Overview
Background
Peyman Moghadam is an Adjunct Associate Professor at the University of Queensland (UQ). He is a Principal Research Scientist at CSIRO Data61 as well as Professor (Adjunct) at the Queensland University of Technology (QUT). He leads the Embodied AI Research Cluster at CSIRO Data61, working at the intersection of Robotics and Machine learning. He is also the Spatiotemporal AI portfolio Leader at the CSIRO's Machine Learning and Artificial Intelligence (MLAI) Future Science Platform and oversees research and development of MLAI methods for scientific discovery in spatiotemporal data streams. In 2022, he served as a Visiting Professor at ETH Zürich. In 2019, he held a Visiting Scientist appointment at the University of Bonn. Peyman has led several large-scale multidisciplinary projects and won numerous awards, including CSIRO's Julius Career Award, National, and Queensland state iAward for Research and Development, CSIRO’s Collaboration Medal and the Lord Mayor’s Budding Entrepreneurs Award. His current research interests include self-supervised learning for robotics, embodied AI, 3D multi-modal perception (3D++), robotics, and computer vision.
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
2023
Conference Publication
Flashback for continual learning
Mahmoodi, Leila, Harandi, Mehrtash and Moghadam, Peyman (2023). Flashback for continual learning. IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2-6 October 2023. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/ICCVW60793.2023.00368
2023
Conference Publication
L3DMC: Lifelong learning using distillation via mixed-curvature space
Roy, Kaushik, Moghadam, Peyman and Harandi, Mehrtash (2023). L3DMC: Lifelong learning using distillation via mixed-curvature space. MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, 8–12 October 2023. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-43895-0_12
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.
Supervision
Availability
- Dr Peyman Moghadam is:
- Available for supervision
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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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