导师姓名：喻高航教授
所属学院：理学院
导师类别：博士生导师、硕士生导师
研究方向：机器学习中的自适应控制与优化/张量分析与计算
博士招生：自动化学院（人工智能学院）
硕士招生：理学院
联系方式：
个人简介：
喻高航，男，教授，博士生导师，教育部新世纪优秀人才，主要从事张量数据分析、大规模优化计算及其在机器学习、视觉计算、医学影像中的应用研究。入选ESI高被引榜，先后在SIAM Journal on Imaging Sciences, International Journal of Robust and Nonlinear Control，IEEE Signal Processing Letters，Neurocomputing, Signal Processing, Computers in Biology and Medicine, Journal of X-Ray Science and Technology, Journal of Mathematical Imaging and Vision，Inverse Problems, Computational Optimization and Applications, Journal of Optimization Theory and Applications,Optimization Methods and Software等国际期刊上发表40余篇SCI论文,目前主持在研1项国家自然科学基金和1项浙江省自然科学基金重大项目，主持完成3项国家自科基金、1项教育部博士点基金和1项教育部新世纪优秀人才支持计划项目等。多次访问香港理工大学，2015-2016公派英国剑桥大学访问学习，2013年起任国际学术期刊Statistics, Optimization and Information Computing执行主编（Coordinating Editor）。任国家重点研发计划项目和国家自然科学基金项目通讯评审专家及多个国际SCI学术刊物的审稿人。到目前为止，所指导的硕士研究生分别有2人获得国家优秀硕士研究生奖学金，有3人获得省优秀硕士毕业论文，有6人应届考取博士。主要科研项目：u 张量数据深度学习/统计学习的优化理论方法及其应用（LD19A010002）,浙江省自然科学基金重大项目，2019.1-2022.12，45万，主持
u 结构张量特征计算及其在张量数据分析中的应用研究（**），国家自然科学基金项目，2017.1-2020.12，40万，主持u 高阶张量谱理论：算法、分析及其在影像科学中的应用(NCET-13-0738),教育部新世纪优秀人才支持计划项目, 2014.1-2016.12, 50万，主持u 高阶张量谱分析与张量场特征可视化及其在MRI医学影像中的应用(**)，国家自然科学基金项目，2013.1-2016.12，47万，主持u 图像处理与高阶扩散张量医疗成像中的快速优化算法研究(**),国家自然科学基金项目，2011.1-2013.12，18万，主持u 大规模非线性方程组与图像恢复问题的自适应梯度型算法(**),国家自然科学基金项目，2010.1-2010.12，3万，主持主要学术成果：u W. Hu, S. Li, W. Zheng, Y. Lu, G. Yu*,Robust sequential subspace clustering via ?1-norm temporal graph, Neurocomputing383(2020), 380-395.
u W. Hu, S. Li, J. Huang, T. Wang, G. Yu*,Computing the nearest polynomial to multiple given polynomials with a given zero via l2, q-norm minimization, Theor. Comput. Sci. 809(2020), 394-406.
u G. Yu, Y.Song,Y.Xu,Z.Yu,Spectral projected gradient methods for generalized tensor eigenvalue complementarity problems,Numerical Algorithms, 80(2019), 1181–1201.
u J.Huang, G. Zhou, G. Yu，Orthogonal tensor dictionary learning for accelerated dynamic MRI, Med. Biol. Engineering and Computing57(9) (2019)1933-1946.
u W. Hu, Z. Wang, S. Liu, X. Yang, G. Yu, J. Zhang, Motion Capture Data Completion via Truncated Nuclear Norm Regularization. IEEE Signal Process. Lett. 25(2018)258-262.
u Y. Sun, G. Yu, On Strong Controllability for Planar A?ne Nonlinear Systems, International Journal of Robust and Nonlinear Control,28(2018) 2668-2677.
u W.Xue, W. Zhang and G. Yu*, Least Absolute Deviations Learning of Multiple Tasks, Journal of Industrial and Management Optimization, 14(2018)719-729.
u S. Niu,J.Huang,Z.Bian, D.Zeng,G.Yu*, Z.Liang,J.Ma,Iterative reconstruction for sparse-view X-ray CT using alpha-divergence constrained total generalized variation minimization, Journal of X-Ray Science and Technology, 25(2017) 673-688.
u G.Yu, Z.Yu, Y.Xu, Y.Song, An adaptive gradient method for computing generalized tensor eigenpairs,Computational Optimization and Applications, 65(2016)781-797.
u Y.Song andG.Yu, Properties of Solution Set of Tensor Complementarity Problem, Journal of Optimization Theory and Applications, 170 (2016) 85-96.
u S.Niu, S. Zhang, J. Huang, Z.Bian, W. Chen, G. Yu*, Z. Liang, J. Ma, Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations, Neurocomputing197(2016) 143-160.
u G. Yu, W. Xue, Y. Zhou, A nonmonotone adaptive projected gradient method for primal-dual total variation image restoration. Signal Processing103 (2014) 242-249.
u L. Qi, G. Yu*, Y. Xu, Nonnegative diffusion orientation distribution function, Journal of Mathematical Imaging and Vision, 45 ( 2013) 103-113.
u G. Yu, S.Niu, J. Ma, Multivariate spectral gradient projection method for nonlinear monotone equations with convex constraints, Journal of Industrial & Management Optimization, 9(2013) 117-129.
u G.Yu,Nonmonotone spectral gradient-type methods for large-scale unconstrained optimization and nonlinear systems of equations, Pacific Journal of Optimization, 7 (2011), 387-404.
u L. Qi, G. Yuand X. Wu, Higher order positive semi-de?nite di?usion tensor imaging, SIAM Journal on Imaging Sciences, 3 (2010) 416-433.
u G. Yu, A derivative-free method for solving nonlinear equations,Journal of Industrial and Management Optimization, 6 (2010) 149-160.
u G. Yu, L. Qi, Y. Sun, Y. Zhou, Impulse noise removal by a nonmonotone adaptive gradient method,Signal Processing,90(2010) 2891-2897.
u G.Yu, J. Huang, Y. Zhou, A descent spectral conjugate gradient method for impulse noise removal, Applied Mathematics Letters, 23(2010) 555-560.
u G. Yu, L. Qi and Y. Dai, On nonmonotone Chambolle gradient projection algorithms for total variation image restoration, Journal of Mathematical Imaging and Vision, 35 (2009) 143-154.
u G. Yu, L. Guan, W. Chen, Spectral conjugate gradient methods with su?cient descent property for large-scale unconstrained optimization, Optimization Methods and Software, 23 (2008) 275-293.
u G. Yu, L. Guan, G. Li, Global convergence of modified Polak-Ribière-Polyak conjugate gradient methods with sufficient descent property, Journal of Industrial & Management Optimization, 4(2008)565-579.
u S. Niu, Z. Bian, D. Zeng,G. Yu, et al., Total image constrained diffusion tensor for spectral computed tomography reconstruction, Applied Mathematical Modelling,Volume 68, (2019) 487-508.
u S. Niu, G. Yu, et al., Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction，Inverse Problems，vol. 34, pp. 024003, 2018
u W. Hu, L.Lu, C. Yin and G. Yu*, A smoothing Newton method for tensor eigenvalue complementarity problems, Pacific Journal of Optimization, 13 (2017) 243-253.