Alex Champandard
Software Engineer at creative.ai
Vienna, Austria
Summary
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RockstarAlex Champandard is a Vienna-based software engineer with 14 years of experience specialising in artificial intelligence and deep learning, co-founder of creative.ai and director of the nucl.ai conference. His background includes senior AI programming at Guerrilla Games and a strong open-source footprint—contributions range from deep-learning image projects like neural-enhance and neural-doodle to visualization work in Vispy and tooling for scikit-compatible neural nets. He combines research-grade ML modelling (perceptual loss, semantic style transfer, super-resolution) with production engineering skills such as CI, refactoring, and interactive visualization. Equally at home prototyping novel architectures and hardening code for collaboration, he brings a rare blend of game-AI experience and practical ML engineering. An understated strength is his knack for turning creative ideas into reproducible, well-structured codebases that others can build on.
14 years of coding experience
Github Skills (21)
scipy10
convolutional-neural-networks10
visualization10
python10
image-processing10
machine-learning10
generative-adversarial-network10
super-resolution10
numpy10
lasagne10
deep-learning10
neural-network10
computer-vision10
visualizations10
opengl10
Programming languages (10)
Github contributions (5)
alexjc/neural-doodle
Mar 2016 - Jul 2016
Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.)
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 185 commits, 10 PRs in 4 months
Contributions summary:Alex implemented a deep neural network model for image processing tasks within the repository. Their contributions included loading and configuring a pre-trained VGG model, creating layers for convolution and pooling, integrating semantic map data, and implementing optimization techniques to generate new images. The user's work involved computing content features and loss functions for the images.
waitstyle-transferartworksneural-networksdoodles
alexjc/neural-enhance
Oct 2016 - Jan 2017
Super Resolution for images using deep learning.
Role in this project:ML Engineer
ML EngineerContributions:5 releases, 101 commits, 25 PRs in 3 months
Contributions summary:Alex developed a super-resolution model for image enhancement using deep learning. Their initial commit introduced a working prototype with perceptual loss, a key element of the model. Subsequent commits involved adding residual blocks to the generator and integrating a discriminator network to improve the model's performance. Finally, they refactored the code to implement sub-pixel deconvolution layers for enhanced image quality.
deep-learningsuper-resolutioncomputervisionresolution
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