报告题目：稀疏多模态数据融合优化

时间：2024年10月19日上午10：30-12：00

地点：主楼317

报告人：姜昊

报告人简介：

姜昊，中国人民大学数学学院教授、 博士生导师，担任中国运筹学会女性工作委员会副秘书长、中国生物信息学（筹）生物信息学算法研究专业委员会秘书长、中国工业与应用数学会数学与生命科学专业委员会委员，主要从事机器学习、 数据挖掘、计算生物信息学、基于学习的建模、优化和控制等方面的研究工作，主持、完成国家自然基金项目 3项，并以核心成员身份参与国家自然科学基金重大研究计划集成项目。在 Pattern Recognition， IEEE Transactions on Neural Networks and learning Systems，Bioinformatics, Briefings in Bioinformatics, Information Sciences， Applied Mathematical Modeling, Applied Soft Computing 等国际权威期刊和会议发表论文 50 余篇。

报告内容简介：

Single-cell transcriptomics has transformed our ability to characterize cell states. New methods for simultaneous profiling of multi-omics single cell data enable a better understanding of the cellular states and functions. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), allowed for parallel quantification of cell-surface protein expression and transcriptome profiling in the same cells; Methylome and transcriptome sequencing from single-cells (scM&T-Seq) allows for analysis of transcriptomic and epigenomic profiling in the same individual cells. However, effective integration method for mining the heterogeneity of cells over the noisy, sparse and complex multi-modal data is in growing need. In this talk, we will address the problem of heterogeneity analysis and representation learning in single cell data, for analyzing the optimal embedding representation and identifying cell clusters in a robust manner.