José C. Garay
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I am an Applied Machine Learning Engineer and Computational Scientist working at the intersection of machine learning, scientific computing, physics-based modelling, optimization, and control.
I hold a Ph.D. in Mathematics from Temple University (USA) and have over eight years of experience developing mathematical and computational algorithms for complex engineering problems, with a background in numerical PDEs and ODEs, numerical linear algebra, domain decomposition, multiscale methods, and high-performance computing.
More recently, I have expanded this foundation into applied machine learning and AI, with a focus on Physics-Informed AI, LLM applications, and MLOps, while also applying Model Predictive Control (MPC), state estimation, and optimization to dynamic physical systems. I build physics-aware, data-driven solutions that combine machine learning, scientific computing, control, structured LLM workflows, and production-oriented software engineering.
My goal is to bridge physical modelling and modern AI—using machine learning where it adds value while leveraging first-principles knowledge, simulation, and computational methods to build reliable solutions for complex engineering and industrial systems.