Song Han will join the Department of Electrical Engineering and Computer Science as an assistant professor in July 2018. He received his master’s degree and PhD in electrical engineering from Stanford. His research focuses on energy-efficient deep learning at the intersection of machine learning and computer architecture. Han proposed the deep compression algorithm, which can compress neural networks by 17 to 49 times while fully preserving prediction accuracy. He also designed the first hardware accelerator that can perform inference directly on a compressed sparse model, which results in significant speed increases and energy saving. His work has been featured by O’Reilly, TechEmergence, and The Next Platform, among others. Han has won best-paper awards at the International Conference on Learning Representations and the International Symposium on Field-Programmable Gate Arrays.