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
Variational Flow Maps: Make Some Noise for One-Step Conditional Generation
Variational Flow Maps (VFM) recasts conditional generation as a problem of learning the right initial noise distribution for a pretrained or jointly trained one-step flow map. An observation-dependent adapter transforms simple noise before the flow map sends it to data space, enforcing the measurement while retaining the learned data prior.
Jul 7, 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
ACM 154: Inverse Problems and Data Assimilation
In winter 2026, I co-taught Caltech ACM 154, Inverse Problems and Data Assimilation, with Prof. Andrew Stuart.
Jan 5, 2026