Welcome
I am Jules Perret, a PhD researcher and research engineer in gravitational astrophysics at the Astroparticle and Cosmology Laboratory (APC, CNRS). My work focuses on data analysis for the LISA space mission, combining Bayesian inference and advanced computational methods to extract astrophysical insights from gravitational-wave signals.
I specialize in developing efficient parameter estimation techniques for compact binary systems—binary neutron stars and binary black holes—using statistical inference, deep learning, and Hamiltonian-based sampling methods. My research aims to accelerate gravitational-wave analysis while maintaining high accuracy, leveraging high-performance computing, GPU acceleration, and large-scale parallel simulations.
Research Focus
Gravitational-Wave Data Analysis 🔭
Extracting physical parameters from gravitational-wave signals detected by current and future observatories (LIGO, Virgo, Kagra, LISA, Einstein Telescope).
Bayesian Inference & Sampling Methods 📊
Developing and applying advanced sampling algorithms to navigate complex, high-dimensional posterior distributions efficiently.
High-Performance Computing 💻
Implementing GPU-accelerated codes and parallel computing frameworks to handle computationally intensive astrophysical simulations.
Collaborations
- LIGO-Virgo-Kagra (LVK) — Ground-based gravitational-wave detection 💫
- Einstein Telescope (ET) — Next-generation gravitational-wave observatory
- Société Astronomique de Bourgogne (SAB) — Astronomy outreach and education
Featured Projects
DeepHMC
A deep neural network-based Hamiltonian Monte Carlo algorithm designed for efficient compact binary parameter estimation. By learning the posterior geometry, DeepHMC significantly reduces the computational cost of Bayesian inference for gravitational-wave sources.
Vanilla HMC
A pedagogical 2-dimensional Hamiltonian Monte Carlo implementation for outreach and teaching. This interactive tool helps students and researchers build intuition about gradient-based sampling methods.
➡️ Explore the code
Bayesian Inference Introduction
An interactive guide to Bayesian methods and sampling algorithms:
- Introduction to Bayesian Inference — From Bayes’ theorem to posterior sampling
- MCMC Sampler — Metropolis-Hastings fundamentals and diagnostics
- HMC Sampler — Hamiltonian dynamics for efficient exploration
Beyond Research
Outreach & Teaching
Active member of the Société Astronomique de Bourgogne, sharing the wonder of astronomy with the public and mentoring aspiring scientists.
Builder & Maker 🚀
Enthusiastic about drones, UAVs, and hands-on engineering projects. I enjoy bridging the gap between theoretical astrophysics and practical hardware development.
Connect
- 🔗 GitHub — Explore my open-source code and projects
- 🏛️ APC Laboratory — Research group and collaborations
- 📧 Contact me for collaborations, questions, or speaking opportunities
This site showcases my research, educational materials, and explorations at the intersection of astrophysics, computation, and inference.