About
I am a researcher working at the intersection of machine learning and biology. My work centers on generative modeling of biological data, with applications spanning biosequences (DNA and protein), high-throughput sequencing, ecology, and the science of science. I am particularly interested in building language models for genomes and plasmids.
I am affiliated with the School of Interdisciplinary Science at BIT and also hold a part-time position at Zhongguancun Academy. I collaborate with researchers from Tsinghua University, Zhejiang University, John Hopkins University, Harvard University, and the Chinese Academy of Sciences. If you are interested in working with me, please send your CV to shaobinlx@gmail.com. I currently have open positions for PhD students (Fall 2027) and postdocs.
Research Themes
Generative Genomics
Long-context language models that learn the grammar of DNA — from plasmids to bacteriophage genomes — to generate novel functional sequences.
Sequence-to-Function
Deep learning methods that map DNA and protein sequences to their biological functions, illuminating the sequence-function landscape.
Quantitative Biology
Single-cell measurements, mathematical modeling, and nonlinear dynamics to dissect the principles of gene expression.