报告人：王景魁 高级生物信息学研究员

报告题目：Statistical and machine learning methods in single-cell data science

报告摘要：Single-cell sequencing technology enables genome-wide measurement at single-cell resolution in tens of thousands of cells simultaneously and it has revolutionized the life science and biomedical research over the last decade. In this talk, we will first introduce briefly the recent single-cell technologies and the relevant biological questions. Next, we will highlight the statistical challenges of analyzing such massive, noisy, sparse and high-dimensional data. Notably, those challenges include data imputation and integration, dimension reduction and modeling biological continuous processing (i.e. trajectory analysis). We will review and discuss the state-of-art solutions that employed a variety of statistical, machine learning and mathematical methods, from classical matrix factorization and optimal transport, an old question in mathematics, to autoencoder as one of the modern deep learning techniques.

报告时间：2022.12.08下午16:00-18:00

报告形式：腾讯会议；  会议号：862-113-432

获取会议密码请发邮件至：zhaoxiaoxue@hit.edu.cn

报告人简介：Jingkui Wang, senior bioinformatic researcher at Research Institute of Molecular Pathology (IMP) in Vienna (Austria). With a bachelor in physics from Harbin Institute of Technology (HIT), he completed his master and PhD in biophysics from University of Lille 1 (France) in 2012. He next worked at École polytechnique fédérale de Lausanne (EPFL, Switzerland) as a postdoctoral researcher in 2012-2016. Currently, his interest mainly focus on quantitative biology and computational genomics, specifically, applying mathematical modeling, statistical and machine learning methods to answer questions in regeneration biology and self-organization in organoid. He has published many papers in Cell, Cell metabolism, Nature methods, PNAS and other high-impact journals.