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
-
Embodied Intelligence; Self-Supervised Learning; spatiotemporal learning
-
Robotics, Computer Vision, Machine Learning, Deep Learning
-
Beyond visible Spectrum Perception (Hyperspectral, Thermal)
-
3D LiDAR SLAM; 3D Scene understanding; 3D Segmentation
Works
Search Professor Peyman Moghadam’s works on UQ eSpace
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.
2018
Conference Publication
Non-rigid reconstruction with a single moving RGB-D camera
Elanattil, Shafeeq, Moghadam, Peyman, Sridharan, Sridha, Fookes, Clinton and Cox, Mark (2018). Non-rigid reconstruction with a single moving RGB-D camera. 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 20-24 August, 2018. Piscataway, NJ, United States: IEEE. doi: 10.1109/icpr.2018.8546201
2018
Conference Publication
Elastic LiDAR fusion: Dense map-centric continuous-time SLAM
Park, Chanoh, Moghadam, Peyman, Kim, Soohwan, Elfes, Alberto, Fookes, Clinton and Sridharan, Sridha (2018). Elastic LiDAR fusion: Dense map-centric continuous-time SLAM. 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 21-25 May 2018. Piscataway, NJ, United States: IEEE. doi: 10.1109/icra.2018.8462915
2018
Conference Publication
Skeleton driven non-rigid motion tracking and 3D reconstruction
Elanattil, Shafeeq, Moghadam, Peyman, Denman, Simon, Sridharan, Sridha and Fookes, Clinton (2018). Skeleton driven non-rigid motion tracking and 3D reconstruction. 2018 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2018), Canberra, Australia, 10-13 December, 2018. Piscataway, NJ, United States: IEEE. doi: 10.1109/dicta.2018.8615797
2017
Conference Publication
Plant disease detection using hyperspectral imaging
Moghadam, Peyman, Ward, Daniel, Goan, Ethan, Jayawardena, Srimal, Sikka, Pavan and Hernandez, Emili (2017). Plant disease detection using hyperspectral imaging. 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, NSW Australia, 29 November - 1 December 2017. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA.2017.8227476
2017
Conference Publication
Probabilistic surfel fusion for dense LiDAR mapping
Park, Chanoh, Kim, Soohwan, Moghadam, Peyman, Fookes, Clinton and Sridharan, Sridha (2017). Probabilistic surfel fusion for dense LiDAR mapping. 16th IEEE International Conference on Computer Vision (ICCV 2017), Venice, Italy, 22-29 October 2017. New York, NY, United States: Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/ICCVW.2017.285
2017
Conference Publication
Plant disease detection using hyperspectral imaging
Moghadam, Peyman, Ward, Daniel, Goan, Ethan, Jayawardena, Srimal, Sikka, Pavan and Hernandez, Emili (2017). Plant disease detection using hyperspectral imaging. International Conference on Digital Image Computing - Techniques and Applications (DICTA), Sydney, Australia, 29 November - 1 December 2017. New York, NY, United States: IEEE.
2016
Journal Article
SAGE: Semantic Annotation of Georeferenced Environments
Moghadam, Peyman, Evans, Benjamin and Duff, Elliot (2016). SAGE: Semantic Annotation of Georeferenced Environments. Journal of Intelligent and Robotic Systems, 83 (3-4), 635-648. doi: 10.1007/s10846-015-0302-3
2016
Conference Publication
Real-Time Stabilisation for Hexapod Robots
Hoerger, Marcus, Kottege, Navinda, Bandyopadhyay, Tirthankar, Elfes, Alberto and Moghadam, Peyman (2016). Real-Time Stabilisation for Hexapod Robots. 14th International Symposium on Experimental Robotics (ISER), Morocco, 15-18 June 2014 . Heidelberg, Bermany: Springer. doi: 10.1007/978-3-319-23778-7_48
2016
Conference Publication
Terrain characterisation and gait adaptation by a hexapod robot
Williamson, Dylan, Kottege, Navinda and Moghadam, Peyman (2016). Terrain characterisation and gait adaptation by a hexapod robot. Australasian Conference on Robotics and Automation, ACRA, Brisbane, Australia, 5-7 December 2016. Australasian Robotics and Automation Association.
2015
Journal Article
Real-time mobile 3D temperature mapping
Vidas, Stephen, Moghadam, Peyman and Sridharan, Sridha (2015). Real-time mobile 3D temperature mapping. IEEE Sensors Journal , 15 (2), 1145-1152. doi: 10.1109/JSEN.2014.2360709
2015
Conference Publication
3D medical thermography device
Moghadam, Peyman (2015). 3D medical thermography device. SPIE Conference on Thermosense - Thermal Infrared Applications XXXVII, Baltimore, MD, United States, 20-23 April, 2015. Bellingham, WA, United States: S P I E - International Society for Optical Engineering. doi: 10.1117/12.2177880
2015
Conference Publication
Coverage-based next best view selection
Cunningham-Nelson, Samuel, Moghadam, Peyman, Roberts, Jonathan and Elfes, Alberto (2015). Coverage-based next best view selection. Australasian Conference on Robotics and Automation, ACRA, Canberra, Australia, 2-4 December 2015. Australasian Robotics and Automation Association.
Supervision
Availability
- Dr Peyman Moghadam is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Available projects
-
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).
-
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.
-
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
-
Doctor Philosophy
Generalizing Implicit Representations for Robotics Manipulation of Articulated Objects
Associate Advisor
Other advisors: Associate Professor Mahsa Baktashmotlagh
Media
Enquiries
For media enquiries about Dr Peyman Moghadam's areas of expertise, story ideas and help finding experts, contact our Media team: