Gabriele Tocci is a Computational Scientist and Senior Machine Learning Engineer with 11+ years of R&D experience building scalable AI and HPC solutions across Microsoft Quantum, University of Zurich, EPFL and UCL. He specializes in integrating advanced deep learning models—especially equivariant graph neural potentials like NequIP and Allegro—directly into physics-based workflows and production-grade simulation engines such as CP2K. Gabriele has led large HPC projects (securing multi-million CPU-hour allocations), optimized multi-GPU Azure HPC and cloud-native ML pipelines, and contributed merged improvements to CP2K to accelerate ML interatomic potential inference. His stack spans PyTorch (Python/C++), CUDA/MPI, RAPIDS, and low-level languages (C/C++, Fortran), and he recently earned NVIDIA certification in Accelerated Data Science. Beyond academia-to-industry transitions, he is applying stochastic calculus and advanced ML generalization techniques to time-series backtesting and exploring local LLM inference on GPU edge devices.
11 years of coding experience
8 years of employment as a software developer
Data Science Bootcamp, Data Science, Data Science Bootcamp, Data Science at Constructor Nexademy
Liceo Scientifico
Energy Engineering, Energy Engineering at Sapienza Università di Roma
Contributions:96 commits, 81 pushes, 4 branches in 2 years 1 month
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