I am a highly skilled developer specializing in the optimization of high-performance computing (HPC) for scientific applications. I hold a Ph.D. in Computer Science, a Master's degree in Computational Physics, and a Bachelor's degree in Physics. With 5 years of experience, I have honed my expertise in developing and optimizing C, Python, C++, and Fortran codes across both academic (3.5 years) and industrial (1.5 years) settings.

Recently, I spent 6 months working on a deep learning solution to predict processor energy consumption based on performance counters, showcasing my ability to integrate advanced machine learning techniques into performance analysis and optimization.

My core competencies include parallel programming with shared memory architectures (OpenMP, vectorization), performance optimization and profiling of compiled codes, as well as proficiency in DevOps practices (GitLab, GitHub, CI/CD, CMake) in international contexts. I am committed to delivering efficient, scalable solutions that drive scientific research and industrial innovation.