I will present our work on learning nonlinear, non-Gaussian filtering distributions in a three-part minisymposium on structure-preserving data assimilation and learning.
I wrote a detailed GitHub repository of textbook-ready demo notebooks for Machine Learning for Inverse Problems and Data Assimilation.
Our new PSEF framework learns calibrated ensemble filters from simulated trajectories without requiring the true filtering distribution as a training target.
Our ensemble flow filter brings training-free flow matching to efficient, scalable data assimilation.
Hojjat Kaveh, Nicholas Nelsen, and I are organizing a minisymposium on measure transport at the 2026 SIAM Conference on Mathematics of Data Science.
Our ICML 2026 paper learns the right initial noise for fast, calibrated conditional generation, inverse problems, and reward alignment.
Our measure neural mapping enhanced ensemble filter has been published in the Journal of Computational Physics.
I will be a mentor for the Caltech Summer Undergraduate Research Fellowships (SURF) program in 2026.
I will be a mentor for Caltech Summer Undergraduate Research Fellowships (SURF) program in 2025.
I designed a unique personal icon inspired by mathematical symbols to represent my identity.
