Peyman Moghadam
Head Of The Embodied AI Cluster at CSIRO
Australia
Summary
👤
Senior🎓
Top SchoolPeyman Moghadam is a multidisciplinary AI and robotics research leader with 11+ years of experience driving R&D from concept to impact across CSIRO’s Data61 and leading university partnerships. As Head of the Embodied AI Cluster he combines hands-on technical expertise in C++, Python, ROS, PyTorch, OpenCV and CUDA with portfolio-level strategy, having grown teams, secured >$1M in grants and delivered multiple MVPs. He has a strong academic footprint as adjunct faculty at QUT and UQ, co-supervising PhDs and winning competitive grants for self-supervised and spatiotemporal learning projects. His background spans thermal imaging, SLAM and real-time perception—commercialised early in his career with award-winning HeatWave 3D technology—showing a rare mix of productisation and foundational research. Known for building diverse, high-performing teams, he also pursues technical entrepreneurship and international collaborations (including visits to ETH Zürich and Bonn), reflecting both global perspective and applied impact.
11 years of coding experience
18 years of employment as a software developer
Emotional Intelligence In Leadership Professional Development Programs, Emotional Intelligence In Leadership Professional Development Programs at Harvard Extension SchoolPhD EEE, PhD EEE at Nanyang Technological University Singapore
English
Github Skills (41)
canopy10
lidar10
point-cloud10
contrastive-learning10
robotics9
representation-learning9
3d9
slam9
mmsegmentation8
command-line-tool8
deep-learning8
density-estimation8
action-recognition8
self-supervised-learning8
metric-learning8
Programming languages (3)
Github contributions (5)
csiro-robotics/InCloud
Mar 2022 - Oct 2022
[IROS2022] Official repository of InCloud: Incremental Learning for Point Cloud Place Recognition, Published in IROS2022 https://arxiv.org/abs/2203.00807
Contributions:4 commits, 5 pushes, 4 comments in 7 months
incremental-learningpoint-cloudcatastrophic-forgettingcontinual-learningdeep-learning
csiro-robotics/agscan3d
Dec 2020 - Sep 2021
This repository contains the code and datasets used in the paper "Canopy Density Estimation in Perennial HorticultureCrops Using 3D Spinning Lidar SLAM" https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22006
Contributions:5 commits, 3 pushes in 9 months
3ddensity-estimationlidarpoint-cloudcanopy
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