Curriculum Vitae

Wassim Kabalan — R&D Engineer & PhD Candidate (APC, CNRS/IN2P3)

Wassim Kabalan

R&D Engineer & PhD Candidate in Computational Cosmology

I am an R&D Engineer and PhD Candidate at APC (CNRS/IN2P3). My work bridges the gap between mathematical physics and industrial-grade software engineering. I specialize in differentiable programming (adjoint-based methods), simulation pipelines, and inverse problems on massive GPU architectures.

GitHub - ASKabalan LinkedIn - Wassim Kabalan Email - wassim@apc.in2p3.fr

Education

Dec 2023 –
Oct 2026
(expected) 		PhD, Physics of the Universe, Université Paris Cité, Paris, France
APC, CNRS/IN2P3
Thesis: Automatically differentiable and distributed probabilistic programming for weak-lensing inference.
Advisors: Eric Aubourg, Alexandre Boucaud, Josquin Errard, François Lanusse
Apr 2023 –
May 2023 		Advanced AI for Data Analysis, École Polytechnique (Executive Education)
Sep 2016 –
Sep 2018 		M2, Electronics, Electrical & Automation, Université Gustave Eiffel
Sep 2013 –
Nov 2016 		Licence, Engineering Science, Université Paris-Est Créteil (UPEC)

Research & Professional Experience

Dec 2023 –
Present 		Graduate Researcher (PhD), APC, CNRS/IN2P3, Paris, France
Focus: Computational Cosmology, HPC & Inverse Problems
  • Differentiable Simulation (LSST): Developing JaxPM, a differentiable N-body forward model enabling gradient-based calibration of high-dimensional physical models using adjoint methods (via JAX AD).
  • Inverse Problems (Simons Observatory): Solving large-scale inverse problems for CMB component separation. Implementing optimization workflows for spatially varying parameters to maximize signal recovery.
  • HPC Infrastructure: Author of jaxDecomp, enabling multi-node domain decomposition and distributed FFTs on GPU clusters (binding to NVIDIA cuDecomp).
Oct 2019 –
Oct 2023 		Software Infrastructure Engineer, Dassault Systèmes, Vélizy-Villacoublay, France
  • Optimized CATIA cache/conversion pipeline (C++/Linux) for very large CAD data flows.
  • Led Linux convergence for a large C++ rich client.
  • Built GitLab CI/CD pipelines for automated testing, packaging, and releases.
Jan 2019 –
Oct 2019 		Data Acquisition Engineer, SERMA (for Renault), Guyancourt, France
  • Built tooling for high-volume vehicle sensor logs, including a custom C decoder for ECU binary data.
  • Developed parallel Python post-processing workflows on an 88-core server for vehicle network data.
  • Automated conversion pipelines for vehicle network data.

Supervision

Jan 2025 –
Jun 2025 		Binh Nguyen, Master 1 Student
Project: Inference of Weak-Lensing Parameters from Blended Galaxies Using Generative Neural Networks.
Outcome: Talk at Rencontres du Vietnam.

Talks & Tutorials

  • Generative AI with JAX [Tutorial] • AISSAI School 2025 • Oct 2025 slides
  • JAXPM: Scalable and Differentiable Particle-Mesh Simulations • Bayesian Deep Learning Workshop • May 2025 slides
  • Bayesian Inference for Cosmology with JAX [Tutorial] • Bayesian Deep Learning Workshop • May 2025 slides
  • Massively Parallel Computing in Cosmology with JAX [Tutorial] • CoPhy 2024 • Nov 2024 slides
  • Differentiable and Distributed Particle-Mesh N-body Simulations • LSST France 2024 • Jun 2024 slides

Open-Source Software

  • jaxDecomp — Author. JAX based code for multi-GPU 3D domain decomposition and distributed FFTs (NCCL); scales cosmology workloads on GPU clusters.
  • JaxPM — Main contributor/Maintainer. Differentiable particle-mesh simulations in JAX; multi-accelerator support for scalable forward modeling and gradient-based inference.
  • FURAX — Main contributor/Maintainer. JAX building blocks for inverse problems; used for CMB component separation at Simons Observatory.
  • FURAX_CS — Main contributor/Maintainer. Component separation pipeline for the Simons Observatory and LiteBird using FURAX.
  • jax-healpy — Main contributor/Maintainer. JAX-native HEALPix utilities for CMB/spherical data; GPU- and autodiff-ready.
  • jax-grid-search — Author. Distributed grid search and gradient-based optimization on JAX/Optax.

Contributions
- S2FFT — Contributor. Differentiable spherical & Wigner transforms (JAX & PyTorch). Contribution: CUDA spherical harmonics to reduce JAX JIT time.

Publications

Refereed

Software

  • Kabalan, W., Lanusse, F., Boucaud, A., and Aubourg, E. 2025. jaxDecomp: JAX Library for 3D Domain Decomposition and Parallel FFTs. Submitted to JOSS.

In Preparation

  • Kabalan, W., Lanusse, F., Boucaud, A., and Aubourg, E. 2025. JAXPM: A JAX-Based Framework for Scalable and Differentiable Particle Mesh Simulations.

  • Kabalan, W., Rizzieri, A., Sohn, W., Beringue, B., Basyrov, A., Chanial, P., Boucaud, A., and Errard, J. 2025. A novel approach to optimize clustering for parametric map-based component separation for upcoming CMB polarization satellites.

Skills

Programming Python (7y), JAX (2y), C++ (5y), CUDA (3y), PyTorch (1y)

HPC & GPU Computing NCCL, MPI, Slurm, Nsight; multi-node GPU, distributed FFTs

Statistics & Machine Learning Bayesian inference (MCMC, HMC, NUTS), simulation-based inference; NumPyro/BlackJAX

DevOps & Software Engineering GitHub/GitLab CI, packaging (PyPI), containers, Linux, CMake, TDD

Languages

French Native
English Professional proficiency (C1)
Arabic Native
German Basic (A2)