Alexander Mordvintsev
Senior Research Scientist at Google
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolAlexander Mordvintsev is a Senior Research Scientist at Google Research with 13 years of experience at the intersection of machine learning, computer vision and computer graphics, best known for creating DeepDream. He focuses on neural network visualization, interpretation and emergent phenomena, bringing a visual-first mindset to making complex models interpretable. His open-source contributions include work on lucid (neural network interpretability tooling) and practical CV additions to OpenCV, showing both research depth and engineering pragmatism. Based in Zurich, he holds a Master’s in Computer Science from ITMO and often bridges research prototypes and usable tooling—evident in utilities for 3D meshes and Colab OpenGL helpers that make advanced visualization accessible.
13 years of coding experience
Master's degree, Computer Science, Master's degree, Computer Science at Saint-Petersburg State University Information Technologies, Mechanic and Optics (University ITMO)
Github Skills (14)
computer-vision10
machine-learning10
opencv10
c-language10
visualization10
tensorflow10
cprogramming-language10
visualizations10
opengl10
python10
image-processing10
deeplearning-ai9
deep-learning9
jupyter-notebook8
Programming languages (9)
Github contributions (5)
tensorflow/lucid
Jan 2018 - Apr 2020
A collection of infrastructure and tools for research in neural network interpretability.
Role in this project:
ML Engineer
ML EngineerContributions:69 commits, 24 PRs, 32 pushes in 2 years 3 months
Contributions summary:Alexander's commits primarily focused on modifying and integrating code related to image processing and visualization, including the creation of setup files for the project. They fixed compatibility issues for Python 3 and removed a dependency on scipy.ndimage. They also introduced utilities for working with 3D meshes and a Google Colab OpenGL helper, showing strong involvement in making the codebase more functional for machine learning related tasks.
pytorchinterpretabilitydeep-learningjupyter-notebookinfrastructure
opencv/opencv
Jul 2012 - Jul 2014
Open Source Computer Vision Library
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:29 commits, 1 comment in 2 years
Contributions summary:Alexander contributed to the OpenCV library by implementing new samples and correcting existing code. Their work included adding a MOSSE tracking sample, a deconvolution sample, and a texture flow estimation sample. The user also added new features like a multitarget planar tracking in the `plane_tracker.py` and augmented reality overlay functionality. Further contributions include fixing minor issues by correcting spelling mistakes in existing code, and making improvements to the feature homography sample.
pythonvisiondeep-learningc-plus-pluscomputer-vision
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