时间: 2026年3月4日，星期三, 10:00 -11:00

地点: 数学高等研究院报告厅

报告人: 杨灿 Can Yang (香港科技大学)

摘要: Deciphering cell niches in complex tissues is essential for understanding tissue structure and disease. Recent advances in spatial omics have enabled subcellular resolution and accurate cell identity mapping. However, robust delineation of cell niches and disease-associated spatial patterns remains difficult. We introduce Harmonics, a novel computational framework that systematically identifies both common and condition-specific cell niches from spatial omics data through hierarchical distribution matching. Harmonics also includes a suite of downstream modules that facilitate comprehensive niche characterization. We demonstrate its scalability, accuracy and generalizability across datasets spanning diverse species, tissues, diseases, spatial modalities, and technological platforms. For condition-agnostic datasets, Harmonics outperforms baseline methods in both accuracy and robustness, and further demonstrates the capability to resolve niche structures at finer granularity. Across diverse diseases including pulmonary fibrosis, triple-negative breast cancer, and colorectal cancer, Harmonics enables precise identification of condition-specific niches and reveals disease-associated dynamics, subtype-specific spatial patterns, and structured immune architectures. We envision Harmonics as a practical and versatile tool for spatial niche analysis that can be applied across a wide range of biological contexts and seamlessly integrated with existing spatial omics workflows. This is a joint work with our lab members, Yuyao Liu, Jiashun Xiao, Xiaoheng Ma, Xiaomeng Wan, Peiqi Jiang, Zhiwei Wang, and Yuheng Chen.

报告人简介：Prof. Yang Can is currently a Professor in the Department of Mathematics at The Hong Kong University of Science and Technology (HKUST), where he also serves as the Director of the Big Data Bio-Intelligence Lab (BDBI). He holds several editorial positions, including Associate Editor for the Annals of Applied Statistics, Section Editor for PLOS Computational Biology, and Associate Editor for Genetics and Human Genetics and Genomics Advances. His research specializes in data science, focusing on the development of novel statistical and computational methods for large-scale data analysis, including deep generative models and scalable AI algorithms. Prof. Yang's work has been published in high-impact journals and at prestigious machine learning conferences, including Nature, Nature Machine Intelligence, Nature Computational Science, Nature Communications, Proceedings of the National Academy of Sciences (PNAS), Annals of Statistics, IEEE Transactions on Pattern Analysis and Machine Intelligence, The American Journal of Human Genetics, and the International Conference on Machine Learning. Prof. Yang has also fostered industrial collaborations supported by the Innovation and Technology Fund of the Hong Kong Government.