieval
Information retrieval (IR), which includes everything from searching private databases to consulting Wikipedia, is central to improving healthcare, facilitating scientific discovery, and providing industry with competitive advantages. This project aims to develop action-centered IR, a type of IR that can retrieve information about events (for example, the acquisition of a company) and accurately match it to a query about the state of the world that resulted from that event. Action-centered IR could greatly improve the quality of knowledge gained through data mining, business analytics, and cybersecurity-related queries.
Casey Dietrich, PhD, assistant professor, civil, construction, and environmental engineering, North Carolina State University
Project title: Mapping and Visualization of Coastal Flood Forecasts for Decision Support
Researchers in North Carolina use the Advanced CIRCulation (ADCIRC) model to provide real-time information about storm surge, water inundation, wind speeds, and wave heights during coastal storms. These models are produced constantly during major storms, however, communicating the information in the simulations to end users, such as emergency managers, is more challenging. This project will use visualization techniques to bring ADCIRC model data to emergency managers so they can quickly identify, analyze, and disseminate information about high-risk areas. By incorporating the model data with other data sources, the researchers hope to enable informed decision-making about evacuations and other disaster management efforts.
The growing network of interconnected devices known as the Internet of Things (IoT) promises to make humans more productive, efficient, and healthy using sensors that monitor everything from the number of steps we take each day to the temperature of our homes. But a major concern about the IoT is its lack of security measures, which creates anxiety about exposing our most personal data. The problem becomes even more complicated when households deploy multiple smart devices—all capable of being hacked and giving up sensitive data. This project seeks to quantify the information being shared among different devices in a smart home environment and prevent secondary information leakage from IoT devices by enforcing privacy policies on all devices without affecting their use.