Giuseppe Carleo
Associate Professor
Lausanne, Vaud, Switzerland
Summary
👤
Senior🎓
Top SchoolGiuseppe Carleo is an associate professor and computational physicist based in Lausanne who develops advanced numerical algorithms at the intersection of quantum physics, machine learning and quantum computing. With a trajectory spanning the Simons Foundation, ETH Zürich and EPFL, he focuses on solving strongly interacting quantum many-body problems for condensed matter, ultracold atoms and quantum technologies. He leads research applying artificial intelligence to quantum problems and has been a core back-end contributor to NetKet, a prominent open-source library for machine learning applied to many-body quantum systems. Known for bridging deep theoretical insight with production-ready code, he combines academic leadership with hands-on algorithm and software development. His background includes a PhD from SISSA and honours degrees from Sapienza, reflecting long-standing excellence in both theory and computational practice.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (PhD), Fisica della materia condensata e dei materiali, Doctor of Philosophy (PhD), Fisica della materia condensata e dei materiali at Scuola Internazionale Superiore di Studi Avanzati di TriesteLaurea Magistrale LM, Fisica, 110/110 cum laude, Laurea Magistrale LM, Fisica, 110/110 cum laude at Sapienza Università di Roma
Italian, English, French, Spanish, German
Github Skills (10)
machine-learning10
cluster-computing10
c-language10
parallel-computing10
cprogramming-language10
scientific-computing10
python9
computer-engineering9
mcmc9
algorithms8
Programming languages (3)
Github contributions (5)
netket/netket
Apr 2018 - Nov 2022
Machine learning algorithms for many-body quantum systems
Role in this project:
Back-end Developer
Back-end DeveloperContributions:24 releases, 609 reviews, 984 commits in 4 years 7 months
Contributions summary:Giuseppe primarily focused on maintaining and improving the core functionality of the NetKet project. Their work included bug fixes, changes to the code headers, and improvements to the existing samplers. They also updated the example code snippets to align with changes in the project, and they also created new experimental code. This demonstrates a good understanding of the project structure and goals.
markov-chain-monte-carlovariational-monte-carlomonte-carlo-methodsquantum-computingvariational-method
gcarleo/netket
Jun 2018 - Sep 2023
Machine learning algorithms for many-body quantum systems
Contributions:5 PRs, 149 pushes, 31 branches in 5 years 3 months
quantum-computingmachine-learning-algorithmsquantum-many-bodybodylearning-algorithms
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