Bio:

CiCi Zheng is a Shanahan Foundation Fellow at the Allen Institute and the University of Washington, where she develops computational and theoretical tools to understand how the cortical cell types are established during development, how that complexity supports learning, and how the structure of experience itself shapes what brains and machines can learn. Her current work spans methods for integrating multi-modal omics data to map developmental trajectories, discovering cell states in the adult brain, and theory for how data arrival and task structure influence continual learning.

She completed her Ph.D. in Quantitative Biology at Cold Spring Harbor Laboratory, advised by Saket Navlakha and Alexei Koulakov, with a thesis on statistical modeling of networks in natural systems. Her interdisciplinary research background spans plant developmental networks (including leaf vein reticulation and root foraging strategies) before transitioning into neuroscience, where she studied sensory circuit adaptation and representational drift in the olfactory system, as well as representation and learning dynamics in neural networks.

Broadly, CiCi is drawn to how complex biological systems assemble with both robustness and plasticity, and what principles connect the formation of neural circuits to the learning they eventually carry out. At the Allen Institute, she is grateful to work alongside experimentalists and theorists to bring quantitative insights to neuroscience questions across scales.