Agamemnon Krasoulis
Senior Machine Learning Scientist at Bindbridge
Athens, Attica, Greece
Summary
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Senior🎓
Top SchoolAgamemnon Krasoulis is a Senior Machine Learning Scientist with 11 years of experience applying deep learning and probabilistic modelling to AI-driven drug discovery, bioinformatics and neural engineering. He has led R&D teams and shipped production-grade molecular property prediction and virtual screening systems—contributing to platforms like Chemistry42 and authoring graph-based virtual screening research—while also consulting on demand forecasting and revenue management through his ML firm. His background spans academic research (PhD in Computational Neuroscience from Edinburgh) and industry, including work on EEG-based BCI, prosthetic control, and clinical outcome prediction from single-cell data. An active open-source contributor, he has improved core scikit-learn probability estimation and tests, showing attention to model correctness beyond bespoke pipelines. Based in Athens, he combines hands-on engineering, publication- and patent-driven R&D, and consultancy flexibility for startups and pharma alike.
11 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Computational Neuroscience and Neuroinformatics, Doctor of Philosophy - PhD, Computational Neuroscience and Neuroinformatics at The University of EdinburghDiploma, Electrical and Computer Engineering, Diploma, Electrical and Computer Engineering at University of Patras
English, Spanish, French, Greek
Github Skills (10)
scikit-learn10
machine-learning10
python10
data-science10
scikit10
pytest9
testing9
statistics8
documentation8
numpy7
Programming languages (10)
Github contributions (5)
scikit-learn/scikit-learn
Jan 2019 - Feb 2021
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:10 commits, 12 PRs, 99 comments in 2 years
Contributions summary:Agamemnon contributed to the scikit-learn library by addressing issues related to probability estimation and model correctness. Their work included fixing bugs in `predict_proba` functions for `LinearDiscriminantAnalysis` and adding tests to validate the accuracy of these probability calculations, particularly for multi-class classification. Furthermore, the user improved the documentation and overall code quality within the library. The user also updated tests for the `MultiOutputClassifier` classes_ attribute, and corrected the mean_absolute_error multioutput test.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
agamemnonc/pyEMG
Nov 2016 - Jun 2019
Contributions:114 commits, 34 pushes in 2 years 7 months
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