Bodo Kaiser
Founder at QProduct Lab
Munich, Bavaria, Germany
Summary
👤
Senior🎓
Top SchoolBodo Kaiser is a founder and hands‑on technical leader with 13 years of experience turning product ideas into validated, cost‑effective hardware and software solutions from Munich. He blends deep physics and quantum optics expertise—now pursuing doctoral work in neutral‑atom quantum simulation—with practical SaaS and prototyping experience as Technical Lead at Ambivation and founder of QProduct Lab. Practically minded, he builds physical prototypes, automates lean development, and helps SMEs prioritize customer value over technical complexity. An active contributor to open‑source computer vision testing in the pytorch/vision repo, he focuses on robust image transform handling and test automation. That mix of academic rigor, product instincts, and QA craftsmanship means he favors experiments that decisively test theory rather than endless speculation.
12 years of coding experience
7 years of employment as a software developer
Ludwig Maximilian University of MunichManagement and Technology, Management and Technology at Technische Universität München
German, English
Stackoverflow
15,402reputation
1.5mreached
50answers
226questions
javascript
top-1%
deep-learning
top-5%
naming-conventions
top-5%
Github Skills (19)
pytorch10
javascript10
machine-learning10
computer-vision10
nodejs10
unit-testing9
python9
deep-learning9
naming-conventions9
numpy8
image-processing8
testing7
browserify6
coroutines6
mochajs6
Programming languages (14)
Github contributions (5)
pytorch/vision
Mar 2017 - Mar 2017
Datasets, Transforms and Models specific to Computer Vision
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:6 commits, 1 PR, 14 comments in 1 day
Contributions summary:Bodo primarily focused on testing the `vision` library's image transformation capabilities. They fixed linting errors, added support for different data types within the `ToTensor` and `ToPILImage` transforms, and corrected existing test functions. Their work involves modifying existing tests to ensure the correct behavior of the image transformations, including correct handling of different image formats and data types. These changes are focused on adding, modifying and testing the functionality of image transformations within the `vision` library.
pytorchvisiondeep-learningdatasetcomputer-vision
bodokaiser/node-swigify
Jan 2014 - Feb 2017
Contributions:14 commits, 1 PR, 2 pushes in 3 years 1 month
transformtwiginheritancebrowserify-transformswig
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