Georgia Tech is a prominent leader in the rapidly emerging field of big data, particularly in developing new methods to analyze or even transform large and complex data sets into value. For example, applying data analytics to social networks may help industries understand trends in consumer behaviors. Big data is also useful for addressing grand challenges in areas such as genomics, precision medicine, materials, manufacturing, and management of physical and cyber resources. It can be used to detect vulnerabilities in power grids or monitor protein interactions in cancer research.
Research in big data at Georgia Tech involves many campus units — spanning colleges, departments, and individual labs. Together, these researchers work to create new collaborative opportunities, strengthen partnerships with industry and government, and maximize the societal impact of the transformative big data research conducted at Georgia Tech. Core areas of big data research conducted through Tech’s centers, labs, and programs include machine learning, high performance computing, data analytics, digital signal processing, modeling and simulation, optimization, and foundations that support advanced programming.
The focus of big data research in CSE spans foundational topics (machine learning, data analytics, high performance computing, visualization) and multiple scientific domains (computational biology, materials science, and urban infrastructure, among others).
Interdisciplinary CSE Faculty specializing in data science and engineering research: