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。
研究与招生方向：
高维统计与金融数据建模；深度学习与金融量化分析；大模型与智能体的金融前沿应用；智能金融核心场景落地应用
课题组培养特色：
1. 文理交叉、优势互补：兼顾数学统计的严谨性与人工智能的前沿性、金融场景的实用性，既训练学生扎实的数理推导、模型证明、算法优化能力，也培养学生解决真实金融工程问题的落地能力，规避纯理论研究脱离应用、纯AI研究缺乏数理支撑的弊端。
2. 理论+实战双轨培养：科研层面聚焦统计学、人工智能、金融交叉领域的创新课题，支持高水平学术论文、专利、科研项目产出；实践层面对接量化投资、金融科技、风控算法等行业场景，指导学生完成模型落地、策略回测、系统优化等实战项目。
3. 适配多元发展路径：培养方案兼顾学术深造与行业就业，学生毕业后可深耕学术科研领域，也可适配金融科技、量化私募、券商研究所、银行风控、人工智能算法等高薪行业岗位。
4. 前沿课题持续迭代：紧跟大模型、智能体、智能金融的行业热点，研究课题新颖、创新性强，贴合当下数字金融、AI量化的发展趋势，科研成果转化率高
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。
(3) 西安市科学技术协会青年人才托举计划，可辨识深度神经网络的统计优化理论与应用(0959202613024)，2026.07-2028.06，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, 2023
(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, 2024
(10) Li Xin; Wu Dongya∗; Low-rank matrix estimation via nonconvex spectral regularized methods in errors-in-variables matrix regression, European Journal of Operational Research, 2025, 323(2): 626-641.
(11) Li Xin; Wu Dongya∗; Sparse estimation in high-dimensional linear errors-in-variables regression via a covariate relaxation method, Statistics and Computing, 2024, 34(1):
(12) Li Xin; Wu Dongya∗; Low-rank matrix recovery via nonconvex optimization methods with application to errors-in-variables matrix regression, Journal of Optimization Theory and Applications, 2025, 205: 42.
西北大学