Rocco Meli
Research Software Engineer at CSCS
Lugano, Ticino, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolRocco Meli is a Research Software Engineer with 11 years of experience combining computational chemistry, materials science and high-performance computing at the Swiss National Supercomputing Center. He builds and hardens scientific software—contributing to widely used open-source projects such as Open Babel, MDAnalysis, Spack and scikit-learn—focusing on backend robustness, testing and packaging for reproducible science. With a DPhil in Computational Biochemistry and advanced physics training from EPFL and Oxford, he bridges domain science and production-grade code, frequently addressing parsing, force-field and performance bottlenecks. Rocco’s work on molecular docking data layers and Spack package maintenance shows a knack for turning complex research workflows into reliable, shareable tooling.
11 years of coding experience
1 year of employment as a software developer
DPhil, Computational Biochemistry, DPhil, Computational Biochemistry at University of OxfordMaster of Science - MS, Physics, Master of Science - MS, Physics at EPFL (École polytechnique fédérale de Lausanne)
English, Italian, French
Github Skills (34)
caffe10
c-language10
python10
package-management10
toolbox10
testing10
scikit10
machine-learning10
cmake10
computational-chemistry10
mdanalysis10
numpy10
chemistry10
cheminformatics10
cython10
Programming languages (13)
Github contributions (5)
gnina/gnina
May 2019 - Sep 2021
A deep learning framework for molecular docking
Role in this project:
Backend Developer & Data Scientist
Backend Developer & Data ScientistContributions:167 commits, 27 PRs, 1 branch in 2 years 4 months
Contributions summary:Rocco's commits primarily involve modifications to the `MolGridDataLayer` class within the caffe framework. They have been working on functions related to the handling and transformation of molecular data. The changes suggest involvement in modifying the data layer to handle flexible residues and to compute and utilize gradients for atoms, which is important for molecular docking. Additionally, there are modifications related to parsing and processing protein data, including the removal of inflexible residues, suggesting contributions in cheminformatics and computational chemistry.
cheminformaticsdockingdrug-discoverydeep-learningmolecular-docking
MDAnalysis/mdanalysis
Aug 2019 - Nov 2022
MDAnalysis is a Python library to analyze molecular dynamics simulations.
Role in this project:Backend Developer
Backend DeveloperContributions:450 reviews, 116 commits, 132 PRs in 3 years 3 months
Contributions summary:Rocco primarily contributed to bug fixes and code improvements related to the MOL2 file format, specifically addressing issues in the parsing and handling of atom types and bond information within the `MDAnalysis` library. They corrected a bug concerning the inclusion of status bit strings in the MOL2 files and also ensured that the elements attribute is correctly populated. The user also added tests to validate their changes and improve existing code quality.
molecular-dynamics-simulationmolecular-dynamics-simulationspython-librarypythonscience
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