CV
Machine learning engineer with a PhD in physics, specializing in GPU-accelerated scientific ML, HPC, and distributed training. Experienced in profiling and optimizing multi-GPU and multi-node ML workloads, developing physics-informed surrogates for large-scale simulations, and validating performance on modern NVIDIA architectures using JAX and PyTorch.
Technical Skills
Machine Learning: Deep learning (JAX/Flax, PyTorch); CNNs, VAEs, fully-connected networks; Physics-informed neural networks; Differentiable programming; Generative models; Bayesian inference, MCMC, gradient-based optimization
Programming: Python (expert): NumPy, SciPy, Pandas, JAX ecosystem; Shell scripting; R; IDL
Infrastructure: GPU/HPC: MPI, OpenMP, SLURM, PBS; CI/CD (GitHub Actions); Testing (pytest); Documentation (Sphinx, ReadTheDocs); PyPI packaging (uv, Poetry)
Open-Source Software
picasso - Neural network for 3D field generation (JAX, Flax) - 2024
- Designed conditional neural network predicting gas pressure fields from dark matter halo properties
- Achieves 10,000× speedup over traditional simulation methods while preserving physical accuracy
- GPU-accelerated and fully differentiable for integration into gradient-based inference pipelines
- Peer-reviewed publication; adopted by South Pole Telescope collaboration
- Documentation: picasso-cosmo.readthedocs.io
halox - Differentiable scientific computing library (JAX) - 2025
- GPU-accelerated, auto-differentiable physics calculations for gradient-based workflows
- Validated against standard benchmarks; 100% test coverage; automated CI/CD; distributed via PyPI
- Publication: Kéruzoré, Submitted to JOSS
- Documentation: halox.readthedocs.io
panco2 - Bayesian inference pipeline (Python) - 2022
- Forward-modeling MCMC pipeline for signal extraction from noisy and filtered image data; ~1000× speedup over prior tool
- Adopted as the official pipeline of the NIKA2 collaboration; peer-reviewed publication
- Documentation: panco2.readthedocs.io
GalGenAI - Experimental generative models for image synthesis (PyTorch) - In development
- Deep generative models (VAE + flow-matching) for conditional astronomical image generation
Experience
2021 – Present: Post-doctoral Research Associate
- Argonne National Laboratory, Cosmological Physics and Advanced Computing group
- Developed neural networks generating synthetic 3D datasets from TB-scale simulations, enabling physics-informed predictions four orders of magnitude faster than traditional methods
- Built and shipped 3 open-source ML libraries with documentation, CI/CD, and PyPI distribution
- Leveraged DOE leadership-class supercomputers (ALCF) for large-scale, multi-node, multi-GPU inference
- Led analysis coordination for multi-institution collaboration (100+ researchers)
- Designed validation benchmarks and stress tests to evaluate generative model fidelity
- Mentored 3 junior researchers on ML projects (3–6 month appointments)
- Communicated complex ML architectures and results to diverse technical audiences at international conferences and research institutes
2018 – 2021: Graduate Research Associate
- Université Grenoble Alpes, Laboratoire de Physique Subatomique et Cosmologie
- Developed end-to-end data pipelines extracting weak signals from noisy observational data
- Built Monte Carlo simulation frameworks for statistical uncertainty quantification & sensitivity analysis
- Designed and deployed collaboration-wide database serving 50+ researchers
- Teaching assistant: 96 hours of instruction in physics courses
Education
- Ph.D. in Astrophysics, Université Grenoble Alpes, 2021
- M.S. in Physics, Université de Montpellier, 2018 · First in class
- Research project: Deep learning for cosmological parameter estimation from supernova data
- B.S. in Physics & Chemistry, Université de Bordeaux, 2016
Professional Development
- Intro to AI-driven Science on Supercomputers - Argonne Leadership Computing Facility (2024)
- Statistical Challenges in Modern Astronomy - Penn State University (2021)
Research Output
- 98 publications (8 as first-author) · 1282 citations · h-index: 17 (Source: NASA ADS) - For the complete list, see the Publications page.
- 20+ public talks - For the complete list, see the Talks page.
Leadership & Communication
- Junior Coordinator, Galaxy Cluster Analysis - South Pole Telescope Collaboration (2024–present)
- Pipeline & Database Lead - NIKA2 Collaboration (2019–2021)
- Science outreach: Argonne “Science 101” public lecture series participant
- 20+ public talks - For the complete list, see the Talks page.