CV

Université Paris-Saclay

Master of Science, Mathematics Vision Learning (MVA)

• Courses: Probabilistic Graphical Models and Deep Generative Models, Geometric Data Analysis, Algorithms and learning for protein science, Reinforcement learning, Advanced learning for text and graph data ALTEGRAD, etc.
• Projects: Development of ML tools for 4 months in partnership with a doctor from Hôpitaux de Paris.

Ecole Polytechnique

Master of Science (Cycle Ingénieur), Applied Mathematics

• Courses: Advanced Machine Learning, Autonomous Agents, Machine Learning and Deep Learning, Stochastic Processes, Monte Carlo Methods, Biomedical Imaging
• Projects: Cross-diffusion in population dynamics, Gaussian Processes and Kriging

Collège Stanislas

Bachelor of Science (Preparatory Classes: CPGE), Mathematics, Physics and Chemistry

• Ranked 10th at Ecole Polytechnique and 7th at Ecole Normale Supérieure (ENS Ulm)

Research Intern, Immunoinformatics and Machine Learning group

DTU, Department of Health Technology

Developed and optimized machine learning algorithms for antigen specificity prediction in T-Cell Receptors

• Researcher in the Immunoinformatics and Machine Learning Group, under the supervision of Professor M. Nielsen.
• Developed and optimised machine learning algorithms to predict the antigen specificity of T-cell receptors (TCRs), significantly advancing immune response modelling.
• Led the enhancement of the NetTCR model, progressing from NetTCR2.2 to NetTCR3.0 by implementing a novel Recurrent Neural Network (RNN)-based architecture, resulting in a 5% improvement in AUC (Area Under the Curve) performance. The upgraded model is now recognised as the first academic model in its field. (Manuscript in progress, scheduled for publication in the coming months).

Data Scientist Intern, Target Ranking team

AQEMIA

Advanced therapeutic target identification using omics data

• Enhance therapeutic target identification by leveraging multi-omics data, including proteomics, genomics, and transcriptomics, to streamline the discovery of novel drug targets and optimise target validation models for the drug development pipeline.
• Analyse large-scale RNA sequencing datasets from public repositories (e.g., UniProt, GEO, and others) to uncover insights into gene expression profiles across diverse diseases.
• Apply advanced statistical models and implement multiple testing correction methods, such as Benjamini & Hochberg, to identify differentially expressed genes, ensuring the selection of the most promising therapeutic targets.

Vice President

X-Forum

Organized Ecole Polytechnique's career fair, one of the largest in France

• Organising Ecole Polytechnique's career fair, one of the largest in France

Research Project : Kriging and Gaussian Processes

Research Team Project on Gaussian Processes for Kriging under the supervision of Pr Thierry Klein, Ecole Polytechnique’s CMAP.

• Gaussian Processes
• Different Kernels study
• Parameters estimation

Machine Learning tools for Time Series

Team Project as part of MAP565 : Modélisation aléatoire et statistique des processus final project.

• Time Series Forecasting
• GARCH/ARCH processes
• Copulas
• Extreme Value Analysis
• GP and Kriging

Extractive Summarization with Discourse Graphs

Team Project as part of INF554 : Machine Learning and Deep Learning course of Pr M. Vazirgiannis.

• Machine Learning for text featurization
• Deep learning methods, especially ANN with LSTM

Cross-diffusion in population dynamics (PySpecies and PySpeciesStochastic)

Collective Scientific Project on Cross-diffusion in population dynamics under the supervision of Professor V. Bansaye and Professor M. Breden, Ecole Polytechnique’s CMAP),.

• PySpecies
• PySpeciesStochastic

Important Samplig on Football Championship data Analysis

Project on Important sampling and Splitting Method for the analysis of football championship data.

• Important Sampling
• Splitting Method