Chenlin Meng is a CS Ph.D. student at Stanford University. She was previously an intern at Google Brain working with Tim Salimans and Jonathan Ho. Her research interests include diffusion models, variational autoencoders, normalizing flows and other types of large-scale generative models. Specifically, she is interested in making large-scale generative models fast, controllable and scalable in real-world settings. She is a fellow of Stanford Interdisciplinary Graduate Fellowship and is awarded the young researcher award for score-based generative model at NeurIPS 2022. Chenlin has served as a reviewer for ACM SIGGRAPH, CVPR, ECCV, ICCV, ICLR, and NeurIPS for several years.