Research

My research develops machine learning methods that are not only accurate but also know when they might be wrong, a critical property for deployment in high-stakes industrial environments. I currently work with Michelin and Augmodo, applying uncertainty-aware ML to manufacturing and retail.

Current Research

Personal Exploration: Diffusion Models

Outside my PhD, I explore topics that spark my curiosity. I am currently looking into diffusion models and their potential for live video generation and industrial simulation, as part of an entrepreneurial project bridging generative AI with practical applications.

Research at Augmodo

Inference-Time Uncertainty Quantification

As a Computer Vision Scientist at Augmodo, I develop uncertainty-aware vision models for open-world retail environments — settings where the camera encounters products, lighting conditions, and edge cases never seen during training. I work alongside Sacha Hu (Senior CV Engineer), Ashley Napier (CV Group Lead), and Shubham Wagh (Senior CV Engineer) on pipelines that process millions of images daily. My contributions include:

• Uncertainty-aware quality control: building open-world classifiers for product identification across tens of thousands of retail shelf images, using test-time augmentation to quantify prediction confidence and reduce manual QC workload by selectively accepting only high-confidence predictions
• Empty shelf area flagging: an RGB+depth classification pipeline that identifies truly empty shelf areas, enabling QC teams to focus human review on genuinely uncertain cases
• Low-cost dataset relabeling: leveraging improved object detectors to update legacy bounding box annotations at scale, adapting existing large datasets to newer models without costly manual re-annotation

Research at Michelin

My PhD at the Centre de Mathematiques Appliquees (CMAP) - Ecole Polytechnique - in collaboration with Michelin, focuses on predicting rubber quality in real time, directly on the production line. Here are the three pillars of this work:

Adaptive Soft Sensing

In tire manufacturing, product quality is traditionally measured offline in a laboratory, a process that is slow, expensive, and arrives too late to correct production issues. I develop soft sensors: machine learning models that predict quality from process data in real time. To handle the fact that industrial processes constantly evolve and could suffer from sudden and gradual drifts, I explore three complementary adaptation strategies:

• Moving Window — learns from the most recent data, tracking temporal shifts in the process
• Just-in-Time Learning — retrieves the most similar historical batches to build sample-specific predictions
• Ensemble methods — combines multiple models for enhanced robustness under changing conditions

Validated on 35,125 production batches at Michelin, with SHAP-based feature selection achieving over 80% dimensionality reduction while improving prediction accuracy by up to 61% over global baselines.

Online Uncertainty Quantification

A prediction is only useful if you know how much to trust it. I develop uncertainty quantification methods, with a focus on conformal prediction, that wrap any soft sensor with statistically valid prediction intervals. Two main contributions:

• JiT-CP (Just-in-Time Conformal Prediction): addresses the fact that standard conformal guarantees are marginal (correct on average) but can fail for specific process regimes. JiT-CP provides locally adaptive coverage by retrieving similar historical batches in a SOM-based latent space, yielding prediction intervals that remain reliable even when the process shifts.
• AnyhowCP (Anyhow Conformal Prediction): a flexible framework for anytime monitoring with budget allocation. Instead of expensive martingale tracking, it uses pre-computed thresholds and configurable monitoring contracts (full-sequence, sliding windows), enabling cost-aware sequential testing with distribution-free guarantees.

Unsupervised Learning and Dimensionality Reduction

I developed TorchSOM, an open-source PyTorch library for Self-Organizing Maps (SOMs) designed for industrial-scale data. SOMs preserve the topology of high-dimensional data at a local level, making them ideal for process monitoring and as a backbone for Just-in-Time retrieval. TorchSOM powers the similarity search at the heart of my soft sensing and conformal prediction pipelines.

TorchSOM achieves 77-99% faster training than MiniSom (on both CPU and GPU) with 34-81% better topographic error, and offers a scikit-learn-compatible API with advanced clustering support (K-Means, GMM, HDBSCAN) and several visualization tools.

Previous Research

Research at the Adaptive & Intelligent Robotics Lab (AIRL)

Model-Based Quality-Diversity Optimization

During my MSc at Imperial College London, supervised by Pr. Antoine Cully, I worked on Quality-Diversity (QD) optimization — a family of evolutionary algorithms that doesn’t just find one good solution, but a diverse repertoire of high-performing ones. Traditional QD methods require hundreds of thousands of evaluations, which is impractical for expensive simulations. I tackled this by integrating surrogate models and active sampling, testing eight algorithmic variants on a robotic arm control task and achieving significant gains in sample efficiency.

Research at the CNRS Brain & Cognitive Research Center (CerCo)

Detection of Pathological Oscillations in Epilepsy

During my MSc at IMT Mines Ales, supervised by Dr. Ludovic Gardy, Pr. Christophe Hurter, and Pr. Emmanuel Barbeau, I developed deep learning methods for detecting fast ripples — brief, high-frequency bursts in brain signals that serve as biomarkers for epileptic tissue. The pipeline converts raw EEG signals into time-frequency images (via continuous wavelet transform), classifies them with CNNs, and uses Grad-CAM to highlight which parts of the signal drive the model’s decision — giving clinicians both a prediction and a visual explanation.