Lu Gan

Lu Gan is an Assistant Professor at the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology, where she joined in January 2024. She leads the Lu's Navigation and Autonomous Robotics (Lunar) Lab and serves as core faculty for the Institute for Robotics and Intelligent Machines (IRIM) at Georgia Tech.

Dr. Gan earned her educational degrees from prestigious institutions: B.S. in Automation from the University of Electronic Science and Technology of China (2013), M.S. in Control Engineering from Beihang University (2016), and both M.S. and Ph.D. in Robotics from the University of Michigan, Ann Arbor (2021, 2022). Prior to her appointment at Georgia Tech, she completed a two-year postdoctoral fellowship at the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT) and the Center for Autonomous Systems and Technologies at Caltech.

Her research focuses on robot perception, robot learning, and autonomous navigation, exploring the integration of computer vision, machine learning, estimation, probabilistic inference, kinematics, and dynamics to develop autonomous systems for ground, air, and space applications. Her work addresses challenges in highly unstructured environments under harsh sensing conditions for tasks including search and rescue, daily housework, and scientific exploration.

Dr. Gan's recent publications demonstrate strong trends in applying advanced machine learning techniques, particularly graph neural networks and multi-modal fusion approaches, to robotics challenges. Her work spans multiple subfields including legged locomotion, semantic scene understanding, contact perception, and thermal imaging applications for robotic systems operating in challenging environments.

• DAAD AInet Fellowship (2023)
• Rising Star in Data Science, University of Chicago (2022)
• National Graduate Scholarship of China (2015)
• IROS Best Paper Award on Agri-Robotics (Finalist, 2023)

Dr. Gan actively mentors students in the Lunar Lab, with current advisees working on cutting-edge robotics research. She serves as an Associate Editor for IEEE Transactions on Robotics (T-RO) and IEEE Robotics and Automation Letters (RA-L), and is Associate Co-Chair of the IEEE RAS Technical Committee for Computer & Robot Vision. Her lab has secured various research grants supporting their work in robotics and autonomous systems.

The Lunar Lab maintains strong collaborations with leading institutions and researchers in the robotics community, hosting seminars and participating actively in major conferences like ICRA. The lab's research directions include physics-informed learning for robotics, multi-modal 3D scene understanding, and heterogeneous multi-robot active SLAM.