Adjunct Professor
Peyman Moghadam
Faculty of Engineering,
School of Electrical Engineering & Robotics
Biography
Peyman Moghadam is an Adjunct Professor in the Speech, Audio, Image and Video Technologies group within the Science and Engineering Faculty at QUT. He is a Principal Research Scientist and the Research Team Leader at Robotics and Autonomous Systems, CSIRO, Data61. He received his PhD in Robotics from the Nanyang Technological University (Singapore) in 2011. Before joining CSIRO, he has worked in number of top leading organizations such as the Deutsche Telekom Laboratories (Germany), the Singapore-MIT Alliance for Research and Technology (Singapore). His current research interests focus on Self-Supervised Learning and Embodied Intelligence for Robotics. Professor Moghadam has led several large-scale multidisciplinary projects and he has won numerous awards for his innovations including CSIRO Julius Career award, National and Queensland iAward for Research and Development, the Lord Mayor’s Budding Entrepreneurs Award.Personal details
Positions
- Adjunct Professor
Faculty of Engineering,
School of Electrical Engineering & Robotics
Keywords
Deep Learning, Robotics, Embodied Intelligence, Self-Supervised Learning, Hyperspectral Perception, Thermal Perception, Machine Learning, SLAM, Multi-modal Learning, 3D multimodal Perception
Research field
Artificial intelligence, Other engineering, Other information and computing sciences
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2020
Qualifications
- PhD
Professional memberships and associations
For more information on Adjunct Prof Peyman Moghadam research activities visit website:
https://research.csiro.au/robotics/
We are currently seeking outstanding candidates to undertake PhD research in Deep Learning applied to Robotics visit website for more details:
https://research.csiro.au/robotics/work-with-us/undergrad-masters-and-phd-students/phds/
Experience
Research Areas
- Robotics, Computer Vision, Machine Learning, Deep Learning.
- Beyond visible Spectrum Perception (Hyperspectral, Thermal).
- Embodied Intelligence, Self-Supervised Learning, Multi-modal Learning
Research applications include:
- Agriculture
- Health/Sports
- Manufacturing
Publications
- Mohamed, S., Haghighat, M., Fernando, T., Sridharan, S., Fookes, C. & Moghadam, P. (2024). FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pre-Training. IEEE Transactions on Geoscience and Remote Sensing, 62. https://eprints.qut.edu.au/245518
- Knights, J., Hausler, S., Sridharan, S., Fookes, C. & Moghadam, P. (2024). GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors. IEEE Robotics and Automation Letters, 9(1), 915–922. https://eprints.qut.edu.au/245271
- Haghighat, M., Moghadam, P., Mohamed, S. & Koniusz, P. (2024). Pre-training with Random Orthogonal Projection Image Modeling. Proceedings of the Twelfth International Conference on Learning Representations (ICLR). https://eprints.qut.edu.au/246732
- Vidanapathirana, K., Moghadam, P., Sridharan, S. & Fookes, C. (2023). Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization. IEEE Robotics and Automation Letters, 8(5), 2494–2501. https://eprints.qut.edu.au/238892
- Knights, J., Vidanapathirana, K., Ramezani, M., Sridharan, S., Fookes, C. & Moghadam, P. (2023). Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), 11322–11328. https://eprints.qut.edu.au/242816
- Park, C., Moghadam, P., Williams, J., Kim, S., Sridharan, S. & Fookes, C. (2022). Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM. IEEE Transactions on Robotics, 38(2), 978–997. https://eprints.qut.edu.au/232911
- Miller, D., Moghadam, P., Cox, M., Wildie, M. & Jurdak, R. (2022). What's in the Black Box? The False Negative Mechanisms Inside Object Detectors. IEEE Robotics and Automation Letters, 7(3), 8510–8517. https://eprints.qut.edu.au/232511
- Knights, J., Moghadam, P., Ramezani, M., Sridharan, S. & Fookes, C. (2022). InCloud: Incremental Learning for Point Cloud Place Recognition. Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 8559–8566. https://eprints.qut.edu.au/237778
- Vidanapathirana, K., Ramezani, M., Moghadam, P., Sridharan, S. & Fookes, C. (2022). LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition. Proceedings of the 39th IEEE International Conference on Robotics and Automation (ICRA 2022), 2215–2221. https://eprints.qut.edu.au/234466
- Stewart, I., Moghadam, P., Borg, D., Kung, T., Sikka, P. & Minett, G. (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. https://eprints.qut.edu.au/200728
QUT ePrints
For more publications by Peyman, explore their research in QUT ePrints (our digital repository).
Supervision
Current supervisions
- Lifelong Collaborative Learning
PhD, External Supervisor
Other supervisors: Professor Sridha Sridharan, Professor Clinton Fookes, Dr Tharindu Fernando Warnakulasuriya - Improving Fine-Grained Understanding of Point Clouds Using Spatio-Temporal Priors
PhD, External Supervisor
Other supervisors: Professor Clinton Fookes, Professor Sridha Sridharan - Self-Supervised Learning for 3D Multimodal Perception
PhD, External Supervisor
Other supervisors: Professor Clinton Fookes, Professor Sridha Sridharan, Dr Tharindu Fernando Warnakulasuriya - Self-Supervised Neural Fields For Hyperspectral Learning
PhD, External Supervisor
Other supervisors: Dr Kien Nguyen Thanh, Professor Sridha Sridharan, Professor Clinton Fookes - Deep Spatial-Spectral Representation Learning for Hyperspectral Data
PhD, External Supervisor
Other supervisors: Professor Sridha Sridharan, Professor Clinton Fookes, Dr Tharindu Fernando Warnakulasuriya, Dr Maryam Haghighat
Completed supervisions (Doctorate)
The supervisions listed above are only a selection.