Meet the People

I’m an Assistant Professor at the Department of Genetics, the BASE program, and the Department of Computer Science at Stanford. I worked with Dr. Cole Trapnell at the University of Washington during my PhD to develop Monocle 2 and 3 for pseudotemporal trajectory analysis of scRNA-seq data. During my post-doc, I worked with Dr. Jonathan Weissman initially at UCSF and then Whitehead Institute and MIT to develop Dynamo to reconstruct RNA velocity vector field and make reprogramming and in silico perturbation predictions with metabolic labeling enabled single-cell RNA-seq. I recently also lead the development of Spateo, for advanced spatiotemporal modeling of single cells. My lab is currently focused mostly on predictive modeling of single cells through a uniuqe synthesis of genomics and machine learning. Outside the lab, I like going to the gym, run and read all kinds of books.

Papers

• Mapping transcriptomic vector fields of single cells
• Systematic functional interrogation of SARS-CoV-2 host factors using Perturb-seq
• Storm: Incorporating transient dynamics to infer the RNA velocity with metabolic labeling information
• Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics
• Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
• Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration
• Inferring gene regulation from stochastic transcriptional variation across single cells at steady state
• Graph-Dynamo: Learning stochastic cellular state transition dynamics from single cell data
• Reversed graph embedding resolves complex single-cell trajectories
• Thyroid hormone regulates distinct paths to maturation in pigment cell lineages
• Single-cell mRNA quantification and differential analysis with Census
• Inferring causal gene regulatory networks from coupled single-cell expression dynamics using scribe
• Aligning single-cell developmental and reprogramming trajectories identifies molecular determinants of myogenic reprogramming outcome
• The single-cell transcriptional landscape of mammalian organogenesis
• Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data
• Comprehensive single-cell transcriptional profiling of a multicellular organism
• Single-cell transcriptomics reveals receptor transformations during olfactory neurogenesis
• Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq
• From understanding the development landscape of the canonical fate-switch pair to constructing a dynamic landscape for two-step neural differentiation
• Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution
• A transcriptional dynamic network during Arabidopsis thaliana pollen development
• HCCNet: an integrated network database of hepatocellular carcinoma
• Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape
• Massively parallel base editing to map variant effects in human hematopoiesis
• The cis-regulatory dynamics of embryonic development at single-cell resolution
• A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition