Lecture time

Lecture location

Conference Room 302, Building 1, Jinfeng Laboratory

Chen Shengquan , professor and doctoral supervisor at the School of Mathematical Sciences of Nankai University, a national high-level young talent, and one of Nankai University’s “100 Young Academic Leaders”. Hosted the National Natural Science Foundation of China youth and general projects, and published more than 30 academic papers as the first or corresponding author (including co-authors). He serves as the secretary-general of the Intelligent Health and Bioinformatics Committee of the Chinese Society of Automation, a standing member of the Bioinformatics and Artificial Life Committee of the Chinese Society of Artificial Intelligence, a director of the Chinese Council of the International Society for Computational Biology, and a youth editorial board member of Science Bulletin, GPB, JGG and other journals. He has been selected as one of Tsinghua University's "Academic Rookies" in 2021, the China Association for Science and Technology's Young Talent Promotion Project in 2023, the National Major Talent Project's Young Scholars in 2024, and the Tianjin U40 Cultivation Project in 2025.

Single-cell sequencing technology can analyze the body's cell types and composition, but single-cell data have high dimensions, high noise, and sparse data, making it difficult for traditional algorithms to accurately identify cell types and analyze cell heterogeneity. This lecture will focus on the pain points of poor single-cell data quality, scarcity of effective information, and complex cell lineages. With data-driven, information fusion, and knowledge guidance as the core ideas, this lecture will explain the intelligent analysis method of cell heterogeneity in multi-omics and multi-research scenarios from three levels: unsupervised, weakly supervised, and supervised learning.