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Mingda Xu is a Research Fellow working in Professor Stephen Gould's group at Australian National University. He is involved in collaborative projects that engage with industry partners like Seeing Machines Ltd. His research interests focus on aspects of autonomous systems and the full robot stack, with an emphasis on machine learning and computer vision problems. He has worked on projects including unsupervised learning of long-form videos, which was nominated for a paper award at CVPR 2024, and feature shaping methods for out-of-distribution detection, presented at ICLR 2024. His ongoing research includes model-based control, specifically discrete-time optimal control and differentiable optimization in robotics. Xu's earlier work for his PhD focused on efficient visual localization algorithms for mobile robots that are robust to appearance changes under varying viewpoints. This included the development of state-estimation algorithms utilizing recursive Bayesian filtering and nonlinear optimization methods alongside modern deep learning approaches for visual place recognition.
Australian National University • Canberra
Involved in research projects related to machine learning and autonomous systems.
Requirements are standardized across most Master of Science and Arts programs within the College of Science and College of Arts & Social Sciences.