Peyman Moghadam

Peyman Moghadam is an Adjunct Associate Professor at the University of Queensland (UQ) and a Principal Research Scientist at CSIRO Data61. He also holds an adjunct professorship at Queensland University of Technology (QUT). His roles include leading the Embodied AI Research Cluster at CSIRO Data61 and overseeing the Spatiotemporal AI portfolio within CSIRO's Machine Learning and Artificial Intelligence (MLAI) Future Science Platform. He has held visiting appointments at ETH Zürich (2022) and the University of Bonn (2019). His research focuses on self-supervised learning for robotics, embodied AI, 3D multi-modal perception, and computer vision applications in robotics and environmental science.

Education details are not explicitly provided in the text, but his professional experience and research output suggest advanced academic training in robotics, computer science, and machine learning. Awards include the CSIRO Julius Career Award, National and Queensland iAwards, and the Lord Mayor's Budding Entrepreneurs Award. He has led large-scale interdisciplinary projects and published extensively in top-tier journals and conferences.

His research themes span robotics perception, AI-driven environmental modeling, and sensor fusion. Notable contributions include benchmark datasets (e.g., WildScenes), novel algorithms for LiDAR place recognition, and geo-encoded transformers for plant species prediction. His work bridges robotics, machine learning, and real-world applications in agriculture, environmental monitoring, and autonomous systems.

• Awards: CSIRO Julius Career Award, Collaboration Medal, National/Queensland iAwards, Lord Mayor's Budding Entrepreneurs Award
• Grants & Projects: Led multidisciplinary projects in robotics, AI, and environmental science funded by CSIRO and industry collaborators
• Labs/Teams: Embodied AI Research Cluster (CSIRO Data61), Spatiotemporal AI portfolio (CSIRO MLAI)