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Song Han
Assistant Professor at Massachusetts Institute of Technology
Song Han is an assistant professor in MIT’s Department of Electrical Engineering and Computer Science. He holds a PhD degree from Stanford University and a bachelor’s degree from Tsinghua University. His research primarily centers around efficient deep learning computing, notably known for his contributions in the development of the 'deep compression' technique to drastically reduce neural network size while maintaining accuracy.
One of Song Han's significant hardware implementations is the 'efficient inference engine,' focusing on leveraging pruning and weight sparsity in deep learning accelerators. His work on hardware-aware neural architecture search has gained recognition from prestigious institutions like MIT, Qualcomm, and publications such as VentureBeat and IEEE Spectrum. This research has been integrated into PyTorch and AutoGluon, receiving numerous awards in low-power computer vision contests at renowned AI conferences including CVPR’19, ICCV’19, and NeurIPS’19.
Song Han's exceptional contributions to the field have resulted in several accolades including Best Paper awards at ICLR’16 and FPGA’17, along with distinguished honors such as the Amazon Machine Learning Research Award, SONY Faculty Award, and Facebook Faculty Award. Notably, he was named among MIT Technology Review's '35 Innovators Under 35' for his groundbreaking work on the 'deep compression' technique, enabling efficient AI programs on low-power mobile devices. He has also been recognized with the NSF CAREER Award for his research on efficient algorithms and hardware for accelerated machine learning.
In terms of education, Song Han completed his Bachelor of Science in Electrical Engineering at Tsinghua University and further pursued studies in Computer Engineering at the University of Toronto. He obtained his Doctor of Philosophy in Philosophy from Stanford University. Throughout his career, Song has been associated with esteemed organizations including his current role as an Assistant Professor at MIT. He has previously held positions as a Research Scientist Intern at Facebook, Teaching Assistant at Stanford University, Software Engineer Intern at Google, Digital Engineer Intern at Apple, Research Assistant at Stanford University, Software Engineer Intern at St Electronics, and Software Research Intern at Microsoft.