Jad Abou-chakra
Co-Founder & CTO at Sydekick
Queensland, Australia
Summary
👤
Senior🎓
Top SchoolJad Abou-chakra is a robotics researcher and engineering leader with eight years of experience bridging applied research and product-focused development, now serving as Co-Founder & CTO in Queensland, Australia. He builds foundation models and structured world representations for embodied intelligence and dexterous manipulation, drawing on a PhD at QUT where he explored Neural Radiance Fields and Gaussian Splatting for robotic 3D perception. His work spans academia and industry—from postdoctoral research and a foundation-model team at The AI Institute to hands-on engineering roles in intralogistics and control software—so he balances novel representation learning with practical system reliability. An active open-source contributor, he improved NeRF training and depth supervision in the high-profile instant-ngp project, reflecting a focus on accurate 3D supervision and usable tooling. He combines mechatronics and biomedical engineering roots with a consistent track record of turning complex perception ideas into deployable robotic capabilities.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at QUT (Queensland University of Technology)Bachelor of Engineering (Mechatronics) - Master of Engineering (Biomedical), Bachelor of Engineering (Mechatronics) - Master of Engineering (Biomedical) at University of New South Wales
Arabic, English
Github Skills (10)
cuda10
machine-learning10
serf10
computer-graphics10
3d-reconstruction10
cprogramming-language9
c-language9
computer-vision9
neural-network8
python6
Programming languages (10)
Github contributions (5)
NVlabs/instant-ngp
Jun 2022 - Sep 2022
Instant neural graphics primitives: lightning fast NeRF and more
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 6 commits, 11 PRs in 3 months
Contributions summary:Jad made several contributions focused on enhancing the neural radiance field (NeRF) training and rendering capabilities. They implemented depth loss control and added an option to render ground truth depth data, improving the model's supervision and visualization features. The user also modified the codebase to allow for configurable ground truth alpha values and addressed a memory alignment issue, demonstrating a focus on model accuracy and usability. These commits indicate the user's work in refining NeRF training and rendering, key components in the project's objective of instant neural graphics primitives.
realtimesigned-distance-functionsprimitivesreal-timecomputer-vision
jc211/JPLC
Jun 2019 - Feb 2021
Contributions:14 commits, 9 pushes in 1 year 8 months
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