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 [take a look]. 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.

Email  /  CV  /  Scholar  /  Github /  Linkedin

profile photo

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

Ph.D Thesis

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

Supervisors: Prof. 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

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