Overview
Background
Peyman Moghadam is an Associate Professor (Adjunct) at The University of Queensland (UQ), a Full Professor (Adjunct) at the Queensland University of Technology (QUT), and a Senior Principal Research Scientist at CSIRO, where he leads the Embodied AI Research Cluster. He has held several senior leadership roles at CSIRO, including Acting Group Leader of Robotic Perception and Autonomy, and previously led the Spatiotemporal AI portfolio in CSIRO’s Machine Learning and Artificial Intelligence Future Science Platform. His work spans embodied AI, self-supervised learning, foundation models and robotics. He has built and led large-scale multidisciplinary R&D programs, translating scientific advances into commercial technologies and impact across science and industry. In 2022, he was a Visiting Professor at ETH Zürich. Peyman has also contributed to the international robotics and AI community through editorial and leadership roles. He is co-chair of the IEEE RAS Robot Learning Technical Committee. He serves on the Organising Committee and as Partnership Chair for Robotics: Science and Systems (RSS) 2026 and 2027. He has also held Area Chair and Senior Program Committee roles across leading AI and robotics conferences, including AAAI, CoRL, ICRA, IROS and RSS. His contributions have been recognised through multiple awards, including the Australian Computer Society (ACS) Queensland Tech Award (2026), a medal for the development and commercialisation of handheld 3D mapping technology through GeoSLAM (2025), CSIRO’s Julius Career Award (2019), the CSIRO Collaboration Medal for research translation in AI and robotics with Google (2022), the Brisbane Lord Mayor’s Budding Entrepreneurs Award (2017), and both National and Queensland iAwards for Research and Development (2014).
Availability
- Dr Peyman Moghadam is:
- Available for supervision
Fields of research
Research interests
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Embodied Intelligence; Self-Supervised Learning; spatiotemporal learning
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Robotics, Computer Vision, Machine Learning, Deep Learning
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Beyond visible Spectrum Perception (Hyperspectral, Thermal)
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3D LiDAR SLAM; 3D Scene understanding; 3D Segmentation
Works
Search Professor Peyman Moghadam’s works on UQ eSpace
2022
Conference Publication
InCloud: incremental learning for point cloud place recognition
Knights, Joshua, Moghadam, Peyman, Ramezani, Milad, Sridharan, Sridha and Fookes, Clinton (2022). InCloud: incremental learning for point cloud place recognition. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23-27 October 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/iros47612.2022.9981252
2022
Conference Publication
A real-time edge-AI system for reef surveys
Li, Yang, Liu, Jiajun, Kusy, Brano, Marchant, Ross, Do, Brendan, Merz, Torsten, Crosswell, Joey, Steven, Andy, Tychsen-Smith, Lachlan, Ahmedt-Aristizabal, David, Oorloff, Jeremy, Moghadam, Peyman, Babcock, Russ, Malpani, Megha and Oerlemans, Ard (2022). A real-time edge-AI system for reef surveys. ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking, Sydney, NSW Australia, 17-21 October 2022. New York, NY, USA: ACM. doi: 10.1145/3495243.3558278
2022
Journal Article
What's in the black box? The false negative mechanisms inside object detectors
Miller, Dimity, Moghadam, Peyman, Cox, Mark, Wildie, Matt and Jurdak, Raja (2022). What's in the black box? The false negative mechanisms inside object detectors. IEEE Robotics and Automation Letters, 7 (3), 1-8. doi: 10.1109/lra.2022.3187831
2022
Conference Publication
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition
Vidanapathirana, Kavisha, Ramezani, Milad, Moghadam, Peyman, Sridharan, Sridha and Fookes, Clinton (2022). LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition. 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA United States, 23-27 May 2022. Piscataway, NJ United States: IEEE. doi: 10.1109/icra46639.2022.9811753
2022
Journal Article
A Survey on Terrain Traversability Analysis for Autonomous Ground Vehicles: Methods, Sensors, and Challenges
Borges, Paulo, Peynot, Thierry, Liang, Sisi, Arain, Bilal, Wildie, Matthew, Minareci, Melih, Lichman, Serge, Samvedi, Garima, Sa, Inkyu, Hudson, Nicolas, Milford, Michael, Moghadam, Peyman and Corke, Peter (2022). A Survey on Terrain Traversability Analysis for Autonomous Ground Vehicles: Methods, Sensors, and Challenges. Field Robotics, 2 (1), 1567-1627. doi: 10.55417/fr.2022049
2022
Conference Publication
Why object detectors fail: investigating the influence of the dataset
Miller, Dimity, Goode, Georgia, Bennie, Callum, Moghadam, Peyman and Jurdak, Raja (2022). Why object detectors fail: investigating the influence of the dataset. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, United States, 19-20 June 2022. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/CVPRW56347.2022.00529
2021
Conference Publication
Reduction of Feature Contamination for Hyper Spectral Image Classification
Mahendren, Sutharsan, Fernando, Tharindu, Sridharan, Sridha, Moghadam, Peyman and Fookes, Clinton (2021). Reduction of Feature Contamination for Hyper Spectral Image Classification. 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD Australia, 29 November - 1 December 2021. Piscataway, NJ United States: IEEE. doi: 10.1109/dicta52665.2021.9647153
2021
Journal Article
Elasticity meets continuous-time: map-centric dense 3D LiDAR SLAM
Park, Chanoh, Moghadam, Peyman, Williams, Jason, Kim, Soohwan, Sridharan, Sridha and Fookes, Clinton (2021). Elasticity meets continuous-time: map-centric dense 3D LiDAR SLAM. IEEE Transactions on Robotics, 38 (2), 978-997. doi: 10.1109/tro.2021.3096650
2021
Conference Publication
Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling
Vidanapathirana, Kavisha, Moghadam, Peyman, Harwood, Ben, Zhao, Muming, Sridharan, Sridha and Fookes, Clinton (2021). Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling. 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 30 May - 5 June 2021. Piscataway, NJ United States: IEEE. doi: 10.1109/icra48506.2021.9560915
2021
Conference Publication
Temporally coherent embeddings for self-supervised video representation learning
Knights, Joshua, Harwood, Ben, Ward, Daniel, Vanderkop, Anthony, Mackenzie-Ross, Olivia and Moghadam, Peyman (2021). Temporally coherent embeddings for self-supervised video representation learning. International Conference on Pattern Recognition (ICPR), Milan, Italy, 10-15 January 2020. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/icpr48806.2021.9412071
2021
Journal Article
Canopy density estimation in perennial horticulture crops using 3D spinning lidar SLAM
Lowe, Thomas, Moghadam, Peyman, Edwards, Everard and Williams, Jason (2021). Canopy density estimation in perennial horticulture crops using 3D spinning lidar SLAM. Journal of Field Robotics, 38 (4) rob.22006, 598-618. doi: 10.1002/rob.22006
2021
Conference Publication
Point cloud segmentation using sparse temporal local attention
Knights, Joshua, Moghadam, Peyman, Fookes, Clinton and Sridharan, Sridha (2021). Point cloud segmentation using sparse temporal local attention. Australasian Conference on Robotics and Automation (ACRA 2021), Online, 6-8 December 2021. Sydney, NSW, Australia: Australasian Robotics and Automation Association.
2020
Conference Publication
Multi-species Seagrass Detection and Classification from Underwater Images
Raine, Scarlett, Marchant, Ross, Moghadam, Peyman, Maire, Frederic, Kettle, Brett and Kusy, Brano (2020). Multi-species Seagrass Detection and Classification from Underwater Images. 2020 Digital Image Computing: Techniques and Applications (DICTA), Melbourne, VIC Australia, 29 November - 2 December 2020. Piscataway, NJ United States: IEEE. doi: 10.1109/dicta51227.2020.9363371
2020
Journal Article
Scalable learning for bridging the species gap in image-based plant phenotyping
Ward, Daniel and Moghadam, Peyman (2020). Scalable learning for bridging the species gap in image-based plant phenotyping. Computer Vision and Image Understanding, 197-198 103009, 103009. doi: 10.1016/j.cviu.2020.103009
2020
Journal Article
Spatiotemporal camera-LiDAR calibration: a targetless and structureless approach
Park, Chanoh, Moghadam, Peyman, Kim, Soohwan, Sridharan, Sridha and Fookes, Clinton (2020). Spatiotemporal camera-LiDAR calibration: a targetless and structureless approach. IEEE Robotics and Automation Letters, 5 (2) 8968361, 1556-1563. doi: 10.1109/lra.2020.2969164
2020
Conference Publication
Digital Twin for the Future of Orchard Production Systems
Moghadam, Peyman, Lowe, Thomas and Edwards, Everard (2020). Digital Twin for the Future of Orchard Production Systems. The Third International Tropical Agriculture Conference TropAg 2019 , Brisbane, QLD Australia, 11-13 November 2019. Basel, Switzerland: MDPI. doi: 10.3390/proceedings2019036092
2020
Conference Publication
Intelligent Systems for Commercial Application in Perennial Horticulture
Edwards, Everard and Moghadam, Peyman (2020). Intelligent Systems for Commercial Application in Perennial Horticulture. The Third International Tropical Agriculture Conference TropAg 2019 , Brisbane, QLD Australia, 11-13 November 2019. Basel, Switzerland: MDPI. doi: 10.3390/proceedings2019036059
2020
Journal Article
Thermal infrared imaging can differentiate skin temperature changes associated with intense single leg exercise, but not with delayed onset of muscle soreness
Stewart, Ian B., Moghadam, Peyman, Borg, David N., Kung, Terry, Sikka, Pavan and Minett, Geoffrey M. (2020). Thermal infrared imaging can differentiate skin temperature changes associated with intense single leg exercise, but not with delayed onset of muscle soreness. Journal of Sports Science and Medicine, 19 (3), 469-477.
2019
Journal Article
Robust photogeometric localization over time for map-centric loop closure
Park, Chanoh, Kim, Soohwan, Moghadam, Peyman, Guo, Jiadong, Sridharan, Sridha and Fookes, Clinton (2019). Robust photogeometric localization over time for map-centric loop closure. IEEE Robotics and Automation Letters, 4 (2) 8626520, 1768-1775. doi: 10.1109/lra.2019.2895262
2019
Conference Publication
Deep leaf segmentation using synthetic data
Ward, Daniel, Moghadam, Peyman and Hudson, Nicolas (2019). Deep leaf segmentation using synthetic data. British Machine Vision Conference 2018, BMVC 2018, Newcastle, United Kingdom, 3 - 6 September 2018. BMVA Press.
Supervision
Availability
- Dr Peyman Moghadam is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Available projects
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Self-Supervised Learning for 3D Multimodal Perception
Potential impact of deep learning is limited due to the lack of large, annotated, and high-quality datasets in domains of interest. Annotating such datasets is laborious, costly and time-consuming. This project proposes to develop self-supervised learning systems to extract and use the relevant context given by strong prior spatio-temporal models (e.g. dense 3D reconstructions) as supervisory signals in training. This new concept will investigate model structures that encodes spatio-temporal data, and show rapid adaptation of models to new domains (few-shot learning) using trained embeddings layers (self-supervised, or prior data).
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3D Scene Understanding
Simultaneous Localization and Mapping (SLAM) is a key enabling component of driverless vehicles, robotics and augmented reality. The SLAM goal is to estimate pose of the vehicle and simultaneously generate dense 3D scene reconstruction. At CSIRO we have developed and deployed state-of-the-art 3D LiDAR-based SLAM systems for the past decade. There is a new direction of research at the intersection of deep learning and geometry-based 3D SLAM. The research in this PhD programme will develop algorithms for geometry-based Deep Learning SLAM in a dynamic and unstructured environment. The PhD programme will involve the development of self or semi-supervised learning methods to address the significant weakness of most current deep networks.
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Hyperspectral Deep Learning
Hyperspectral cameras are currently undergoing a change from bulky and expensive equipment towards mobile and portable devices. A hyperspectral camera comprises of hundreds of bands with shortwave dependencies. Compared to conventional colour cameras (RGB bands), one could use these shortwave dependencies to design and develop a deep network for object classification, semantic segmentation and scene understanding. Both spectral and spatial relationship needs to be modelled by the deep networks simultaneously. The research in this PhD programme will develop algorithms for hyperspectral deep learning. The PhD programme will involve the development of learning with self-supervision algorithms to address the significant weakness of most current deep networks.
Supervision history
Current supervision
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Doctor Philosophy
Generalizing Implicit Representations for Robotics Manipulation of Articulated Objects
Associate Advisor
Other advisors: Associate Professor Mahsa Baktashmotlagh
Media
Enquiries
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