Matthijs Hollemans
Technical Lead at The Audio Programmer
Breda, North Brabant, Netherlands
Summary
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Rockstar🎓
Top SchoolMatthijs Hollemans is a Technical Lead and seasoned software engineer with 15+ years of experience building audio software, ML integrations, iOS apps and games from the ground up. He runs his own indie app business and leads technical work at The Audio Programmer, combining product-minded craftsmanship with deep hands-on coding. His open-source contributions include adding MobileViT to Hugging Face transformers and multiple iOS ML/ CoreML projects—demonstrating rare cross-domain fluency between DSP, machine learning and mobile performance optimization. A hobbyist musician and producer, he brings practical audio expertise to his engineering work and is also the author of widely used iOS tutorials and game tooling. Based in Breda, Netherlands, he blends a long history of tinkering (from Amiga assembly games to modern ML) with a pragmatic focus on shipping performant, user-facing audio and mobile software.
15 years of coding experience
16 years of employment as a software developer
Bachelor of Science (B.Sc.) Electrical and Electronics Engineering, Bachelor of Science (B.Sc.) Electrical and Electronics Engineering at Hogeschool West-Brabant
Stackoverflow
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tensorflow
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machine-learning
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swift
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keras
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shader
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ios
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Github Skills (35)
uiimage10
objective-c10
algorithms10
pytorch10
ios10
python10
machine-learning10
data-structure10
coreml10
deep-learning10
data-structures10
object-detection10
spritekit10
transformer10
computer-vision10
Programming languages (8)
Github contributions (5)
hollance/YOLO-CoreML-MPSNNGraph
Jun 2017 - Nov 2019
Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
Role in this project:
Mobile Developer (iOS)
Mobile Developer (iOS)Contributions:25 commits, 2 PRs, 22 pushes in 2 years 5 months
Contributions summary:Matthijs primarily focused on implementing and refining a Tiny YOLO object detection model for iOS. Their contributions included adapting the model to the new MPS graph API, fixing compatibility issues with Xcode betas, and improving performance. Furthermore, the user made improvements to the object detection, and optimized the code for speed. The user's work integrated the model with Vision framework for inference.
apitinyyolo-tinycoremlios
hollance/CoreMLHelpers
Aug 2017 - Jan 2021
Types and functions that make it a little easier to work with Core ML in Swift.
Role in this project:Mobile Developer (iOS)
Mobile Developer (iOS)Contributions:38 commits, 10 PRs, 23 pushes in 3 years 5 months
Contributions summary:Matthijs primarily contributed to the development of the iOS demo application for the `coremlhelpers` library. They implemented core functionality, including image conversion using the library, adding UI elements such as a menu for selecting image processing methods, and adding non-maximum suppression functionality. The user also added methods to convert the `MLMultiArray` to a `UIImage`, demonstrating their focus on enhancing the image processing capabilities within the framework.
machine-learningcoremlswiftioscore-ml
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