Robin Schirrmeister
Chief Scientific Officer at Alpica
Freiburg im Breisgau, Baden-Württemberg, Germany
Summary
👤
Senior🎓
Top SchoolRobin Schirrmeister is a Chief Scientific Officer and neuroscientist-engineer with 13 years of experience applying deep learning to EEG/MEG and medical problems, now translating cutting-edge research into practical AI products at Alpica. He combines a strong academic track record—PhD-level research on brain-signal decoding, generative reversible networks, and anomaly detection—with industry stints including Meta and Bosch AI, bridging theory and production. An active contributor to the braindecode open-source project, he has improved memory-efficient prediction handling and device compatibility for EEG deep-learning pipelines. Comfortable working across unsupervised and (semi-)supervised methods, he focuses on models that both leverage more data and increase human interpretability. Based in Freiburg, Germany, he pairs rigorous technical depth with a talent for turning novel ML ideas into usable, cost-conscious solutions for enterprises.
13 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BS, Computer Science, 1.1 (GPA-equivalent=3.9), Bachelor of Science - BS, Computer Science, 1.1 (GPA-equivalent=3.9) at The University of FreiburgMaster of Science (M.Sc.), Computer Science, 1.0 (GPA-equivalent=4.0), Master of Science (M.Sc.), Computer Science, 1.0 (GPA-equivalent=4.0) at Albert-Ludwigs-Universität Freiburg im Breisgau
Github Skills (9)
neural-network10
pytorch10
deeplearning-ai10
deep-learning10
python10
convolutional-neural-networks9
scikit8
scikit-learn8
testing7
Programming languages (9)
Github contributions (5)
braindecode/braindecode
Jan 2022 - Oct 2022
Deep learning software to decode EEG, ECG or MEG signals
Role in this project:
ML Engineer
ML EngineerContributions:4 releases, 216 reviews, 7 commits in 9 months
Contributions summary:Robin primarily focused on refining the codebase related to deep learning and EEG/MEG signal processing. Their contributions included modifying the processing of predictions within callbacks to optimize memory usage and ensuring compatibility with the correct device. Additionally, they made changes to the documentation, the handling of training parameters, and updating test configurations. These modifications improve the efficiency, usability and testing of the library.
pytorchneuroimagingneurosciencepythondeep-learning
robintibor/python-mindwave-mobile
Aug 2012 - May 2019
Scripts to read data out of the Neurosky Mindwave Mobile unter Ubuntu
Contributions:36 commits, 4 PRs, 9 pushes in 6 years 9 months
mindwaveneuroskyubuntu
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