I am a PhD candidate in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering. Before joining NYU, I earned my Ingénieur Polytechnicien degree from École Polytechnique and my M.Sc. (MVA) from Institut Polytechnique de Paris.
My research focuses on data-driven decision making under uncertainty. I develop mathematical models for learning and decision systems, bringing together machine learning, stochastic control, optimization, and geometry to enable reliable decisions in uncertain environments. I am interested in how an agent should act on the data it has, when and how it should gather additional information before acting, and how it should make decisions in strategic settings in the presence of other agents.
Published Papers
- GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning
- GeoHNNs: Geometric Hamiltonian Neural Networks
- Aggregation Dispersion: An Information-Geometric Diagnostic of Oversmoothing in Graph Neural Networks