About Sven
I am a mathematician and physicist specialized in computer vision and 3D/2D image processing using C++ and Python. I utilize deep learning methods and develop for embedded devices. I have already worked on projects with well-known companies such as Zalando, Infineon, Testo, and Gerhard Schubert.
My involvement in projects such as customer identification via body and face using AI, bacterial detection in microscopic images, breast cancer detection in MRI images, text recognition, robotic arm optimization, and intelligent trunk opening systems demonstrate my ability to develop innovative and disruptive solutions showing my high-level technical skills and in-depth understanding of AI systems.
German
Native or bilingual
English
Fluent
Can work on-site
Berlin (up to 50km)
Experience
- Infineon Technologies AG & intive GmbHData Science embedded AI devTECHNovember 2022 - March 2023 (5 months)Berlin, GermanyProject: Smart Trunk OpenerAs a Senior AI Engineer, I led the development of a Smart Trunk Opener project, which aimed to accurately predict kick gestures for hands-free trunk opening using the BGT24***** radar based on the Doppler effect and using Neural Networks on embedded devices.- Developed algorithms to detect abnormal signals and validate them as kicks using Python and Matlab- Created NN models for better predictions, and optimised them using TensorRT for efficient use on embedded devices.- Improved data reception and pre-processing on the radar using the C programming language.- Compiled complete production code as C code using Atmel Studio and deployed it on the embedded system.
- Testo SE & Co. KgaAComputer Vision Software devMECHANICAL ENGINEERINGJune 2022 - January 2023 (8 months)Lenzkirch, GermanyProject: Bacteria DetectionDevelopment of a bacteria detection project using microscopic images, achieving 99% accuracy in detecting the location and number of bacteria.- Developed and optimized computer vision algorithms using OpenCV techniques to detect bacteria and their numbers with high accuracy.- Trained yolo5 deep neural networks to extract relevant bacteria bounding boxes using IoUs.- Accelerated the AI on Jetson Nano by converting to TensorRT (CUDA) and developed the final C++ code interference with pre- and post-processors.- Optimized the yolo network decoder from Python to C++ and programmed tensors directly on the GPU using CUDA C++.- Reduced the size of the AI utilizing Knowledge Distillation techniques to achieve high performance processing on the Jetson Nano.
- DAYIANA GmbHComputer Vision SpecialistCIVIL ENGINEERINGJanuary 2022 - June 2022 (6 months)Berlin, GermanyProject: Breast Cancer Detection on MRIDevelopment of a neural network-based algorithm to detect, classify and segment breast tumors in mammography X-ray images to improve radiologists' performance in breast cancer screening.- Used segmentation techniques to train Mask R-CNNs to detect the tumors.- Improving the inferenc via pretraining of the Mask R-CNN with the bounding box mask of the Yolo detector. Then training on qualitatively better but few masked data.- Integrated sensitivity and precision metrics, and achieved state-of-the-art results.- Deployed the finished code on AWS endpoint for professional level breast cancer detection.
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Education
- MasterHumboldt-Universität zu Berlin2019Master of Science in Mathematics, M.Sc. Final grade: 1.0, equivalent to a GPA of 4.0 Thesis: "Optimal control of singularly perturbed parabolic Partial Differential Equations interpreted as regularized continuous analogues of Deep Neural Networks". Relevant coursework: Statistics; Machine Learning; Neural Networks; Optimization; Partial Differential Equations; Finite Element Methods
- German AbiturManfred-von-Ardenne-Gymnasium2013German Abitur Final grade: 1.3, equivalent to a GPA of 3.7 Focus: Mathematics, Physics
Certifications
- Azure AI FundamentalsMicrosoft Certified2023
- Azure AI Engineer AssociateMicrosoft Certified2023