2026-06
Zhijian accepts an offer to join the School of Medicine, CUHK-Shenzhen, as Assistant Professor.
>> Colab · CUHK-Shenzhen Decoding the language of life
Decoding the language of life
through computation.
We build computational and machine-learning models to decode genome regulation, cellular state, and disease mechanisms — turning high-dimensional biological data into testable hypotheses for human health.
1 Publications
1 Postdocs
1 PhD students
1 Research assistants
About the PI
Principal Investigator at the School of Medicine, CUHK-Shenzhen.
Zhijian Li, Ph.D. is a Principal Investigator at the School of Medicine, The Chinese University of Hong Kong, Shenzhen. His research sits at the intersection of computational biology, genomics, and machine learning, with the goal of understanding how genome regulation shapes cellular identity, development, and disease.
Before joining CUHK-Shenzhen, he trained in computational genomics and developed methods for single-cell multi-omics, regulatory element discovery, and statistical inference from sparse, high-dimensional measurements. The lab continues this work by combining principled probabilistic models with modern deep learning, and collaborates closely with wet-lab partners on hypothesis-driven validation.
Research highlights
Three threads, one question: how is biological information encoded, read, and rewritten?
Genome regulation
Modeling cis-regulatory grammar from sequence and chromatin data to predict how non-coding variation reshapes gene expression and disease risk.
Single-cell & spatial
Probabilistic and deep-learning methods for single-cell multi-omics and spatial transcriptomics — recovering cell states, lineages, and tissue architecture from sparse measurements.
AI for human disease
Translating regulatory and cellular models into mechanism-aware predictors for cancer, immune, and metabolic phenotypes — built to be interpretable and clinically actionable.
Selected publications
Representative papers — full list on the publications page.
We are looking for curious people.
Postdocs, PhD students, research assistants and visiting scholars with backgrounds in computational biology, statistics, machine learning, or molecular biology — we'd love to hear from you.