英文网页链接：https://www.math.fsu.edu/~whuang2

教育经历：

2014，美国佛罗里达州立大学，应用与计算数学，博士

2007，中国科学技术大学，信息与计算科学，学士

2020.11-至今 加拿大28预测在线预测可刮奖，数学科学学院，教授

2018.09-2020.11 加拿大28预测在线预测可刮奖，数学科学学院，副教授

2016-2018，美国莱斯大学，计算与应用数学，法伊佛讲师博士后

2014-2016，比利时新鲁汶大学，ICTEAM，博士后研究员

2014，美国佛罗里达州立大学，科学计算，博士后研究员

2007-2008，厦门吉比特网络技术有限公司，数值策划

数值优化，主要包括流形上的优化算法设计分析、相关软件设计开发及其应用。应用包括图像处理，信号复原，机器学习，网络计算等。

社会兼职：

中国运筹学会理事

授课情况：

• 2022，9-11月，四川大学（国家天元数学西南中心），流形上的优化第二、三期，流形与嵌入子流形与一阶二阶优化算法，线上

• 2021，11月，四川大学（国家天元数学西南中心），流形上的优化第一期，数值优化基础，线上

• 2021，1月，南京师范大学，数学科学学院，流形上的优化

• 2020，12月，广西大学，数学与信息科学学院，流形上的优化

• 2019，12月，武汉大学（国家天元数学中部中心），流形上的优化

• 2019，11月，复旦大学，大数据学院，流形上的优化

2021年国家级高层次人才青年项目入选者

科研成果：

· TreeScaper: 由C++编写、使用Qt、 CLapack和VTK库的系统发生树(Phylogenetic tree)可视化与诊断工具。下载链接见：https://github.com/whuang08/TreeScaper/releases。

· ROPTLIB：一个由C++编写的流形优化软件工具包。使用标准线性代数库Blas与Lapack，实现多个流形上优化算法与常见流形，提供Matlab与Julia接口。下载链接见：https://www.math.fsu.edu/~whuang2/Indices/index_ROPTLIB.html。

国家自然科学青年基金 12001455, 2021.01-2023.12

· Yuetian Luo, Wen Huang, Xudong Li, Anru R. Zhang*, "Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence", accepted in Operations Research.

· Wen Huang*, Ke Wei*, "An Inexact Riemannian Proximal Gradient Method", doi: 10.1007/s10589-023-00451-w, 2023

· Wen Huang*, Kyle A. Gallivan, “A limited-memory Riemannian symmetric rank-one trust-region method with a Restart Strategy”, 93:1, 2022

· Jiali Wang, Wen Huang, Rujun Jiang*, Xudong Li, Alex L. Wang, "Solving Stackelberg Prediction Game with Least Squares Loss Via Spherically Constrained Least Squares Reformulation", In Proceeding of International Conference on Machine Learning 2022 (ICML), Outstanding paper award, 2022.

· Wen Huang*, Ke Wei*, "An Extension of Fast Iterative Shrinkage-thresholding to Riemannian Optimization for Sparse Principal Component Analysis", Numerical Linear Algebra with Applications, 29(1), e2409, 2022.

· Melissa Marchand, Kyle Gallivan, Wen Huang, Paul Van Dooren*, "Analysis of the Neighborhood Pattern Similarity Measure for the Role Extraction Problem", SIAM Journal on Mathematics of Data Science, 3:2, pp. 736-757, 2021.

· Wen Huang*, Ke Wei*. "Riemannian Proximal Gradient Methods", Mathematical Programming, doi:10.1007/s10107-021-01632-3, 2021.

· Wen Huang*, Paul Hand, Reinhard Heckel, Vladislav Voroninski. "A Provably Convergent Scheme for Compressive Sensing under Random Generative Priors", Journal of Fourier Analysis and Applications, 27, doi:10.1007/s00041-021-09830-5, 2021.

· Chafik Samir*, Wen Huang*. "Coordinate Descent Optimization for One-to-One Correspondence with Applications to Supervised Classification of 3D Shapes", Applied Mathematics and Computation, Applied Mathematics and Computation, 388, 125539, 2021.

· Xinru Yuan, Wen Huang*, P.-A. Absil, K. A. Gallivan. "Computing the matrix geometric mean: Riemannian vs Euclidean conditioning, implementation techniques, and a Riemannian BFGS method", Numerical Linear Algebra with Applications, 27:5, 1-23, 2020.

· Sean Martin, Andrew M. Raim, Wen Huang, Kofi P. Adragni*. "ManifoldOptim: An R Interface to the ROPTLIB Library for Riemannian Manifold Optimization", Journal of Statistical Software, 93:1, pp. 1-32, 2020.

· Reinhard Heckel*, Wen Huang, Paul Hand, Vladislav Voroninski. "Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior", Information and Inference: A Journal of the IMA, 2020.

· Wen Huang*, Paul Hand. "Blind Deconvolution by a Steepest Descent Algorithm on a Quotient Manifold", SIAM Journal on Imaging Sciences, 11:4, pp. 2757-2785, 2018.

· Wen Huang*, P.-A. Absil, Kyle Gallivan, Paul Hand. "ROPTLIB: an object-oriented C++ library for optimization on Riemannian manifolds", ACM Transactions on Mathematical Software, 44:4, pp. 43:1-43:21, 2018.

· Somayeh Hosseini, Wen Huang*, Roholla Yousefpour. "Line Search Algorithms for Locally Lipschitz Functions on Riemannian Manifolds", SIAM Journal on Optimization, 28(1), pp. 596-619, 2018.

· Wen Huang*, P.-A. Absil, Kyle Gallivan. "A Riemannian BFGS Method without Differentiated Retraction for Nonconvex Optimization Problems", SIAM Journal on Optimization, 28:1, pp. 470-495, 2018.

· Wen Huang*, Kyle A. Gallivan, Xiangxiong Zhang. "Solving PhaseLift by low-rank Riemannian optimization methods for complex semidefinite constraints", SIAM Journal on Scientific Computing, 39:5, pp. B840-B859, 2017.

· Jim Wilgenbusch*, Wen Huang, Kyle A. Gallivan. "Visualizing Phylogenetic Tree Landscapes", BMC Bioinformatics, 18:85, DOI:10.1186/s12859-017-1479-1, 2017.

· Wen Huang*, P.-A. Absil, Kyle Gallivan. "Intrinsic Representation of Tangent Vectors and Vector Transport on Matrix Manifolds", Numerische Mathematik, 136:2, p.523-543, DOI:10.1007/s00211-016-0848-4, October, 2017.

· Wen Huang*, Guifang Zhou, Melissa Merchand, Jeremy Ash, Paul Van Dooren, Jeremy M. Brown, Kyle A. Gallivan, Jim Wilgenbush. "TreeScaper: visualizing and extracting phylogenetic signal from sets of trees", Molecular Biology and Evolution, 33(12):3314-3316 DOI:10.1093/molbev/msw196, 2016.

· Guifang Zhou, Wen Huang, Kyle Gallivan, Paul Van Dooren, P.-A. Absil*. "A Riemannian rank-adaptive method for low-rank optimization", Neurocomputing, 192, 72-80, June 2016.

· Wen Huang*, Kyle A. Gallivan, Anuj Srivastava, Pierre-Antoine Absil. "Riemannian Optimization for Registration of Curves in Elastic Shape Analysis", Journal of Mathematical Imaging and Vision, 54(3), 320-343, 2016.

· Wen Huang*, Kyle A. Gallivan, Pierre-Antoine Absil. "A Broyden Class of Quasi-Newton Methods for Riemannian Optimization", SIAM Journal on Optimization, 25:3, pp. 1660-1685, 2015.

· Wen Huang, Pierre-Antoine Absil*, Kyle A. Gallivan. "A Riemannian symmetric rank-one trust-region method", Mathematical Programming Series A, 150:2, pp. 179-216, 2015.

在读博士：陈建恒、郭媛媛、司武涛、魏萌（美国佛罗里达州立大学）、张束光（美国佛罗里达州立大学）

在读硕士：黄亦徽、黄振威、刘方玉、秦婉璐、苏雨秋，杨子林、Florentin Goyens（比利时新鲁汶大学，已毕业）