Data science aims to solve major global challenges such as: detecting and preventing diseases in human populations; revealing community structure in large social networks; protecting our elections from cyber threats and improving the resiliency of the electric grid. Unlike traditional applications in computational science and engineering, solving these social problems at scale often presents new challenges due to the sparsity and lack of locality in the data, the need for research into scalable algorithms and architectures, and developing frameworks for solving these real-world problems. world problems on powerful computers, and for improved models that capture the noise and bias inherent in the deluge of data streams. In this lecture, Bader discusses the opportunities and challenges in massive data science for applications in the social sciences, natural sciences and engineering. Institute for Data Science, New Jersey Institute of Technology This talk was delivered at a TEDx event in the TED conference format, but independently organized by a local community. For more information, visit https://www.ted.com/tedx

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