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Quantitative finance
Backtesting engines, pricing, Greeks, VaR and regime-aware allocation - from Itô to production C++.
dSₜ = μSₜdt + σSₜdWₜ
Hi, my name is
Quant developer in Paris. I build pricing and risk tools, train models on noisy financial data, and spend my evenings with quantum circuits.
I live at the crossroads of applied mathematics, artificial intelligence and quantum computing - turning proofs into simulations, and simulations into tools people can actually run.
I came in through competitive math prep (MP*), then drifted to where mathematics comes alive: PDEs, stochastic processes, neural differential equations and, more and more, Hilbert spaces and quantum algorithms.
Today I build quantitative tooling at Louvre Banque Privée and co-run NotitiaPlatform, an AI-augmented newsroom. Off-screen: chess, arXiv, and long runs.
Tools I use:

What I work on:
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Backtesting engines, pricing, Greeks, VaR and regime-aware allocation - from Itô to production C++.
dSₜ = μSₜdt + σSₜdWₜ
λ
Deep learning on noisy time series, explainable models, and agentic LLM pipelines with an audit trail.
∇θ L = E[∇θ log πθ · A]
|ψ⟩
Hilbert spaces, density matrices and circuits - Qiskit, PennyLane, and a deep curiosity for what computes.
S(ρ) = −Tr(ρ log ρ)
A convolutional neural network for image classification, built with Keras on TensorFlow.
Hand gesture recognition using deep learning and OpenCV.
Birthday wisher with a countdown and a shareable generated link.
Abstract
Detects latent market regimes via HMM, GMM and spectral clustering, engineers 13 market features, and adapts allocation across five methods. Walk-forward backtests (2009-2023) show regime-conditional strategies beat naive baselines. 9-page LaTeX paper with auto-generated charts.
Abstract
Five classifiers (logistic regression, random forest, gradient boosting, XGBoost, neural networks) on synthetic credit data, side-by-side with three European call pricing methods: Black-Scholes analytical, Monte Carlo, and a neural-network approximator. Explainable-AI lens throughout.
Price
6.927
Δ
0.569
Γ
0.0253
ν
0.278
Θ/d
-0.021
ρ
0.250
d₁ = 0.174 · d₂ = 0.019 - payoff at expiry (grey) vs present value (blue)
step 1 / 6
A photon hits a photoreceptor in the retina. A single molecule, retinal, changes shape, and a chemical cascade turns light into a change of voltage. The world is now a pattern of electrical signals.
~120 million rods per retina
86 bn
neurons in a human brain
~10¹⁴
synapses, give or take
~20 W
the power of a light bulb
120 m/s
fastest signals on myelinated axons
Orders of magnitude from the literature (Azevedo et al. 2009 for the neuron count). The membrane curve model: Hodgkin and Huxley, 1952.