cv

PhD

• Supervised by Antoine Chambaz and Julie Josse, and Geneviève Robin
• Causal inference, Interpretability and Conformal Prediction
• Collaborating with clinicians to better understand treatment response heterogeneity in diverse settings

MVA Master

• First semester courses, Convex Optimization, Computational Statistics, Probabilistic Graphical Models, Reinforcement Learning, Geometric Data Analysis, Optimal Transport and Object Recognition

Cycle Ingénieur

• Specialized in Applied Mathematics
• Courses in Statistics, Machine Learning, Operations Research...
• GPA: 3.94/4.0

Classe Préparatoire MPSI-MP*

• Intensive two-year high-level program to prepare for the competitive entrance

Teaching Assistant

Research Intern

• Prediction of patient chemotherapy response from baseline gene expression profiles using models trained on pre-clinical data.
• Worked on bulk, single-cell and spatial RNA-sequencing data from the MOSAIC initiative.

Research Intern

• Research internship supervised by Simon Tavaré and Khanh Dinh.
• Mathematical modeling and simulation of ecDNA dynamics in cancer cell populations.
• Moran process with selective pressure, ABC inference. Python code available on GitHub.

R&D Intern

• Worked on evaluating the robustness of a Deep Learning algorithm for lung nodule detection and segmentation.
• CT scan images
• Python code

Tutor

• Tutoring third-year Bachelor students in Asymptotic Statistics.