Deep learning engineer — computer vision and medical imaging, and the runtime underneath both.
I work at the layer where models meet hardware: input validation, bounds and integer-overflow bugs, and CUDA kernel memory safety in PyTorch core — alongside applied CV for medical and real-world video.
Major open-source libraries
Research codebases
In review: rmaphoh/RETFound#55 (extending the retinal foundation model to OCT drusen segmentation)
Internal work: MedVisBonn/EyeLab — a multi-modal annotation tool for retinal imaging data. Fixed bugs and added functionality on top of the open-source codebase for production use. Changes were kept in-house and not upstreamed, per organization and client requirements.
- Memory safety in tensor kernels — OOB reads, integer overflow in index math, missing shape and dtype guards. Turning sanitizer crashes into clear error messages.
- Computer vision — detection, segmentation, tracking, OCR; deepfake and face analysis.
- Medical imaging — retinal and volumetric segmentation, metrics, MONAI.
- Build and supply-chain hygiene — CVE remediation in packaging pipelines.
PyTorch · CUDA / C++ · OpenCV · MONAI · Python · ONNX · Docker · Git