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
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
Learning Probabilistic Filters with Strictly Proper Scoring Rules
The proper scoring ensemble filter (PSEF) learns an ensemble analysis operator that targets the complete Bayesian filtering distribution rather than only its conditional mean. The operator takes a forecast ensemble and a new observation as input and returns an analysis ensemble. A permutation-invariant transformer ensures that the result respects the exchangeability of ensemble members and can be evaluated at different ensemble sizes.
Jun 25, 2026
Flow Matching for Efficient and Scalable Data Assimilation
The ensemble flow filter (EnFF) is a training-free data-assimilation framework that uses flow matching to transform a forecast ensemble into samples from the filtering distribution. Its Monte Carlo flow-field estimator and localized observation guidance avoid model training while retaining the flexibility of generative flow design.
Jun 15, 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
AGU25 Oral — Learning Enhanced Ensemble Filters
AGU25 oral talk — Tue, Dec 16, 2025, 09:42–09:52 CT (07:42–07:52 PT), Session NG21A.
Nov 2, 2025