Davide Piras
AI Consultant at Sqaix
Geneva, Geneva, Switzerland
Summary
👤
Senior🎓
Top SchoolDavide Piras is an ML software engineer with nine years of experience applying machine learning to astrophysics and space missions, currently implementing novel AI techniques at ETH Zürich's Space Centre. He holds a PhD from UCL in data-intensive science and spent recent postdoctoral years at the University of Geneva and UCL focusing on robust, explainable ML for cosmology. As a freelance AI consultant at Sqaix, he blends research-grade methodology with practical deployment and business-focused solutions. His background spans teaching and tooling—training students in Python and building web apps for NLP—so he pairs clear communication with production coding. Davide’s work emphasizes robustness, explainability and real-world applicability of models, making him adept at translating complex research into mission-ready systems. Based in Geneva, he brings a rare combination of deep domain knowledge in astrophysics and hands-on software engineering for high-stakes environments.
9 years of coding experience
Master's degree, Physics, 110/110 cum laude, Master's degree, Physics, 110/110 cum laude at Università degli Studi di PadovaHigh School Diploma, E. Fermi, Padova, Graduation with 100/100 cum laude, High School Diploma, E. Fermi, Padova, Graduation with 100/100 cum laude at Liceo ScientificoUniversity College London
Italian, English, Chinese, French
Stackoverflow
41reputation
3kreached
1answer
0questions
Github Skills (42)
gaussian-mixture-models10
python10
mutual-information10
mixture10
gmm10
gaussian10
cosmology9
machine-learning9
tensorflow9
neural-network9
deep-learning9
bayesian-inference8
bayesian8
mpi4py8
deep-neural-networks8
Programming languages (4)
Github contributions (5)
dpiras/cosmopower-jax
Apr 2023 - Dec 2024
Differentiable cosmological emulators: the JAX version of CosmoPower
Contributions:13 PRs, 34 pushes, 13 branches in 1 year 8 months
bayesian-inferencecosmologydeep-learningemulatorsjax
dpiras/GMM-MI
Mar 2022 - Dec 2022
Estimation of mutual information (MI) distribution with Gaussian mixture models (GMMs)
Contributions:2 releases, 254 commits, 20 PRs in 9 months
deep-learninggaussian-mixture-modelsgmmestimationgaussian
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