Even as a child, Catherine Schuman liked the challenges fundamental to computer science. "I always liked puzzles, especially analytical puzzles," Schuman said. "Code is exactly that." Her father, a math teacher, and her brother, a programmer, introduced Schuman to computers early, but as a teenager, the Harriman, Tennessee, native also enjoyed biology. She kept a pamphlet detailing the mammalian genetics research at ORNL's "Mouse House" on her bookshelf. Today, her research on neuromorphic computing—a novel, energy-efficient technology that mimics biological neural networks like those in the human brain—allows Schuman to combine both interests. The speed, efficiency and localized networks of neuromorphic computers make them useful for a wide range of applications, from autonomous vehicle sensing to edge computing, in which data is stored and processed near where it is generated. Schuman became interested in nature-inspired machine learning techniques such as neural networks as an undergraduate studying computer science and math at the University of Tennessee, where she stayed for her Ph.D. in computer science. At ORNL in 2015, she accepted a Liane B. Russell Fellowship-named after the pioneering geneticist whose work inspired Schuman years earlier.
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机译：甚至在小时候，凯瑟琳·舒曼（Catherine Schuman）就喜欢计算机科学的根本挑战。舒曼说：“我一直喜欢拼图，特别是分析拼图。” “代码就是这样。”她的父亲是数学老师，兄弟是程序员，早在计算机时代就将Schuman引入了计算机，但是在十几岁的时候，田纳西州哈里曼（Harriman），本地人也很喜欢生物学。她在书架上保留了一本小册子，详细介绍了ORNL的“老鼠之家”的哺乳动物遗传学研究。如今，她对神经形态计算的研究（一种模仿人类大脑等生物神经网络的新型节能技术）使舒曼能够将这两种兴趣结合在一起。神经形态计算机的速度，效率和局部网络使其在从自动车辆感应到边缘计算的广泛应用中有用，在这些应用中，数据在生成位置附近进行存储和处理。舒曼（Schuman）在田纳西大学（University of Tennessee）攻读计算机科学和数学的本科生后，便对自然启发式的机器学习技术（例如神经网络）产生了兴趣，并在那里留校攻读博士学位。在计算机科学领域。在2015年的ORNL，她接受了Liane B. Russell奖学金，该奖学金以这位先驱遗传学家的名字命名，他的工作在几年前激发了舒曼。
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