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Mikel Landajuela Larma

Computational and Applied Mathematician at Lawrence Livermore National Laboratory, holding a Ph.D. from Université Pierre et Marie Curie and Inria, with a demonstrated international history of working in the research industry. Awarded with the SMAI-GAMNI award 2017 (French Society of Industrial and Applied Mathematics) for the best thesis in numerical methods for the mechanical and engineering sciences. Skilled in Scientific Computing, Computational Mechanics, Mathematical Modeling and Data science. Educated at University of the Basque Country and Utrecht University.

Jul. 2020 - Today

Data Scientist, Computational Engineering, Lawrence Livermore National Laboratory, Livermore, US.

Jul. 2018 - Jul. 2020

PostDoctoral fellow, Biochemical and Biophysical Systems Group, Lawrence Livermore National Laboratory, Livermore, US.
Topic: Computational Heart Electrophysiology and Machine Learning

Apr. 2016 - Apr. 2018

PostDoctoral fellow, MOX - Dipartimento di Matematica, Politecnico di Milano, Milano, Italy.
Topic: Computational Heart Electromechanics

Oct. 2012 - Mar. 2016

Ph.D. in Computational and Applied Mathematics, Université Pierre et Marie Curie and Inria, Paris, France.
Topic: Biomechanical Fluid-Structure Interaction
Get the manuscript here.

2011-2012

European Exchange Program, Utrecht University, Utrecht, The Netherlands.
Master program in Scientific Computing

Jun. 2011- Jul. 2011

Research Internship, Basque Center for Applied Mathematics, Bilbao, Spain.
Topic: Burgers equation
Get the report here.

2007-2012

5-years Degree in Applied Mathematics, University of the Basque Country, Bilbao, Spain

A detailed CV can be found here.

Publications on Referred Journals:

  • M. A. Fernández, M. Landajuela, Splitting schemes and unfitted-mesh methods for the coupling of an incompressible fluid with a thin-walled structure. IMA Journal of Numerical Analysis, dry098, 2019, pdf
  • A. Quarteroni, C. Vergara, M. Landajuela, Mathematical and Numerical Description of the Heart Function. Imagine Math 6: Between Culture and Mathematics, 2018, pdf
  • M. Landajuela, C. Vergara, A. Gerbi, L.Dede', L. Formaggia, A. Quarteroni, Numerical approximation of the electromechanical coupling in the left ventricle with inclusion of the Purkinje network. International Journal for Numerical Methods in Biomedical Engineering, 2018;34:e2984, pdf
  • M. Landajuela, M. Vidrascu, D. Chapelle, M. A. Fernández, Coupling schemes for the FSI forward prediction challenge: comparative study and validation. International Journal for Numerical Methods in Biomedical Engineering, 2040-7947, 2016, pdf
  • F. Alauzet, B. Fabrèges, M. A. Fernández, M. Landajuela, Nitsche-XFEM for the coupling of an incompressible fluid with immersed thin-walled structures. Computer Methods in Applied Mechanics and Engineering, 301:300 – 335, 2016, pdf
  • M. A. Fernández, M. Landajuela, M. Vidrascu, Fully decoupled time-marching schemes for incompressible fluid/thin-walled structure interaction. Journal of Computational Physics, 297:156-181, 2015. pdf
  • M. A. Fernández, M. Landajuela, Splitting schemes for incompressible fluid/thin-walled structure interaction with unfitted meshes. Comptes Rendus Mathématique, 353(7):647-652, 2015, pdf
  • M. A. Fernández, M. Landajuela, J. Mullaert, M. Vidrascu, Robin-Neumann schemes for incompressible fluid-structure interaction. In Domain Decomposition Methods in Science and Engineering XXII, Lecture Notes in Computer Science (LNCS), Lugano, Switzerland, 2015, pdf
  • M. A. Fernández, M. Landajuela, A fully decoupled scheme for the interaction of a thin-walled structure with an incompressible fluid. Comptes Rendus Mathématique, 351(3):161-164, 2013, pdf

Patents:

  • A non-invasive method and apparatus of extracting transmembrane potentials of cardiac tissue from a common 12 lead cardiac ekg. LLNS.002PR / IL-13207, 2019.

Other:

  • M. Landajuela, Phase Plane Dynamics of FitzHugh-Nagumo Model of Neuronal Excitation. Wolfram Demonstrations Project, Published: March 15, 2019 Go to link
  • M. Landajuela, Regularized Logistic Regression. Wolfram Demonstrations Project, Published: November 30, 2018 Go to link
  • M. Landajuela, Mass Matrix Computation in the Finite Element Method. Wolfram Demonstrations Project, Published: March 9, 2018 Go to link
  • M. Landajuela, Linear Regression. Wolfram Demonstrations Project, Published: March 2, 2018 Go to link
  • M. Landajuela, Windkessel Model for Hemodynamics in Arterial Systems. Wolfram Demonstrations Project, Published: February 19, 2018 Go to link
  • M. Landajuela, Heart Electrophysiology. Wolfram Demonstrations Project, Published: January 16, 2018 Go to link
  • M. Landajuela, Burgers equation. Report in BCAM Internship: Basque center for applied mathematics, 2011, pdf

Programming Skills

Operating systems: Linux, Mac OS, Windows.
Languages: C/C++, Python.
Parallel frameworks : MPI, PETSc, Trilinos.
Version Control: Svn, Git, GitHub, Bitbucket.
Code Development:
FELiScE - Finite Elements for LIfe SCiences and Engineering
LifeV - A parallel finite element library for the solution of PDEs

Scientific software/libraries

Matlab, Mathematica (some personal examples here), FreeFem++, Gmsh, Vmtk, ParaView, Ensight.
PyTorch (some personal examples here), Scikit-learn, Pandas, seaborn.

Certificates

Machine Learning by Stanford University on Coursera. Certificate earned on Wednesday, January 17, 2018 3:57 PM GMT
See certificate here

Deep Learning, a 5-course specialization by deeplearning.ai on Coursera. Specialization Certificate earned on October 14, 2018
See certificate here

Parallel and Concurrent Programming with Python 1, LinkedIn, See certificate here

Advanced Python, LinkedIn, See certificate here

Building and Deploying Deep Learning Applications with TensorFlow, LinkedIn, See certificate here

Python for Data Science Essential Training, LinkedIn, See certificate here

C++ Essential Training, LinkedIn, See certificate here

Python Essential Training, LinkedIn, See certificate here

Programming Foundations : Object Oriented Design, LinkedIn, See certificate here

Languages

English: Fluent.
Spanish: Mothertongue.
French: Fluent.
Italian: Fluent.
Basque: Basic.

Mar. 2017

SMAI-GAMNI award 2017

French Society of Industrial and Applied Mathematics.
Annual award for the best thesis in numerical methods for the mechanical and engineering sciences.
See the press note here.
See also this interview for Inria on March 27th 2017.

Nov. 2012

Premio extraordinario de carrera award 2012

University of the Basque Country.
Prize awarded to the most outstanding graduate in Mathematics of the 2011/2012 class.
See the press note here.

Jul. 2012

Inria Paris-Rocquencourt PhD Recruitment Campaign 2012

Inria Paris.
1st selected candidate of the 2012 PhD Inria Paris-Rocquencourt recruitment campaign.

My research interests include:

Machine Learning

Machine Learning to Biosignals, Encoder-decoder architectures, Recurrent Models, Seq2seq models, Time-series prediction.

Biomechanical modelling and simulation

Blood flow simulation, Blood-Arterial wall interaction simulation, Valve simulation, Electrophysiology, Purkinje network, Heart Electromechanics

Electromechanical activation of the heart Blood flow in the Aorta

Fluid-Structure Interaction

Coupling schemes, Robin-Neumann schemes, Incompressible fluids, Thin shells, Immersed structures, Energy-based estimates

Fluid-Structure interaction Valve simulation

Numerical Simulation of Partial Differential Equations

Finite element method, Time-discretization, Operator splitting techniques, Unfitted mesh methods, Extended finite element method

Mesh intersection Moving mesh

Go to my Youtube channel to see movies associated to my research

A research statement can be found here