个人简历

教育背景：
2005.09-2009.07，山西大同大学，物理学专业，学士
2009.09-2012.06，大连理工大学，理论物理专业，硕士
2012.10-2015.09，日本会津大学，计算机与信息系统学专业，博士
工作经历（工作经历，按时间顺序排序）
2015.10-2017.03，日本会津大学，客座研究员
2017.03-至今，广东工业大学自动化学院，青年百人特聘副教授
承担教学工作
2017-2018学年，电磁学与微波技术，32学时
2018-2019年，信号与系统，32学时
承担（含主持和参与）的科研项目（包括纵向、横向）
1. 结构化解析字典学习及其在压缩感知图像重构的应用研究，国家自然科学基金青年基金项目，主持，2019.1-2022.12。
2. 基于稀疏表示与字典学习的图像重构算法研究，日本文部省独立行政法人日本学术振兴会(JSPS) 研究项目， 主持，2014，4-2015.9，已结题。

研究领域

"智能信息处理，机器学习"

近期论文

[1] Zhenni Li, Zuyuan Yang and Shengli Xie, Computing Resource Trading for Edge-Cloud-assisted Internet of Things，IEEE Transactions on Industrial Informatics，2019. (SCI，中科院一区)
[2] Zhenni Li, Shuxue Ding, Wuhui Chen, Zuyuan Yang and Shengli Xie, Proximal Alternating Minimization for Analysis Dictionary Learning and Convergence Analysis, IEEE Transactions on Emerging Topics in Computational Intelligence, 2(3), pp.1-11, March, 2018.
[3] Zhenni Li, Shengli Xie, Wuhui Chen, Zuyuan Yang, Overcomplete Transform Learning With the log Regularizer, IEEE Access, 6, pp. 65239 - 65249, Oct., 2018. (SCI，中科院二区)
[4] Zhenni Li, Shuxue Ding, Yujie Li, Zuyuan Yang, Shengli Xie and Wuhui Chen, Manifold-optimization based Analysis Dictionary Learning with a ℓ1/2-Norm Regularizer, Neural Networks, 98, pp. 212-222, 2018. (SCI，中科院一区)
[5] Zhenni Li, Shuxue Ding, Takafumi Hayashi and Yujie Li, Analysis Dictionary Learning Using Block Coordinate Descent Framework with Proximal Operators, Neurocomputing (Elsevier), Vol. 239, pp.165-180, May, 2017. (SCI，中科院二区)
[6] Zhenni Li, Takafumi Hayashi, Shuxue Ding and Yujie Li, Dictionary Learning with ℓ1/2-regularizer and the Coherence Penalty and Its Convergence Analysis, International Journal of Machine Learning and Cybernetics, Vol. 9 (8), 1351-1364, 2018,8. (SCI)
[7] Zhenni Li, Shuxue Ding, Takafumi Hayashi and Yujie Li, Incoherent Dictionary Learning with Log-regularizer Based on Proximal Operators, Digital Signal Processing (Elsevier), Vol. 63, pp.86-99, Jan.,2017. (SCI)
[8] Zhenni Li, Shuxue Ding and Yujie Li, A Fast Algorithm for Learning Overcomplete Dictionary for Sparse Representation Based on Proximal Operators, Neural computation, Vol. 27, Issue 9, pp. 1951–1982, July, 2015. (SCI)