Publications: TensorGRaD: Tensor Gradient Robust Decomposition for Memory-Efficient Neural Operator Training (2025), Neural operators for accelerating scientific simulations and design (2024), A library for learning neural operators (2024), etc.; Open-Source Software Contributions: TensorLy, ZenCFG, Neural Operator, TensorLy-Torch.
Research Experience
Work Experience: Senior Research Scientist at NVIDIA, Founding Member of Samsung AI Center in Cambridge; Research Projects: TensorLy (Tensor Methods Library), NeuralOperator (Scientific Machine Learning Library), TensorLy-Torch (Deep Tensorized Neural Networks in PyTorch).
Education
Degree: PhD; School: Imperial College London; Advisor: Professor Maja Pantic; Field: Artificial Intelligence; Time: Not specified; Other Educational Background: French Engineering Diploma in Mathematics, Computer Science, and Finance, BSc in Advanced Mathematics.
Background
Research Interests: Tensor methods, Neural Operators, Scientific Machine Learning, Deep Learning. Bio: Leads research at NVIDIA in the field of AI for Engineering and Scientific Simulation, focusing on advancing new algorithmic paradigms to solve complex physics-based problems. Core research combines tensor methods with deep learning to develop efficient and powerful neural architectures.
Miscellany
Personal Mission: To democratize advanced computational techniques; Created and leads the development of two widely-used open-source libraries, TensorLy and NeuralOperator, to accelerate scientific discovery.