About Stefano Zampini Stefano Zampini Senior Research Scientist, Hierarchical Computations on Manycore Architectures High Performance Computing Domain Decomposition Scientific Machine Learning Parallel and Distributed Computing computational science and engineering As a computational mathematician and software engineer, Stefano Zampini's work lies at the intersection of numerical analysis, predictive simulations, and high-performance computing, with recent forays into machine learning. Events Presented Events Mar 9 - Mar 15, 2025 On the Use of "Conventional" Unconstrained Minimization Solvers for Training Regression Problems in Scientific Machine Learning Stefano Zampini, Senior Research Scientist, Hierarchical Computations on Manycore Architectures Mar 13, 12:00 - 13:00 B9 L2 R2325 petsc PETScML machine learning This talk introduces PETScML, a framework leveraging traditional second-order optimization solvers for use within scientific machine learning, demonstrating improved generalization capabilities over gradient-based methods routinely adopted in deep learning. Sep 24 - Sep 30, 2023 Device accelerated solvers for PDEs. Current status and future perspectives Stefano Zampini, Senior Research Scientist, Hierarchical Computations on Manycore Architectures Sep 26, 16:00 - 17:00 B5 L5 R5220 Shaheen GPU supercomputing petsc PDEs In the next months, KAUST expects to place into service Shaheen-3, a supercomputer with a GPU partition whose planned 1 Exaflop/s HPL AI capability would rank it in the Top 6 globally if delivered today.
On the Use of "Conventional" Unconstrained Minimization Solvers for Training Regression Problems in Scientific Machine Learning Stefano Zampini, Senior Research Scientist, Hierarchical Computations on Manycore Architectures Mar 13, 12:00 - 13:00 B9 L2 R2325 petsc PETScML machine learning This talk introduces PETScML, a framework leveraging traditional second-order optimization solvers for use within scientific machine learning, demonstrating improved generalization capabilities over gradient-based methods routinely adopted in deep learning.
Device accelerated solvers for PDEs. Current status and future perspectives Stefano Zampini, Senior Research Scientist, Hierarchical Computations on Manycore Architectures Sep 26, 16:00 - 17:00 B5 L5 R5220 Shaheen GPU supercomputing petsc PDEs In the next months, KAUST expects to place into service Shaheen-3, a supercomputer with a GPU partition whose planned 1 Exaflop/s HPL AI capability would rank it in the Top 6 globally if delivered today.
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