Paris, Île-de-France, France
PhD candidate in Systems Immunology and Bioinformatics at Sorbonne Université and Inserm, specializing in computational immunology, T-cell receptor (TCR) repertoire analysis, and machine learning. Research focused on understanding T-cell antigen specificity and regulatory T-cell biology in autoimmunity through computational approaches, including immune repertoire analysis, protein language models, representation learning, and structural modeling. Background in structural bioinformatics and in silico drug design, with experience in protein structure prediction, molecular docking, and protein–protein interaction analysis. Research interests lie at the intersection of computational immunology, AI-driven structural biology, and therapeutic protein design, with a particular interest in molecular recognition and immune-related applications.
Doctoral research at the i3 laboratory (Inserm UMR-S 959, Sorbonne Université), focusing on T-cell receptor (TCR) antigen specificity and regulatory T-cell biology in type 1 diabetes and autoimmunity. Research activities include: • Analyzing TCR repertoires to investigate antigen specificity, immune diversity, and disease-associated signatures. • Developing and applying machine learning approaches, including protein language models, representation learning, and predictive modeling. • Investigating TCR-peptide-MHC interactions using protein structure prediction, molecular docking, and structural analysis. • Analyzing sequencing data and developing reproducible computational workflows using Python, R, Snakemake, and HPC infrastructure. Teaching and supervision: • Teaching practical sessions in Python, R, biostatistics, and biological data analysis to undergraduate and master’s students at Sorbonne Université. • Supervising four research interns on computational biology and bioinformatics projects. Additional academic activities include contributing to scientific publications, presenting research at conferences, and peer reviewing for Nature Communications (2025, 2026).
Identification of a T Cell Receptor (TCR)-based signature for type 1 diabetes (T1D) in a non-obese diabetic (NOD) mouse model Clustering of TCR with potentially the same specificity Training binary classification model using deep autoregressive generative model (Recurrent Neural Network with Gated Recurrent Unit) Software used: Python, R, Bash, NGS data analysis (TCR repertoires), Machine Learning Immunology, Immunopathology, Immunotherapies (i3), Hôpital Pitié-Salpêtrière
Analysis of Influenza A Virus Protein NS1 Structural Polymorphism Based on Linker’s Length and Viral Subtype Performed Molecular Dynamics Simulations on 18 systems of the viral protein. Quantified domain's mouvement in MD' simulation using Solvent Accessible Surface Area (FreeSASA) and centroids distance (developed python scripts). Optimized and automated the calculations for the large amount of data generated. Classified and clustered the systems using Machine Learning (k-means, Multidimensional scaling, and PCA). Computational Modeling of Protein-Ligand Interactions (Unit of Functional and Adaptive Biology)
Modeling inhibitor complexes of HIV-1 integrase deriving from the diketoacid family at the viral DNA-protein interface. Analyzing the modified inhibitors using qualitative studies (NCIPlot and MEP: Molecular Electrostatic Potential Maps) and a quantitative study (quantum chemistry, density-functional theory, Gaussian software). Laboratory of Macromolecular Structure and Interaction
Participated in a project led by the production department and explored the different departments of an industry in order to gain a better understanding of how the different sections of a company operate and communicate.