Invited Talk at SIAM MDS26: Learning Filtering Distributions Using Strictly Proper Scoring Rules
I will present our work on learning nonlinear, non-Gaussian filtering distributions in a three-part minisymposium on structure-preserving data assimilation and learning.
Jul 14, 2026
GitHub Demos for Machine Learning for Inverse Problems and Data Assimilation
I wrote a detailed GitHub repository of textbook-ready demo notebooks for Machine Learning for Inverse Problems and Data Assimilation.
Jul 8, 2026
Learning the Whole Filtering Distribution with Proper Scoring Rules
Our new PSEF framework learns calibrated ensemble filters from simulated trajectories without requiring the true filtering distribution as a training target.
Jun 25, 2026
Flow Matching for Data Assimilation Accepted in SIAM/ASA JUQ
Our ensemble flow filter brings training-free flow matching to efficient, scalable data assimilation.
Jun 15, 2026
Organizing ‘Measure Transport for Inverse Problems and Data Assimilation’ at SIAM MDS26
Hojjat Kaveh, Nicholas Nelsen, and I are organizing a minisymposium on measure transport at the 2026 SIAM Conference on Mathematics of Data Science.
May 18, 2026
🔊 Make Some Noise: Variational Flow Maps for One-Step Conditional Generation
Our ICML 2026 paper learns the right initial noise for fast, calibrated conditional generation, inverse problems, and reward alignment.
Mar 10, 2026
Learning Enhanced Ensemble Filters Published in JCP
Our measure neural mapping enhanced ensemble filter has been published in the Journal of Computational Physics.
Dec 11, 2025