Cybenko has served as an advisor for the Defense Science Board and several other government panels and is the founding editor-in-chief of the IEEE Security & Privacy magazine. His current research interests are distributed information, control systems, and signal processing, with a focus on applications to security and infrastructure protection. He is known for proving the universal approximation theorem for artificial neural networks with sigmoid activation functions.
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