Education

Ph.D., Image and Signal Processing, INSA-Rennes, France, 2014.

M.E., Communication and Information and  System, Shandong University, 2010.

B.E., Electronic and Information Engineering, Shandong University, 2007.

Research Interests

Research interets include signal processing and machine learning.  Currently, we focus on the analysis, construction, and quantification of deep neural networks, particularly through the lens of compressive sensing, random projection, and statistical theories. Our objective is to design  models that are both theoretically interpretable and computationally efficient.

Prospective Students:  We are looking for students with interests in both foundational machine learning  research and its applications to scientific problems. Contact: wzlu@sdu.edu.cn

Recent Papers

• Binary and Ternary Quantization Can Enhance Feature Discrimination

  Weizhi Lu, Mingrui Chen, and Weiyu Li. 

  arXiv, 2025.

• The Sparse Matrix-Based Random Projection: A Study of Binary and Ternary Quantization

  Weizhi Lu, Zhongzheng Li and Mingrui Chen and Weiyu Li

  Transactions on Machine Learning Research (TMLR), 2025.

• The Sparse Matrix-Based Random Projection: An Analysis of Matrix Sparisty for Classification

  Weizhi Lu, Mingrui Chen, Kai Guo and Weiyu Li

  Preprint, 2025.

(1)LU Weizhi. Binary Matrices for Compressed Sensing .IEEE Transactions on Signal Processing .2017

(2)杨硕. Explicit-to-Implicit Robot Imitation Learning by Exploring Visual Content Change .《IEEE-ASME TRANSACTIONS ON MECHATRONICS》 .2022

(3)李蔚郁. Collaborative Dictionary Learning for Compressed Sensing .IEEE Transactions on Industrial Informatics .2024

(4)鲁威志. Cascaded Compressed Sensing Networks .IEEE Signal Processing Letters .2023 (30):364

(5)陈明锐. Deep learning to ternary hash codes by continuation .ELECTRONICS LETTERS .2021 ,57 (24):925

(6)饶振环. Visual Navigation With Multiple Goals Based on Deep Reinforcement Learning .IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS .2021 ,32 (12):5445

(7)张伟. Feature Aggregation With Reinforcement Learning for Video-Based Person Re-Identification .IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS .2019 ,30 (12):3847

(8)张伟. A Multi-Scale Spatial-Temporal Attention Model for Person Re-Identification in Videos .IEEE Transactions on Image Processing .2019 ,29 :3365

(9)张伟. Learning Intra-video Difference for Person Re-identification .IEEE Transactions on Circuits and Systems for Video Technology .2018

(10)LU Weizhi. Compressed sensing performance of random Bernoulli matrices with high compression ratio. .IEEE Signal Processing Letters .2015 ,22 :1074

(11)Wang, Tingwei. Action recognition using dynamic hierarchical trees .Visual Communication and Image Representation .2019 ,61 :315

(12)LU Weizhi. Expander Recovery Performance of Bipartite Graphs with Girth Greater than 4 . IEEE Transactions on Signal and Information Processing over Networks .2019

1. 一种基于压缩感知和生成对抗的鱼类图像生成方法

2. 基于多任务关联的风机叶片损伤识别方法与系统

1. （包干项目）气象遥感与智能感知, 2024/01/01-2026/12/31

2. 面向机器视觉的玻璃制品缺陷检测关键技术研究及示范应用, 2023/06/01-2026/06/30

3. 鱼群观测特征基线数据库构建与智能识别系统研发, 2022/12/01-2025/11/30

4. 基于大数据多维感知融合的变电设备安全, 2022/06/30-2024/12/31

5. 多像感器协同智能感知与决策关键技术及应用, 2022/01/01-2024/12/31

6. 基于边缘计算的输电线路本体缺陷在线监测技术研究及应用, 2021/06/24-2023/04/30

7. GFJG-KM20210007, 2020/08/06-2020/12/06

8. 基于视觉导航的变电站无人机巡检技术研究及应用, 2021/06/25-2023/12/31

9. 复杂海况单艇准确完备自主感知机理研究, 2020/01/01-2024/12/31

10. 基于多模环境交互的自主安保机器人关键技术研究, 2019/12/02-2023/12/31

11. 基于膨胀图的压缩感知, 2018/08/16-2021/12/31