Hoang Trieu Vy LE

2025 –

I joined Beink Dream as a Senior Research Scientist in Paris, where I architect end-to-end agentic AI systems. I work on multiple challenging problems — from generative images with flow to building multi-modal systems that maintain a structured internal representation of scene state across 2D, 3D, and CAD modalities.

2024 – 2025

I was a Research Engineer at CEA – DIGITEO Labs in Saclay, where I worked on lightweight learning models for 3D CT reconstruction — building physics-consistent scene representations from sparse, noisy measurements and limited high-dimensional observations.

2020 – 2023

My PhD at ENS Lyon was focused on variations of the Mumford-Shah functional for interface detection in degraded images, bridging proximal algorithms and unrolled deep architectures. My advisers were Prof. Nelly Pustelnik and Dr. Marion Foare. Along the way I spent a year at UC Louvain (2020–2021) designing scalable accelerated algorithms for joint image restoration and edge detection on large-scale physics experiment images.

I also taught Introduction to Machine Learning and Optimisation at EPITA, and Machine Learning & Optimization hands-on courses at ENS Lyon.

2015 – 2020

I studied at INSA Toulouse where I earned my Master of Engineering in Applied Mathematics, majoring in Mathematical Modelling and Numerical Analysis.

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Ph.D Thesis

Variations on the Mumford-Shah functional for interface detection in degraded images: from proximal algorithms to unrolled architectures
[PDF]

Supervisors: Dr. Nelly Pustelnik, Dr. Marion Foare

Reviewers: Prof. Caroline Chaux, Assoc. Prof. Matthieu Kowalski

Examiners: Prof. Jean-François Aujol, Prof. Elie Bretin, Prof. Luca Calatroni, Prof. Marc Sebban

Publications

My research spans unfolded neural networks, image processing, computer vision, and generative AI.

Unfolded discrete Mumford-Shah functional for joint image denoising and edge detection
HoangTrieuVy LE, Marion Foare, Audrey Repetti, Nelly Pustelnik
EUSIPCO 2025
[PDF]
Embedding Blake-Zisserman Regularization in Unfolded Proximal Neural Networks for Enhanced Edge Detection
HoangTrieuVy LE, Marion Foare, Audrey Repetti, Nelly Pustelnik
IEEE Signal Processing Letters, 2025
[PDF]
Proximal Neural Networks based reconstruction for few-view CT applications
HoangTrieuVy LE, Caroline Bossuyt, Marius Costin, Hang Wang, Jan De Beenhouwer, Jan Sijbers
14th Conference on Industrial Computed Tomography (iCT 2025), Antwerp, Belgium
[PDF]
Unfolded proximal neural networks for robust image Gaussian denoising
HoangTrieuVy LE, Audrey Repetti, Nelly Pustelnik
IEEE Transactions on Image Processing, 2024
code / [PDF]/ BibTeX
Deep image prior for sparse-view reconstruction in static, rectangular multi-source X-ray CT systems for cargo scanning
Caroline Bossuyt, Jan De Beenhouwer, Jan Sijbers, Domenico Iuso, Marius Costin, Julie Escoda, HoangTrieuVy LE, Arjan J den Dekker
Developments in X-Ray Tomography XV, SPIE, 2024
[DOI]
Proximal based strategies for solving Discrete Mumford-Shah with Ambrosio-Tortorelli penalization on edges
HoangTrieuVy LE, Marion Foare, Nelly Pustelnik
IEEE Signal Processing Letters, 2022
code / [PDF]/ BibTeX
The faster proximal algorithm, the better unfolded deep learning architecture ? The study case of image denoising
HoangTrieuVy LE, Nelly Pustelnik, Marion Foare
EUSIPCO 2022
code / [PDF]/ BibTeX
Fast Proximal Unrolled Algorithms for the Analysis of Piecewise Homogeneous Fractal Images
HoangTrieuVy LE, Nelly Pustelnik, Barbara Pascal, Nelly Pustelnik, Marion Foare, Patrice Abry
GRETSI 2022
[PDF]/ BibTeX

Talks

EUSIPCO, Belgrade, Serbia, Aug 2022
Conference on Digital Geometry and Discrete Variational Calculus, Mar 2021

Teaching

Introduction to Machine Learning and Optimisation, EPITA
Machine Learning and Optimization hands-on courses at ENS Lyon