Shuran Song

Shuran Song is an Assistant Professor of Electrical Engineering at Stanford University, where her research lies at the intersection of robotics, computer vision, and machine learning. Her work focuses on developing generalizable visuomotor policies, large-scale robot learning systems, and manipulation algorithms that operate robustly in unstructured real-world environments. Song’s group explores topics such as diffusion-based visuomotor policies, active perception, contact-rich manipulation, policy adaptation, and data-driven simulation-to-real transfer, with applications in humanoid control, household robotics, and autonomous manipulation. Before joining Stanford, she was a faculty member at Columbia University. She received her PhD in Computer Science from Princeton University and her BEng. from HKUST, with prior research experience at Google Brain. Song’s research has received numerous awards, including Best Paper Awards at RSS and T-RO, Best System Paper Awards at CoRL and RSS, the NSF CAREER Award, and a Sloan Research Fellowship. Her work has been widely recognized across academia and industry, and she collaborates broadly on advancing scalable robot learning.