Song Han

Song Han is an Associate Professor in the Department of Electrical Engineering and Computer Science (EE and CS) at the Massachusetts Institute of Technology (MIT). His research focuses on Artificial Intelligence + Machine Learning, Computer Architecture, Integrated Circuits and Systems, and Systems and Networking. He is affiliated with the EECS Communication Lab and contributes to the department's academic and research initiatives. His work includes advancements in AI model training efficiency, edge computing, and AI-driven image generation. His contact information is available at songhan@mit.edu, and his profile can be accessed at https://www.eecs.mit.edu/people/song-han/.

Song Han is a researcher at the Massachusetts Institute of Technology, specializing in Computer Architecture, Deep Learning, and Computer Vision. His work focuses on optimizing neural networks for efficiency and performance, with applications in model compression, quantization, and hardware acceleration. Key contributions include Deep Compression, SqueezeNet, and EIE, which address challenges in reducing model size and computational costs. His research also explores efficient video understanding, neural architecture search, and multi-sensor fusion for autonomous systems. Han's work bridges theoretical advancements with practical deployment, emphasizing the integration of hardware-aware techniques in deep learning.