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
ILAS 2026 Talk - Learning Enhanced Ensemble Filters: Continuum Limits of Attention on Measures
I will speak at ILAS 2026 on Tuesday, May 19, 2026, 2:50 PM Eastern, in McBryde Hall 113 at Virginia Tech.
May 19, 2026
Talk Announcement: SIAM Student Chapter Talk - Learnable Operators on Probability Measures for Ensemble Data Assimilation
I will give a SIAM student chapter talk on Wednesday, March 11, 2026, 12:00-1:00 PM Pacific at ANB 213, Caltech.
Mar 4, 2026
Talk Announcement: JHU AMS Postdoc Seminar - Learnable Operators on Probability Measures for Ensemble Data Assimilation
I will give a JHU AMS postdoc seminar on Wednesday, March 11, 2026, 12:30-1:30 PM Eastern (9:30-10:30 AM Pacific).
Mar 4, 2026
Learning Enhanced Ensemble Filters
This work introduces the measure neural mapping enhanced ensemble filter (MNMEF), a learning-based data-assimilation method derived from a mean-field formulation of the filtering problem. Measure neural mappings extend neural operators to maps acting on probability measures; their finite-ensemble implementation uses a permutation-invariant set transformer.
Feb 15, 2026