Martin Weigert
Dresden, Saxony, Germany
Summary
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RockstarMartin Chatton is an AI Software Engineer based in Lausanne with 12 years of experience building production-ready ML systems and scientific software. Trained at EPFL (BSc CS, MSc Data Science), he has moved between industry roles from data science at a commodities firm to ML engineering in sports and now AI engineering at Deeplink.ai, bringing a pragmatic bridge between research and product. He contributes to open-source projects like pyopencl and StarDist, improving numerical robustness and test coverage for scientific computing and object-detection pipelines. Comfortable with low-level performance details and high-level ML model behavior, he pays attention to edge cases and reproducibility—evident from tests for inplace operations, NMS, and class-aware predictions. Mart in combines hands-on coding, test automation, and domain-aware modelling to deliver reliable, well-tested AI components.
12 years of coding experience
Github Skills (14)
testing10
object-detection10
opencl10
computer-vision10
machine-learning10
pytest10
python10
numpy10
parallel-computing9
arrayobject9
data-analysis8
gpu8
performance-monitor7
performance-analysis7
Programming languages (9)
Github contributions (5)
stardist/stardist
Jul 2018 - Jun 2022
StarDist - Object Detection with Star-convex Shapes
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:4 releases, 17 reviews, 525 commits in 4 years
Contributions summary:Martin contributed to the development and testing of the StarDist object detection library. They implemented a non-maximum suppression test and improved the handling of edge cases with constant images. Furthermore, they added a test case for the accurate detection of object instances using the model on a 2D example image and integrated surface calculations into the rays object. Additionally, the user integrated new test cases and made it possible to use the model with a custom set of classes for the multi class prediction head.
object-detectionbioimage-analysiscell-segmentationnuclei-segmentationdeep-learning
inducer/pyopencl
Jan 2017 - Feb 2019
OpenCL integration for Python, plus shiny features
Role in this project:Backend Developer & Test Automation Engineer
Backend Developer & Test Automation EngineerContributions:10 commits, 1 PR, 7 comments in 2 years
Contributions summary:Martin primarily focused on enhancing the `pyopencl` library by implementing and testing inplace division functionality for the `Array` class. They added tests for inplace division with both scalars and arrays, ensuring compatibility with NumPy's behavior. Furthermore, the user addressed potential type casting issues and code formatting, demonstrating a commitment to code quality and accuracy within the OpenCL integration.
openclpythongpuheterogeneous-parallel-programmingnvidia
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