Philipp Kainz
CEO, Co-Founder at KOLAIDO
Thal, St. Gallen, Switzerland
Summary
👤
Senior🎓
Top SchoolPhilipp Kainz is a serial founder and technology leader with 11 years of experience at the intersection of medical image analysis, computer vision, and product-driven startups. He has founded and led multiple companies (including KML Vision, KOLAIDO and Verdarion) while also teaching as a guest lecturer at technical universities, bridging academic research and commercial product development. Trained as a doctor of medical science with a background in eHealth and health-care engineering, he brings deep domain expertise to AI-driven healthcare solutions and a track record of moving research into deployable systems. An active developer in open-source ML tooling, he contributed substantive fixes and usability enhancements to the popular hyperas wrapper for Keras/Hyperopt, enabling more robust hyperparameter workflows. Based in Thal, St. Gallen, he combines entrepreneurial grit with hands-on engineering and an appetite for continuous movement and iteration.
11 years of coding experience
9 years of employment as a software developer
Master of Science (MSc) eHealth, Master of Science (MSc) eHealth at FH JoanneumDoctor of Medical Science (Dr. scient. med.) Medical Image Analysis Computer Vision, Doctor of Medical Science (Dr. scient. med.) Medical Image Analysis Computer Vision at Medizinische Universität Graz
English, German
Github Skills (8)
unit-testing10
hyperparameter-optimization10
keras10
hyperopt10
python10
jupyter6
nbconvert5
nib5
Programming languages (8)
Github contributions (5)
maxpumperla/hyperas
May 2017 - Jun 2017
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:21 commits, 5 PRs, 16 comments in 16 days
Contributions summary:Philipp contributed to the `hyperas` project, primarily focusing on enhancing its functionality and resolving issues. Their work included adding support for import aliases in the utility functions, fixing failing tests, and updating dependencies. They also introduced new utility functions for evaluating hyperopt space and enabled the resumption of training with pickled trial objects.
hyperparameterdeep-learningoptimizationhyperparametersmachine-learning
kmlvision/openslide
Jan 2019 - Jan 2022
C library for reading virtual slide images
Contributions:3 releases, 3 PRs, 6 pushes in 3 years 1 month
slideslide-imagesc-library
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