Félix Raimundo
Co-Founder & CEO at TychoBio.ai
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolFélix Raimundo is a founder and computational biologist with 11 years of experience applying machine learning to biology and healthcare, currently leading TychoBio.ai from Paris. He combines deep expertise in single-cell multi-omics, epigenetics and molecular biology with hands-on ML engineering demonstrated in contributions to high-profile projects like Google DeepVariant and the mne-python library. Félix has bridged academia and industry—from a PhD and Google/DeepMind research on statistical methods for triple-negative breast cancer to leading computational teams in immunotherapy target discovery—while scaling teams and production workflows. He brings a rare mix of algorithmic rigor (PhD-level biomathematics), low-level engineering experience (Haskell, C, firmware) and practical model evaluation work, making him fluent in both research and production ML for genomics.
11 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Biomathematics Bioinformatics and Computational Biology, Doctor of Philosophy - PhD Biomathematics Bioinformatics and Computational Biology at Mines Paris - PSLMaster of Engineering (M.Eng.) Embedded systems / Algorithms, Master of Engineering (M.Eng.) Embedded systems / Algorithms at Télécom ParisPCSI / PSI* Engineering Physics/Applied Physics, PCSI / PSI* Engineering Physics/Applied Physics at Lycée Janson de Sailly
French, English
Stackoverflow
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Github Skills (24)
variants10
python10
evaluation10
testing10
scikit10
machine-learning10
neuroscience10
eval10
deep-learning10
trainings10
tensorflow10
scikit-learn10
bioinformatics10
modeling10
pandas9
Programming languages (13)
Github contributions (5)
mne-tools/mne-python
Jun 2016 - Jul 2016
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:9 commits, 4 PRs, 18 comments in 17 days
Contributions summary:Félix's contributions primarily focused on improving the `mne-python` library's functionality and testing infrastructure. They implemented the `apply_baseline` method within the `Evoked` and `Epochs` classes and added a corresponding test to ensure proper baseline correction. Moreover, the user refactored the codebase to utilize the `apply_baseline` function and also corrected documentation and added a new option. Their work improved the library's usability and maintainability.
visualizationneuroimagingneurosciencepythonstatistics
google/deepvariant
Aug 2018 - Sep 2018
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
Role in this project:ML Engineer
ML EngineerContributions:10 commits in 1 month
Contributions summary:Félix primarily contributed to the development and evaluation of a window selection model for variant calling within the DeepVariant pipeline. Their work includes the creation of a script to generate and train an AlleleCountLinearModel using data from VCF and BAM files. They also developed a model evaluation script to assess the performance of the trained model using precision-recall metrics. Additionally, the user made modifications to the realigner and window selector components, and updated the default window selection model and test scripts.
genomedeepvariantdnabioinformaticstensorflow
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