个人信息
  • 姓名:李欣
  • 部门:计算数学系
  • 职称:讲师
  • 荣誉:硕士生导师,专业学位硕士生导师
  • 电子邮件:lixin@nwu.edu.cn
  • 研究方向:高维数据建模与统计计算,基于高维统计学习方法的EEG脑电信号分析,基于深度学习方法的脑机接口研究,高维统计学习方法在深度学习中的应用


个人简介


        李欣,陕西西安人。2014年本科毕业于浙江大学数学与应用数学专业,2019年博士毕业于浙江大学计算数学学院。2019年7月至今在西北大学数学学院从事教学和科研工作。

 

项目及论文成果


1.科研项目

    (1) 国家自然科学基金青年项目, 测量误差情形下高维矩阵回归模型的纠偏估计及应用 ( 12201496 ), 2023.01-2025.12 ,主持。

 

    (2) 陕西省自然科学基础研究计划青年项目,测量误差情形下高维多响应回归的纠偏研究及应用(2022JQ-045), 2022.01-2023.12,主持。


2.主要论文

(1) Li Xin; Wu Dongya; Cui Yue; Liu Bing; Henrik Walter; Gunter Schumann; Li Chong; Jiang Tianzi*; Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies, BMC Bioinformatics, 2019, 20(1)

(2) Li Xin; Wu Dongya; Li Chong*; Wang Jinhua; Yao Jen-Chih; Sparse recovery via nonconvex regularized M-estimators over lq-balls, Computational Statistics and Data Analysis, 2020, 152

(3) Li Xin; Wu Dongya*; Minimax rates of lp-losses for high-dimensional linear errors-in-variables models over lq-balls, Entropy, 2021, 23(6)

(4) Li Xin; Hu Yaohua*; Li Chong; Yang Xiaoqi; Jiang Tianzi; Sparse estimation via lower-order penalty optimization methods in high-dimensional linear regression, Journal of Global Optimization, 2022

(5) Wu Dongya; Li Xin; Jiang Tianzi*; Reconstruction of behavior-relevant individual brain activity: an individualized fMRI study, Science China Life Sciences, 2019

(6) Wu Dongya; Li Xin*; Feng Jun*; Multi-hops functional connectivity improves individual prediction of fusiform face activation via a graph neural network, Frontiers in Neuroscience, 2021

(7) Wu Dongya; Li Xin*; Feng Jun*; Connectome-based individual prediction of cognitive behaviors via graph propagation network reveals directed brain network topology, Journal of Neural Engineering, 2021

(8) Wu Dongya*; Li Xin*; Graph propagation network captures individual specificity of the relationship between functional and structural connectivity, Human Brain Mapping, 2023

(9) Li Xin*; Wu Dongya*; Low-rank matrix estimation via nonconvex optimization methods in multi-response errors-in-variables regression, Journal of Global Optimization, 2023