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About me
I am a Ph.D. candidate in the ARGO team at INRIA Paris and the Département d’Informatique de l’ENS (DI ENS, PSL Université), advised by Prof. Ana Bušić. I am also working with the famous scholar Prof. Jean-Michel Fourneau. My research focuses on Learning in Dynamic Matching Learning Models, a topic at the intersection of algorithms, optimization, and learning theory.
Previously, I earned an M.Sc. in Graph Theory, where I explored advanced topics such as the Matrix-Tree Theorem, family graphs with small rank, and the vector Laplacian on graphs. My work involved collaborations with leading researchers, including Prof. Jianfeng Wang, Dr. Ning Bian, and others.
I hold a B.Sc. in Applied Mathematics, where I built a strong foundation in Linear Algebra, Number Theory, and Modern Algebra—disciplines that continue to shape my research.
Beyond my thesis, I am passionate about bridging theoretical insights with practical applications in computer science and mathematics. You can learn more about my academic journey in my CV.
Research Interests
Algebra → Linear Algebra → Matrix Theory
I am interested in the structural properties of matrices and their roles in linear systems and algebraic computations.Combinatorics / Discrete Mathematics → Graph Theory → Spectral Graph Theory
I study the spectral properties of graphs, particularly how eigenvalues relate to graph structure, using algebraic techniques in discrete settings.Game Theory & Mechanism Design → Random Matching Models → Learning Behavior and the Performance Paradox
I explore random matching mechanisms under incomplete information, focusing on how individual learning dynamics can lead to systemic performance paradoxes.