Daniele Grattarola
Head Of Machine Learning at Proxima
Italy
Summary
🤩
Rockstar🎓
Top SchoolDaniele Grattarola is a Staff Research Scientist based in Italy with 11 years of experience applying geometric deep learning and generative models to problems across structural biology, drug discovery, and clinical neuroscience. Currently at Isomorphic Labs, he has advanced multimodal and post-training approaches for large models after a progression from ML Research Scientist to senior technical leadership roles. His academic background includes a PhD in Computer Science and postdoctoral work at EPFL where he fused neural fields with protein design, and a visiting role at Krembil that produced a clinically-relevant seizure-localization method. An active open-source contributor, he materially improved the popular spektral library—fixing dataset loaders, adding edge-attribute support, and enabling variable-sized inputs—demonstrating a blend of research rigor and engineering craftsmanship. Colleagues rely on him to translate cutting-edge theory into robust, production-ready ML components that impact both labs and industry.
11 years of coding experience
4 years of employment as a software developer
Master's degree Computer Science and Engineering, Master's degree Computer Science and Engineering at Politecnico di MilanoDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at USI Università della Svizzera italiana
Italian, English, Spanish
Stackoverflow
1,517reputation
97kreached
37answers
10questions
deep-learning
top-5%
keras
top-5%
Github Skills (11)
machine-learning10
tensorflow10
graph-neural-network10
python10
deep-learning9
keras9
neural-network6
numpy6
classification6
linear-regression6
scikit-learn6
Programming languages (8)
Github contributions (5)
danielegrattarola/keras-multiplexer
Jun 2017 - Nov 2019
Contributions:10 commits, 8 pushes, 1 branch in 2 years 6 months
danielegrattarola/keras-gat
Nov 2017 - Oct 2020
Keras implementation of the graph attention networks (GAT) by Veličković et al. (2017; https://arxiv.org/abs/1710.10903)
Contributions:38 commits, 5 PRs, 34 pushes in 3 years
kerasthegraphdeep-learninggraph-attention-networksgraph
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