I hold a PhD in Mathematics from ETH Zurich and am a postdoctoral researcher at the Chinese University of Hong Kong, Shenzhen. My research focuses on the mathematical theory of deep learning and on learning-based numerical methods. I am currently particularly interested in the application of diffusion models in the approximation of PDEs.

Research interests Scientific Machine Learning · Diffusion Models · High-dimensional Approximation · Numerical Methods for PDEs · Deep learning theory

CV

• ETH Medal (2019) for an outstanding Master's thesis

• 05/2018 — Stochastic Optimization Seminar, ETH Zurich
  Error analysis and lower error bounds for SGD.
• 05/2019 — SMAI, Guidel Plages
  Overcoming the curse of dimensionality with DNNs: Theoretical approximation results for PDEs. [Slides]
• 07/2019 — International Conference on Computational Finance, A Coruña
  Overcoming the curse of dimensionality with Deep Learning: Methods and theoretical results for PDEs. [Slides]
• 06/2022 — 11th World Congress of Bachelier Finance Society, Hong Kong
  Overcoming the curse of dimensionality in the approximation of semilinear Black-Scholes PDEs. [Slides]
• 06/2023 — Foundations of Computational Mathematics, Paris
  Learning the random variables: Combining Monte Carlo simulations with machine learning. [Slides]

Research