Learning objectives

• Use continuous flow-based generative models to uncover cell-state dynamics from sparse scRNA-seq data.
• Apply the stVCR framework to analyze time-series spatial transcriptomics and simulate tissue development.
• Connect regularized unbalanced optimal transport (RUOT) with Schrödinger Bridges and diffusion models for temporal scRNA-seq analysis.
• Integrate cellular interactions with efficient deep generative solvers in single-cell transcriptomics.

Speaker biography

Dr. Peijie Zhou is a tenure-track Assistant Professor at the Center for Machine Learning and the Center for Quantitative Biology, Peking University. He earned his B.S. and Ph.D. in Computational Mathematics from Peking University. From 2020 to 2023, he was a Visiting Assistant Professor at UC Irvine. His research focuses on computational systems biology, especially AI-driven modeling of complex biological systems using single-cell data, with work published in interdisciplinary journals like Nature Methods, Nature Communications and PRX.