Seeking Patterns in Big Data

University at Albany University at Albany Headlines

Computer Scientist Feng Chen selected for prestigious NSF CAREER Award

Assistant Professor of Computer Science Feng Chen

ALBANY, N.Y. (April 10, 2018) — As technology advances, the massive amount of data generated continues to increase faster than our ability to analyze it. This is the puzzle that Assistant Professor Feng Chen grapples with: We can improve the ability of different information systems to interact, but can we find a way to understand the avalanche of information produced in a coherent way?

Chen has been awarded a prestigious National Science Foundation (NSF) CAREER grant to do just that: develop a unified, theoretical framework for discovering complex patterns in big data in a multitude of tasks.

“Recent advances in sensing and computing techniques have led to a need for massive quantities of data to be aggregated from various information sources in fields such as science, engineering, and business that are naturally modeled in the form of big attributed networks,” said Chen, an assistant professor of Computer Science at the College of Engineering and Applied Sciences.

Big attributed networks (BANs) are ever-present in the modern world, with social media, computer networks, biological networks and enterprise systems among well-known examples. Different from traditional networks that are represented by nodes and links, BANs have the additional rich set of features on the nodes and/or edges. Effective analysis of BAN data relies on the simultaneous sparse feature selection and subgraph mining. However, as yet little has been done to bridge these two important research areas. This is where Chen’s research comes in.

“The focus of our project is therefore to unify a wide range of complex pattern discovery tasks and to resolve the fundamental modeling, algorithmic, and interactive challenges associated with ubiquitous BAN data in today’s big data era,” said Chen.

The research could benefit, for example, in the detection and forecasting of societal events (disasters, …

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