Data Assimilation

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
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
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
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
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

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

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

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