Our group leverages cutting-edge machine learning techniques to advance our understanding of cellular behavior and interactions. We use generative AI to enhance our understanding of cellular networks and tissue architecture. This involves integrating diverse data types, such as microscopy images and omics data, to simulate and predict biological behavior, ultimately improving disease diagnosis and treatment.
We are interested in developing and evaluating deep learning techniques applied to large multimodal datasets to map higher-order relationships and dependencies of single-cell and spatial features. This would enable deeper biological insights into the topology of cellular networks, cell-type-specific mappings, and cellular microenvironments at both spatial and functional levels.
Group Members:
We are hiring PhDs and Postdocs. Feel free to get in touch!