Felipe Vicentin

Image Engineering Research Intern · DxO Labs

Boulogne-Billancourt, France

• Researched multi-frame image fusion for denoising in low-light conditions within DxO's research lab, developing code with PyTorch on GPU, NumPy, OpenCV, and matplotlib.
• Achieved a 5 dB PSNR improvement on AWGN noise in RGB images.
• Full-time position (35 h/week) over 6 months, equivalent to the final master's research placement in the French system.
• Supervised by Sylvain Leroy, Thomas Veit and Ricardo Sapaico.

Undergraduate Research Fellow (FAPESP-funded) · Unicamp, IMECC

Campinas, Brazil

• Researched partial orders and lattices over finite chains, advised by Prof. Peter Sussner (Department of Applied Mathematics).
• Developed rigorous proofs on counting and constructing admissible orders of intervals over finite chains, published in Fuzzy Sets and Systems (DOI: 10.1016/j.fss.2025.109372).

On counting and constructing all admissible orders of the non-empty intervals in any finite chain

Data Engineering Intern · Dextra Systems

Campinas, Brazil

• One-year internship at a large IT consultancy (~1,000 employees), as the only intern in an agile squad delivering to enterprise clients across sectors ranging from food to banking.
• Built PySpark and Delta Lake pipelines on a layered data lake (ingestion, anonymisation for LGPD compliance, standardisation, KPIs) over conversation data, running on GCP with Airflow, Cloud Storage, BigQuery and Dataproc across separate production and staging environments.
• Delivered production pipelines for 4 enterprise clients and 15+ KPIs, including an ML layer running LDA topic modelling over cleaned text.

Master of Science (M2), MVA (Mathematics, Vision, Learning)

Université Paris-Saclay, ENS Paris-Saclay · Paris, France

• Double degree between Unicamp and IP-Paris. Widely regarded as one of the leading machine learning master's programmes worldwide.
• Representation Learning, Convex Optimization, Optimal Transport, Probabilistic Graphical Models, Geometric Deep Learning, Generative Models for Images, Kernel Methods, Reinforcement Learning.
• Generative Models for Images, Large-Scale Model Training and Deployment, Advanced Deep Learning, Time Series, Reinforcement Learning.

Subjects I have written about

• optimal transport
• convex optimization
• functional analysis

Master of Engineering (Diplôme d'Ingénieur)

Institut Polytechnique de Paris, Télécom Paris · Palaiseau, France · Grade 16.3 / 20

• Alumni excellence scholarship.
• Linear Models, Machine Learning, Variational and Bayesian Methods and Discrete Optimization, Image Learning and Object Recognition, Statistics, 3D Vision and Video.

BSc in Computer Science

University of Campinas (Unicamp) · Campinas, Brazil · Grade 9.34 / 10

• Calculus, Linear Algebra, Discrete Mathematics, Databases, Algorithm Analysis, Data Structures, Operating Systems, Introduction to Cryptography.

Technical Diploma in Computing

Colégio Técnico de Campinas (COTUCA) · Campinas, Brazil

vicentin

• Python library with a dual-backend architecture that transparently switches between NumPy and PyTorch based on input data, enabling automatic differentiation and GPU use with no API change.
• From-scratch implementations in optimization (Newton, barrier, proximal/ISTA), machine learning (SVM, PCA, kernel methods) and deep learning (VAE, Wasserstein GANs), plus paper reimplementations (Grad-CAM, SimCLR).

Multi-categorical LAFTR

• Implementation of the LAFTR model, extending it to multi-categorical settings. Supervised by Prof. Loïc Le Folgoc.
• Learns representations that stay predictive while making a protected attribute hard to recover — the core problem behind fairness auditing in credit and risk decision systems.

Image restoration

• Implementation of variational techniques (Tychonov and Total Variation) to invert transformation kernels on noisy images, using NumPy and PyTorch. Supervised by Prof. Arthur Leclaire.