Brain-Inspired Computing

Catherine Schuman, Oak Ridge National Laboratory

By mimicking the neural systems of the human brain, neuromorphic computing aims to turbocharge AI applications and to operate at a fraction of the power required by conventional chips. MIT Technology Review AI Reporter Karen Hao and Katie Schuman, Research Scientist at Oak Ridge National Laboratory, examine if this radical departure from conventional computing is the future of AI.

Catherine Schuman, Research Scientist, Oak Ridge National Laboratory

Katie Schuman is a research scientist in computational data analytics at Oak Ridge National Laboratory. Katie received her doctorate in computer science in 2015 from the University of Tennessee, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. She is continuing her study of models and algorithms for neuromorphic computing, as well as other topics in artificial intelligence and machine learning, as part of her work at ORNL. Katie is also an adjunct assistant professor at the University of Tennessee, where she, along with four other professors at UT, leads a neuromorphic research team made up of more than 25 faculty members, graduate student researchers, and undergraduate student researchers.

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