Calculated based on number of publications stored in Pure and citations from Scopus
Calculated based on number of publications stored in Pure and citations from Scopus
Calculated based on number of publications stored in Pure and citations from Scopus
20072025

Research activity per year

Personal profile

Biography

After completing my PhD studies in Computational Science & Engineering (CSE) at Universitat Jaume I, Castellon, Spain, and a short 6-month post-doc at that university (EU-FP7 TEXT project), I moved to Barcelona (Spain) in March, 2011, where I did post-doctoral research until December, 2015. I worked for the Technical University of Catalonia (UPC) and the International Center for Numerical Methods in Engineering (CIMNE) funded by two competitive post-doctoral research grants (awarded by UPC and the Catalan gov, resp.) Besides, I participated in two strategic EU projects for CIMNE, the ERC Starting Grant COMFUS, to support basic research in scalable numerical algorithms for the HPC simulation of realistic fusion technology problems, and the EU-FP7 NUMEXAS project, towards the next generation of advanced numerical methods for future exascale computing infrastructures. In January, 2016 I was promoted as senior researcher at CIMNE (permanent contract), position which I held until November, 2019. In this period, I participated with a managing role in several high quality EU-funded projects, among which I highlight the FET­HPC H2020 ExaQUte project. Then, early 2020, I moved to Monash University, Australia, where I was appointed as a research fellow in the School of Mathematics. On Dec, 22, I moved to the Australian National University, School of Computing, where I was appointed as a senior lecturer (Level C) in the Computational Science research cluster.

Qualifications

PhD. in Computational Science

Research Interests

My field of expertise is High Performance Scientific Computing. Within this field, I have particularly specialized in: (1) the design of advanced, application-tailored, finite element discretizations and fast and scalable solution methods for the numerical approximation of Partial Differential Equations (PDEs), and their parallel message-passing implementation for the efficient exploitation of current petascale distributed memory supercomputers; (2) the development of innovative mathematical software design patterns for the numerical approximation of PDEs, the implementation of these in open source scientific software packages, and the application of these advances in the solution of real-world challenges in collaboration with application-problem specialists, and/or private sector companies, (3) the development of Scientific Machine Learning innovative approaches for the data-driven, physics-constrained numerical solution of inverse PDE problems. I am leading the development of the so-called in Gridap.jl Julia ecosystem of packages for the numerical solution of PDEs on supercomputers.

  • Computer solution of mechanistic systems of PDEs (Partial Differential Equations)
  • Finite element discretization methods
  • Scalable solution methods (multigrid, multilevel domain decomposition)
  • Message-passing and multi-threaded parallel algorithms
  • Scientific Machine Learning
  • Innovative scientific software design patterns
  • Open source scientific software
  • Geophysical flows with application to atmosphere and ocean dynamics

Education/Academic qualification

Computational Science, PhD, Exploiting Thread-Level Parallelism in the Preconditioning and the Iterative Solution of Sparse Linear Systems, Jaume I University

Award Date: 15 Jul 2010

Research student supervision

  • Registered to supervise

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