Shishun Liang is a synthetic biologist with interests in metabolic engineering, directed evolution, and AI-assisted biological design. His research focuses on developing data-driven approaches to understand and engineer cellular metabolism.

During his PhD, he worked on integrating CRISPR-based genetic perturbation libraries with machine learning and active learning frameworks to accelerate the discovery and correction of genome-scale metabolic networks.

More broadly, he is interested in combining synthetic biology, automation, and artificial intelligence to enable more efficient engineering of microbial systems for biotechnology and sustainable production of valuable compounds.