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�������� computer science professor Shaikh Arifuzzaman has been awarded a two-year, nearly $250,000 National Science Foundation grant to design fast, scalable algorithms capable of dissecting and analyzing complex dynamic data.

The modern technological revolution is data-driven, and scientific discovery in diverse domains relies on efficient mining, learning and analysis of these complex datasets, Arifuzzaman said. However, most social and technical sectors are now experiencing exponential growth of data, too large to be processed by conventional methods. Examples of such sectors include social media platforms like Facebook and Twitter, biological systems (e.g., protein interactions), business systems and infrastructure.

Adding to that complexity is that patterns and properties, inherent of such datasets, are dynamic and evolve over time. 

Arifuzzaman’s research aims at designing scalable methods for revealing dynamic behaviors of a socio-technical system by developing innovative algorithmic and computing techniques. The existing work on dynamic graphs shows only limited scalability for large-scale practical datasets, Arifuzzaman said. Arifuzzaman’s research will address such complexity by designing algorithms that are able to efficiently adapt to the increased demands of large-scale dynamic graph (network) data. 

“Graphs are a versatile scientific framework to represent and analyze biological, social and human-made complex systems,” Arifuzzaman said. “Such complex systems are inherently dynamic. For example, social interactions and human activities are intermittent; links appear and disappear in functional brain networks.” 

Despite “time” playing a central role in those systems, most of the classic studies on graphs are based on the topological properties of static graphs, which are graphs that do not change over time, Arifuzzaman said.

Algorithmic methods generated from this research will be applicable in understanding dynamic properties of various real-world systems, Arifuzzaman said. For instance, locating key neurons in cortical (brain) networks, route planning for time-varying traffic in infrastructure networks or tracking information propagation in social/contact networks, he said.

Arifuzzaman will collaborate with the Performance and Algorithms Group at Lawrence Berkeley National Laboratory using some of the most powerful supercomputers in the world that are located at the Berkeley Lab.