Waleed Abdulla
Engineering Leader · Applied AI, Agentic Engineering, 3D Perception
Waleed Abdulla
Engineering Leader · Applied AI, Agentic Engineering, 3D Perception
I lead an applied research organization at Apple, building and growing teams that develop generative AI and computer vision technologies for Vision Pro and other Apple products. My responsibilities span technical strategy, developing engineering leaders, and bringing research into products. I also lead and influence cross-organizational agentic engineering initiatives.
Previously, I founded a venture-backed startup that reached over 7 million users and created widely adopted open-source machine learning tools, including the Matterport Mask R-CNN implementation and HiddenLayer.
Selected Apple Work
My work at Apple (2018–present) is confidential, so I can't detail specific contributions. My organization develops algorithms in the areas covered by these public materials:
Luce: Relightable Gaussians for 3D Asset Generation (2026)
Generating relightable 3D assets from a single image, combining geometry and physically based materials.
Explore enhancements to visionOS object tracking (2026)
WWDC26 session: new capabilities in object tracking and spatial accessory input on visionOS, including tracking moving and handheld objects and building custom spatial accessories.
Iterate your spatial scenes faster with Reality Composer Pro 3 (2026)
WWDC26 session: building immersive scenes in Reality Composer Pro 3, with content, visual effects, lighting, interactivity, and AI-assisted iteration in the editor.
visionOS 26 introduces powerful new spatial experiences for Apple Vision Pro (2025)
Apple Newsroom: overview of the new spatial experiences and capabilities introduced in visionOS 26.
Explore spatial accessory input on visionOS (2025)
WWDC25 session: developer guide to spatial accessory input on visionOS.
Explore object tracking for visionOS (2024)
WWDC24 session: developer guide to object tracking on visionOS.
visionOS 2 brings new spatial computing experiences to Apple Vision Pro (2024)
Apple Newsroom: overview of visionOS 2, including spatial photos and new hand gestures.
Meet ARKit for spatial computing (2023)
WWDC23 session: introduction to ARKit on visionOS, covering world tracking, scene understanding, and hand tracking.
Open-Source Projects
Mask R-CNN for Object Detection and Segmentation
An implementation of Mask RCNN on Keras and TensorFlow. I built this during my work at Matterport and they graciously agreed to open source it. Since its release in November 2017, it has become one of the top instance segmentation models on TensorFlow and was used by thousands of developers in applications ranging from Kaggle competitions to Ph.D theses. It has earned around a thousand academic citations, plus 25K+ stars and 11K+ forks on GitHub.
HiddenLayer: Neural Network Visualization and Monitoring
Like TensorBoard, but simpler and runs directly in Jupyter notebook. It was adopted by Microsoft as one of the building blocks of their TensorWatch tool. Supports PyTorch, TensorFlow, and Keras.
Earlier Talks and Articles
Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow (2018)
A question I kept getting about my Mask RCNN implementation was: how do I use it with my own dataset? So I built a toy example and documented the whole process, including manually annotating a small dataset from scratch. The result was an article and code to automatically apply a color splash effect on images of balloons (i.e. convert the image to grayscale except for balloons, which stay colored).
Traffic Sign Recognition with TensorFlow (2017)
I occasionally talk about deep learning at local events. This is a talk I did for ACM (Association for Computing Machinery) about using TensorFlow to recognize traffic signs.