Sebastien Fischman
Research Engineer - Computer Vision at DAMAE Medical
Paris, Ile-de-France
Summary
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Rockstar🎓
Top SchoolSebastien Fischman is a research engineer specializing in computer vision with a decade of experience building and deploying machine learning solutions across medical imaging and financial/ad tech domains. Based in Paris, he combines strong mathematical training (ENS Cachan, UPMC) with hands-on expertise in deep learning research roles at Nokia Bell Labs and in industry at DreamQuark and DAMAE Medical. He has led data science teams and contributed to open-source ML tooling—helping refactor and add sklearn-compatible APIs and feature importance to a PyTorch TabNet implementation. Comfortable moving models from research to production, he is equally at home with probabilistic modeling, large-scale data engineering (PySpark) and practical feature engineering. A less obvious strength is his long-standing mix of academic rigor and product-focused delivery, which enables rapid translation of novel algorithms into reliable systems.
10 years of coding experience
5 years of employment as a software developer
Classes préparatoires, Maths/Physique/Info, Classes préparatoires, Maths/Physique/Info at Lycée Saint-LouisMaster 2 (M2), Probabilités et Modèles Aléatoires, Master 2 (M2), Probabilités et Modèles Aléatoires at Université Pierre et Marie Curie (Paris VI)Master 2 (M2), Master MVA, Machine Learning, Master 2 (M2), Master MVA, Machine Learning at Ecole Normale Supérieure de CachanMaster, Mathématiques, Master, Mathématiques at Ecole normale supérieure de Cachan
Github Skills (10)
pytorch10
machine-learning10
deep-learning10
python10
tabular9
datatable9
deep-neural-networks9
neural-network9
scikit7
scikit-learn7
Programming languages (5)
Github contributions (5)
dreamquark-ai/tabnet
Oct 2019 - Dec 2022
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 76 reviews, 91 commits in 3 years 2 months
Contributions summary:Sebastien contributed to the `tabnet` repository, which implements the TabNet paper. Their work included refactoring the package, adding a softmax function to the `predict_proba` method, and addressing an issue with the functional balanced version. Further contributions included adding representation strings and a scikit-learn compatible version of the model. The user also added feature importances to the TabNetClassifier and TabNetRegressor classes.
pytorchpytorch-tabnetarxivpdfdeep-learning
Optimox/torchtune
Apr 2024 - Nov 2024
A Native-PyTorch Library for LLM Fine-tuning
Contributions:24 pushes, 6 branches in 6 months
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