Soomi Lee

Soomi Lee is a researcher in the fields of Robotics and Machine Learning with a focus on geometric control theory and deep learning applications. Her work bridges robotics and artificial intelligence, addressing challenges in robot learning and control systems. Recent contributions include advancements in SE(3)-equivariant methodologies and incremental scaling techniques for neural networks.

• Key research areas: Robotics, Machine Learning, Control Systems
• Notable collaborations with Jongeun Choi, Roberto Horowitz, and Joohwan Seo

Publications emphasize theoretical frameworks for robotic manipulation and neural architecture optimization, reflecting interdisciplinary expertise.