I am thrilled to share a paradigm-changing work in generative modeling: Flux Matching by the very brilliant graduate student Peter @peterpaohuang (co-mentored with @StefanoErmon). By extending beyond the score functions used in diffusion models to a broader class of vector

I am thrilled to share a paradigm-changing work in generative modeling: Flux Matching by the very brilliant graduate student Peter

(co-mentored with

). By extending beyond the score functions used in diffusion models to a broader class of vector fields, Flux Matching enables structural priors in dynamics, faster sampling, more interpretable generation, and many new possibilities. In biology, Peter shows that replacing the EM algorithm in scVelo with Flux Matching can dramatically improve RNA velocity accuracy, including cross-boundary correctness and consistency. Its ability to train on large-scale single-cell and perturbation data makes it especially exciting for building better causal virtual cell and virtual embryo models. I am deeply grateful for the support from Laude Institute

, Pantas and Ting Sutardja Foundation, the Wu Tsai Neurosciences Institute Big Ideas in Neuroscience Program, NIH DP2 grant 1DP2OD037052-01, and NIH K99/R00 grant 4K99HG012887-02

. Most importantly, I am deeply honored to have Peter as the first graduate student in the lab! I want to congratulate Peter on this outstanding achievement. He developed this idea independently, drawing on his background in causal learning, diffusion models, and Perturb-seq, and pushed through many technical challenges with remarkable creativity, persistence, and diligence. I cannot wait to see the impact this work will have in both machine learning and biology! See more information from Peter below: