About Esmail Abdul Fattah Esmail Abdul Fattah Postdoctoral Research Fellow, Statistics HPC Bayesian computational statistics Events Presented Events Oct 11 - Oct 17, 2026 Solvers That Don't Choose Sides Esmail Abdul Fattah, Postdoctoral Research Fellow, Statistics Oct 15, 12:00 - 13:00 B9 R2325 sparse computation scientific computing programming abstractions dense linear algebra Sparse Linear Algebra sTiles In this talk, I will present sTiles, a direct solver that decides sparse or dense tile by tile instead of once for the whole matrix, covering the full range from sparse to fully dense and outpacing established sparse direct solvers on the repeated factorizations that drive large-scale inference. Jun 4 - Jun 10, 2023 Approximate Bayesian inference based on dense matrices and new features using INLA Esmail Abdul Fattah, Postdoctoral Research Fellow, Statistics Jun 4, 15:00 - 16:00 B4 L5 R5220 Bayesian computational statistics The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects.
Solvers That Don't Choose Sides Esmail Abdul Fattah, Postdoctoral Research Fellow, Statistics Oct 15, 12:00 - 13:00 B9 R2325 sparse computation scientific computing programming abstractions dense linear algebra Sparse Linear Algebra sTiles In this talk, I will present sTiles, a direct solver that decides sparse or dense tile by tile instead of once for the whole matrix, covering the full range from sparse to fully dense and outpacing established sparse direct solvers on the repeated factorizations that drive large-scale inference.
Approximate Bayesian inference based on dense matrices and new features using INLA Esmail Abdul Fattah, Postdoctoral Research Fellow, Statistics Jun 4, 15:00 - 16:00 B4 L5 R5220 Bayesian computational statistics The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects.
Engage ORCID LinkedIn ShareClipboard Related Sites Bayesian Computational Statistics and Modeling (BAYESCOMP) Hierarchical Computations on Manycore Architectures (HiCMA) Statistics (STAT) Related Content Articles 3 Events 2 Upcoming Events Solvers That Don't Choose Sides Esmail Abdul Fattah, Postdoctoral Research Fellow, Statistics Oct 15, 12:00 - 13:00 B9 R2325 sparse computation scientific computing programming abstractions dense linear algebra Sparse Linear Algebra sTiles Related Links Esmail Abdul Fattah's personal website
Solvers That Don't Choose Sides Esmail Abdul Fattah, Postdoctoral Research Fellow, Statistics Oct 15, 12:00 - 13:00 B9 R2325 sparse computation scientific computing programming abstractions dense linear algebra Sparse Linear Algebra sTiles
