Peyman Moghadam

Peyman Moghadam is a Principal Research Scientist at CSIRO and Professor (Adjunct) at the Queensland University of Technology (QUT). Currently, he is the head of Embodied AI Research Cluster at CSIRO, working at the intersection of Robotics and Machine learning. Most recently, he served as the Group Leader (Acting) of Robotic Perception and Autonomy at CSIRO. From 2020-2024, he led the Spatiotemporal AI portfolio at CSIRO’s Machine Learning and Artificial Intelligence (MLAI) Future Science Platform, advancing MLAI methods for scientific discovery in spatiotemporal data streams. In 2022, he was a Visiting Professor at ETH Zürich. Peyman is a Senior Member of IEEE, a Member of ACM, and an ACM Distinguished Speaker. He has held several editorial and leadership roles such as Senior Program Committee Member at AAAI-22, Session Chairs at robotics conference such as ICRA and IROS, tutorials Co-Chair at ICCV and ACCV, and Chair of few symposiums and workshops.  He has led several large-scale, multidisciplinary research portfolios and received numerous awards, including CSIRO's Julius Career Award, CSIRO Collaboration Medal, the Brisbane Lord Mayor’s Budding Entrepreneurs Award, and both National and Queensland iAwards for the best Research and Development. His current research interests include self-supervised learning, embodied AI, foundation models, robotics, and computer vision.

• 3D Geometry meets Learning
  In this lecture, I will give an overview of current Deep Learning methods for lidar-based place recognition. Place recognition aims to associate input lidar data to a global map or database of...
• Environmental Intelligence Unleashed: Harnessing the Power of Human-Robots Teaming
  As climate change and environmental degradation intensify, the need for scalable, intelligent solutions to monitor and protect ecosystems has never been more urgent. In this lecture, I explore the...
• Self-Supervised and Continual Representation Learning in Unstructured Environments

  In today's robotic applications, learning-based methods play a key role in tasks like (re)-localization, perception, navigation, and manipulation. However, these methods...

• The Future of Computing in the Era of Embodied Intelligence

  We are entering a new era where AI is no longer confined to the cloud, it is becoming embodied AI agents that are learning by...