Summary
Fernando Acosta is a software engineer and PhD-trained physicist specializing in machine learning for fundamental science, with nine years of experience and a strong track record in C++, Python, high-performance computing, and deep learning. He has applied generative diffusion models, AI-assisted co-design, and ML-based deconvolution to challenging inverse problems in particle and nuclear physics, including work at CERN with ALICE and at Berkeley Lab. His research bridged simulation and experiment—implementing novel stable-matching corrections and deep photon classifiers—and has been deployed in HPC contexts for large-scale training and simulation. Now at Google, he brings both production software engineering skills and domain expertise in scientific ML, combining rigorous experimental thinking with scalable model development. An atypical strength is his ability to translate detector-level problems into ML architectures and efficient C++/Python implementations that run on HPC infrastructure.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Nuclear Physics, Doctor of Philosophy - PhD, Nuclear Physics at University of California, Berkeley
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at Stony Brook University