Research Engineer · Scientific Computing · Deep Learning · Image-based Modelling · Computational Mechanics
I am a research engineer working at the intersection of numerical simulation, computational geometry, and deep learning for scientific imaging. I currently develop C++ and Python simulation software at CEA‑LIST, contributing smooth‑surface ray tracing and Fermat‑path acoustic propagation modules to the CIVA simulation platform, with applications to transcranial focused‑ultrasound therapy planning and industrial non‑destructive testing.
Over the past fifteen years I have developed scientific software across four countries, including from‑scratch finite‑element solvers, image‑based mesh generators, deep learning for X‑ray micro‑computed tomography, constitutive material laws, and, most recently, smooth B‑spline surface fitting combined with parallel ray tracing. The underlying engineering profile has remained consistent: producing accurate, smooth representations of discrete geometry on which physics simulation can deliver the correct result.
The sections below summarise current and previous work.
Sole developer of the smooth‑surface fitting + Fermat‑path acoustic‑propagation module being integrated into the CIVA platform. The pipeline fits a Multilevel B‑spline (MBA) surface to CT‑segmented skull meshes, ray‑traces against it with a custom backend, and solves the multi‑surface Fermat path for ultrasound propagation through patient‑specific anatomy.
A chronological tour of the positions and projects I have built and shipped. Most of the code is proprietary (industry partners — Safran Aircraft Engines, Siemens PLM, CEA‑LIST); the associated peer‑reviewed papers are listed below.
LMPS / ENS Paris‑Saclay · Safran Aircraft Engines
Hyper‑elastic and elastoplastic material laws for explicit‑dynamics forming simulation of Safran fan‑blade‑class textile parts. Abaqus + LS‑DYNA HPC simulation, macro‑scale parameter identification, CT‑image deformation analysis.
HPCAbaqusLS‑DYNAVUMATPythonFortran
Mines ParisTech CMM (PSL) · Safran Aircraft Engines
Sole author of YarnPath, a regression‑CNN pipeline (TensorFlow / Python) that reconstructs yarn centrelines from µCT scans of 3D woven textile composites used in LEAP‑1A / LEAP‑1B aircraft engine root blades (A320neo, 737 MAX).
PythonTensorFlowU‑NetµCTCUDA
Published in Composites Part A 186 (2024) 108396.
Ghent University · UGCT · Siemens PLM
TensorFlow‑based U‑Net µCT segmentation, image‑based meshing and FE modelling of carbon‑fibre 3D textile composites and short‑fibre‑reinforced polymers in collaboration with UGCT and Siemens Industry Software. Synthetic image generation for training‑data augmentation, eliminating the manual‑annotation bottleneck. HPC training on the VSC Ghent cluster.
PythonTensorFlowU‑NetµCTHPCCUDA
Institut Pprime
Custom image‑to‑mesh pipeline — Python version of vox2tet. µCT‑image‑based multi‑physics FE modelling of moisture diffusion and hygro‑thermo‑mechanical response in carbon‑fibre textile composites. Abaqus with Fortran user subroutines, and full periodic‑BC homogenisation.
PythonMatlabFortranAbaqusCGAL
Karlsruhe Institute of Technology · Institute of Engineering Mechanics
From‑scratch Matlab FEM codebase for composite‑ microstructure optimisation, multi‑scale homogenisation, foam modelling, and CT‑image‑based FEM. Results were validated against synchrotron X-ray diffraction.
MatlabMathematicaAbaqusStressCheck
Lviv National University · IAPMM NASU
From‑scratch FEM solver with 2D meshing, a posteriori adaptive refinement and exponentially-fitted Petrov-Galerkin for the convection‑diffusion problem.
C++FEMh-Adaptivityexponential splinesMFC
Publicly released code.
2018 — Present
C++ pipeline that converts labelled 3D voxel images into high‑quality conforming tetrahedral meshes for finite element simulation. Multi‑material marching cubes, smoothing, iterative remeshing, TetGen tetrahedralisation.
C++CMakeEigenOpenMPlibtiffTetGen
github.com/skenop/vox2tet · Composite Structures 298 (2022) 116003
Full publication list: scholar.google.com/citations?user=HTOhzwsAAAAJ