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
2014
Conference Publication
HeatWave: the next generation of thermography devices
Moghadam, Peyman and Vidas, Stephen (2014). HeatWave: the next generation of thermography devices. Conference on Thermosense - Thermal Infrared Applications XXXVI, Baltimore, MD, United States, 5-7 May, 2014. Bellingham, WA, United States: S P I E - International Society for Optical Engineering. doi: 10.1117/12.2053950
2013
Journal Article
HeatWave: a handheld 3D thermography system for energy auditing
Vidas, Stephen and Moghadam, Peyman (2013). HeatWave: a handheld 3D thermography system for energy auditing. Energy and Buildings, 66, 445-460. doi: 10.1016/j.enbuild.2013.07.030
2013
Conference Publication
Terrain classification using a hexapod robot
Best, Graeme, Moghadam, Peyman, Kottege, Navinda and Kleeman, Lindsay (2013). Terrain classification using a hexapod robot. Australasian Conference on Robotics and Automation, ACRA , Sydney, Australia, 2-4 December 2013. Australasian Robotics and Automation Association.
2013
Conference Publication
3D thermal mapping of building interiors using an RGB-D and thermal camera
Vidas, Stephen, Moghadam, Peyman and Bosse, Michael (2013). 3D thermal mapping of building interiors using an RGB-D and thermal camera. 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 6-10 May 2013. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/icra.2013.6630890
2013
Conference Publication
Line-based extrinsic calibration of range and image sensors
Moghadam, Peyman, Bosse, Michael and Zlot, Robert (2013). Line-based extrinsic calibration of range and image sensors. 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 6-10 May 2013. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/icra.2013.6631095
2013
Conference Publication
Ad hoc radiometric calibration of a thermal-infrared camera
Vidas, Stephen and Moghadam, Peyman (2013). Ad hoc radiometric calibration of a thermal-infrared camera. International Conference on Digital Image Computing - Techniques and Applications (DICTA), Hobart, Australia, 26-28 November, 2013. Piscataway, NJ, United States: IEEE. doi: 10.1109/dicta.2013.6691478
2012
Conference Publication
Pingu: a new miniature wearable device for ubiquitous computing environments
Ketabdar, Hamed, Moghadam, Peyman and Roshandel, Mehran (2012). Pingu: a new miniature wearable device for ubiquitous computing environments. Sixth International Conference on Complex, Intelligent, and Software Intensive Systems , Palermo, Italy, 4-6 July 2012. doi: 10.1109/cisis.2012.123
2012
Journal Article
Fast Vanishing-Point Detection in Unstructured Environments
Moghadam, Peyman, Starzyk, Janusz A. and Wijesoma, W. S. (2012). Fast Vanishing-Point Detection in Unstructured Environments. IEEE Transactions On Image Processing, 21 (1), 425-430. doi: 10.1109/tip.2011.2162422
2012
Conference Publication
Road direction detection based on vanishing-point tracking
Moghadam, Peyman and Dong, Jun Feng (2012). Road direction detection based on vanishing-point tracking. 25th IEEE\RSJ International Conference on Intelligent Robots and Systems (IROS), Algarve, Portugal, 7-12 October, 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/iros.2012.6386089
2012
Conference Publication
Magnetic signatures in air for mobile devices
Ketabdar, Hamed, Moghadam, Peyman, Naderi, Babak and Roshandel, Mehran (2012). Magnetic signatures in air for mobile devices. 14th ACM International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI '12), San Francisco, CA, United States, 21-24 September 2012. New York, NY, United States: ACM Press. doi: 10.1145/2371664.2371705
2012
Conference Publication
Assessing the vulnerability of magnetic gestural authentication to video-based shoulder surfing attacks
Shirazi, Alireza Sahami, Moghadam, Peyman, Ketabdar, Hamed and Schmidt, Albrecht (2012). Assessing the vulnerability of magnetic gestural authentication to video-based shoulder surfing attacks. 30th ACM Conference on Human Factors in Computing Systems, CHI 2012, Austin, TX, United States, 5 - 10 May 2012. New York, NY, USA: ACM. doi: 10.1145/2207676.2208352
2012
Conference Publication
Magi guitar: a guitar that is played in air!
Ketabdar, Hamed, Chang, Hengwei, Moghadam, Peyman, Roshandel, Mehran and Naderi, Babak (2012). Magi guitar: a guitar that is played in air!. 14th ACM International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI), San Francisco, CA, United States, 21 - 24 September 2012. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2371664.2371704
2011
Conference Publication
Computationally efficient navigation system for unmanned ground vehicles
Moghadam, Peyman, Salehi, Saba and Wijesoma, Wijerupage Sardha (2011). Computationally efficient navigation system for unmanned ground vehicles. 2011 IEEE Conference on Technologies for Practical Robot Applications, Woburn, MA, United States, 11-12 April 2011. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/tepra.2011.5753495
2010
Conference Publication
Collaborative Multi-Vehicle Localization and Mapping in High Clutter Environments
Moratuwage, M. D. P., Wijesoma, W. S., Kalyan, B., Patrikalakis, Nicholas M. and Moghadam, Peyman (2010). Collaborative Multi-Vehicle Localization and Mapping in High Clutter Environments. 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore Singapore, Dec 07-10, 2010. NEW YORK: IEEE.
2010
Conference Publication
Towards A Fully-Autonomous Vision-based Vehicle Navigation System in Outdoor Environments
Moghadam, Peyman, Wijesoma, Wijerupage Sardha and Moratuwage, M. D. P. (2010). Towards A Fully-Autonomous Vision-based Vehicle Navigation System in Outdoor Environments. 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore Singapore, Dec 07-10, 2010. NEW YORK: IEEE.
2009
Conference Publication
Online, Self-Supervised Vision-Based Terrain Classification in Unstructured Environments
Moghadam, Peyman and Wijesoma, Wijerupage Sardha (2009). Online, Self-Supervised Vision-Based Terrain Classification in Unstructured Environments. IEEE International Conference on Systems, Man and Cybernetics, San Antonio Tx, Oct 11-14, 2009. NEW YORK: IEEE. doi: 10.1109/ICSMC.2009.5345942
2008
Conference Publication
Improving Path Planning and Mapping Based on Stereo Vision and Lidar
Moghadam, Peyman, Wijesorna, Wijerupage Sardha and Feng, Dong Jun (2008). Improving Path Planning and Mapping Based on Stereo Vision and Lidar. 10th International Conference on Control, Automation, Robotics and Vision, Hanoi Vietnam, Dec 17-20, 2008. NEW YORK: IEEE. doi: 10.1109/ICARCV.2008.4795550
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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