教育经历：

2004.9－2008.7，黄淮学院，本科；

2008.9－2011.7，betway官方app,硕士；

2011.9－2014.7，华南理工大学,博士；

工作经历：

2014.7－2019.4，betway官方app ，讲师;

2019.4－至今，betway官方app ，副教授;

研究领域：
研究方向：

主讲本科生课程：《 高等数学》、《微积分 》、《 概率论 》、《概率论与数理统计》
主讲研究生课程：《矩阵分析与应用 》

（1）betway官方app2015-2016年度“三育人”先进个人

（2）betway官方app2016-2018年度优秀教师

（1）国家自然科学基金天元基金，“压缩感知中正交匹配追踪算法的理论研究”，主持。

（2）河南省高等学校重点科研项目计划,“基于框架的压缩感知重建算法理论及其应用研究”，主持。

（3） 河南省重点研发与推广（科技攻关），“基于框架稀疏的非负信号重建理论及应用研究”，主持。

（4） 国家自然科学基金青年基金，“基于A*OMP算法的稀疏信号重构理论及应用研究”，主持。

(22)Haifeng Li, Guoqi Liu, and Jian Zou, Sufficient condition for exact support recovery of sparse signals through greedy block coordinate descent,IET Signal Processing, 2019, 13(2), 176-182. (SCI)

(21)Haifeng Li, Jinming Wen, Generalize d covariance-assisted matching pursuit,Signal Processing, 2019, 163, 231-237. (SCI)

(20)Haifeng Li, Jinming Wen, A new analysis for support recovery with block orthogonal matching pursuit,IEEE Signal Processing Letters, 2019, 26(2), 247-251. (SCI)

(19)Haifeng Li, Jian Wangand Xin Yuan, On the Fundamental Limit of MultipathMatching Pursuit[J],IEEE Journal of Selected Topics in Signal Processing, 2018，12(5)，916-917. (SCI)

(18)Haifeng Liand Guoqi Liu, An Improved Analysis for Support Recovery With Orthogonal Matching Pursuit Under General Perturbations,IEEE Access, 2018, 18856-18867. (SCI)

(17)Haifeng Li,Yingbin Ma and Yuli Fu,An improved RIP-based performance guarantee for sparse signalrecovery via simultaneous orthogonal matching pursuit.Signal Processing, 2018, 144: 29-35.(SCI)

(16)Haifeng Li,Jing Zhang and Jian Zou,Improving the bound on the RIP constant in

multiple orthogonal least squares.IET Signal Processing, 2018, 666-671.(SCI)

(15)Haifeng Li.Comment on “Strong recovery conditions for least support orthogonal matching pursuit in noisy case”.Electronics Letters.2017, 53(10): 659-660.(SCI)

(14)Haifeng Liand Guoqi Liu. Perturbation analysis of signal space fast iterative hard thresholding with redundant dictionaries.IET Signal Processing, 2017, 11(4): 462-468.(SCI)

(13)Haifeng Li.The weighted minimization for partially known support.Wireless Personal Communications, 2016, 91(1): 255-265.(SCI).

(12)Haifeng Li.Improved analysis of SP and CoSaMP under total perturbations.Eurasip Journal on Advances in Signal Processing, 2016, 112: 1-6.(SCI).

(11)Haifeng Li, Yingbin Ma, WenAn Liu and Yuli Fu.Improved analysis of greedy block coordinate descent under RIP.Electronics Letters, 2015, 51(6): 488-490.(SCI).

(10)Haifeng Li, Yuli Fu, Rui Hu and Rong Rong. Perturbation analysis of greedy block coordinate descent under RIP.IEEE Signal Processing Letters, 2014, 21(5): 518-522. (SCI).

(9)Yuli Fu,Haifeng Li, Qiheng Zhang and Jian Zou. Block-sparse recovery via redundant block OMP,Signal Processing. 2014 , 97(4) : 162-171. (SCI).

(8)Haifeng Li, Yuli Fu, Qiheng Zhang and Rong Rong. A generalized hard thresholding pursuit algorithm.Circuits, Systems, and Signal Processing, 2014, 33(4): 1313-1323. (SCI).

(7)Haifeng Li, Rui Li and Bei Li. Block MMV for the reconstruction of multiband signals.Proceedings of the 34th Chinese Control Conference, 2015: 4523-4528. (EI).

(6)Guoqi Liu,Haifeng Li, Liu Yang, A topology preserving method of evolving contours based on sparsity constraint for object segmentation.IEEE Access, 2017, 5: 19971-19982.(SCI)

(5)Jian Zou, Yuli Fu, Qiheng Zhang andHaifeng Li. Split Bregman algorithms for multiple measurement vector problem.Multidimensional System Signal Processing,2015, 26(1)：207-224. (SCI).

(4)Qiheng Zhang, Yuli Fu,Haifeng Liand Rong Rong. Optimised projections for generalised distributed compressed sensing.Electronics Letters, 2014, 50(7): 520-521. (SCI).

(3)Qiheng Zhang, Yuli Fu,Haifeng Liand Rong Rong. Optimized projection matrix for compressed sensing.Circuits System and Signal Processing, 2014, 33(5): 1627-1636. (SCI).

(2)Yuli Fu, Jian Zou, Qiheng Zhang andHaifeng Li. Recoverability analysis of block-sparse representation.Journal of Systems Engineering and Electronics, 2014, 25(3): 373-379. (SCI).

(1)Qiheng Zhang, Yuli Fu,Haifeng LiandJianZou. Dictionary learning method for joint sparse representation-based image fusion.Optical Engineering, 2013, 52(5): 1-11. (SCI).