報告題目：稀疏多模態數據融合優化
時間：2024年10月19日上午10：30-12：00
地點：主樓317
報告人：姜昊
報告人簡介：
姜昊，中國人民大學數學學院教授、 博士生導師，擔任中國運籌學會女性工作委員會副秘書長、中國生物信息學（籌）生物信息學算法研究專業委員會秘書長、中國工業與應用數學會數學與生命科學專業委員會委員，主要從事機器學習、 數據挖掘、計算生物信息學、基于學習的建模、優化和控制等方面的研究工作，主持、完成國家自然基金項目 3項，并以核心成員身份參與國家自然科學基金重大研究計劃集成項目。在 Pattern Recognition， IEEE Transactions on Neural Networks and learning Systems，Bioinformatics, Briefings in Bioinformatics, Information Sciences， Applied Mathematical Modeling, Applied Soft Computing 等國際權威期刊和會議發表論文 50 余篇。
報告內容簡介：
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.
