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Dr. Zexiao Liang | Machine Learning | Best Researcher Award

Doctorate at School of Integrated Circuits, Guangdong University of Technology, China

Summary:

Dr. Zexiao Liang is a postdoctoral assistant researcher at the School of Integrated Circuits, Guangdong University of Technology. He completed his Bachelor’s, Master’s, and Ph.D. degrees in Automation from Guangdong University of Technology between 2012 and 2022. Dr. Liang has extensive experience in machine learning, with a focus on multi-information fusion algorithms and domain-specific applications. He has published multiple SCI papers as the lead author and holds several invention patents. Dr. Liang is also skilled in guiding students, contributing to the publication of additional SCI papers and conference papers.

Profile:

Education:

Dr. Zexiao Liang completed his Bachelor’s degree in Automation from Guangdong University of Technology in 2016. He continued his studies at the same institution, earning a Master’s degree in Automation in 2019. Dr. Liang then pursued a Ph.D. in Automation at Guangdong University of Technology, which he completed in 2022. Throughout his academic journey, Dr. Liang consistently demonstrated exceptional academic performance, securing top ranks in his major and achieving multiple academic milestones, including the publication of several papers and the acquisition of invention patents.

Professional Experience:

Dr. Zexiao Liang has accumulated valuable professional experience as a postdoctoral assistant researcher at the School of Integrated Circuits, Guangdong University of Technology since July 2022. His work primarily focuses on the research and design of machine learning algorithms, particularly in the integration of multi-information fusion and clustering analysis. Dr. Liang has developed algorithms to address specific domain challenges, such as predicting the effects of multi-drug interactions and conducting reliability analyses for low-quality chip images. His role also involves guiding students, which has led to the publication of numerous scientific papers and contributions to various research projects.

Research Interests:

Dr. Zexiao Liang’s research interests lie in the field of multi-information fusion machine learning algorithms, including the integration of multiple transformation domain information and the development of multi-modal, multi-view learning techniques. He is particularly interested in clustering analysis and the design and application of machine learning algorithms to address domain-specific challenges. His work encompasses algorithms for predicting the effects of multi-drug interactions and reliability analysis for low-quality chip images. Dr. Liang is dedicated to advancing machine learning methodologies and their practical applications in solving complex problems in various domains.

Skills:

Dr. Zexiao Liang specializes in advanced machine learning and data analysis techniques, focusing on the integration of multiple transformation domain information, multi-modal and multi-view learning, and clustering analysis. His expertise also includes designing algorithms to address domain-specific challenges, such as predicting multi-drug interactions and analyzing low-quality chip images for reliability. He is proficient in feature fusion strategies and parameter optimization, as well as dimensionality reduction techniques for manifold-based semi-supervised classification.

 

Publications:

 

Spectral clustering based on high‐frequency texture components for face datasets

  • Authors: Z Liang, S Guo, D Liu, J Li
  • Journal: IET Image Processing
  • Volume: 15
  • Issue: 10
  • Pages: 2240-2246
  • Year: 2021
  • Citations: 3

Optimal Mean Linear Classifier via Weighted Nuclear Norm and L2,1 Norm

  • Authors: D Zeng, Z Liang, Z Wu
  • Journal: 电子与信息学报
  • Volume: 44
  • Issue: 5
  • Pages: 1602-1609
  • Year: 2022
  • Citations: 2

An effective clustering algorithm for the low-quality image of integrated circuits via high-frequency texture components extraction

  • Authors: Z Liang, G Tan, C Sun, J Li, L Zhang, X Xiong, Y Liu
  • Journal: Electronics
  • Volume: 11
  • Issue: 4
  • Article Number: 572
  • Year: 2022
  • Citations: 2

HDGN: Heat diffusion graph network for few-shot learning

  • Authors: Q Tan, Z Wu, J Lai, Z Liang, Z Ren
  • Journal: Pattern Recognition Letters
  • Volume: 171
  • Pages: 61-68
  • Year: 2023
  • Citations: 1

2D DOA Estimation Through a Spiral Array Without the Source Number

  • Authors: J Li, J Dai, Z Liang, D Liu, S Guo, Y Liu
  • Journal: Circuits, Systems, and Signal Processing
  • Volume: 41
  • Issue: 5
  • Pages: 3011-3022
  • Year: 2022
  • Citations: 1

A fusion representation for face learning by low-rank constrain and high-frequency texture components

  • Authors: Z Liang, D Zeng, S Guo, J Li, Z Wu
  • Journal: Pattern Recognition Letters
  • Volume: 155
  • Pages: 48-53
  • Year: 2022
  • Citations: 1

Face recognition via optimal mean robust linear discriminant analysis

  • Authors: D Zeng, Z Wu, Z Ren, Z Liang, S Xie
  • Conference: 2018 Chinese Automation Congress (CAC)
  • Pages: 1504-1509
  • Year: 2018
  • Citations: 1
Zexiao Liang | Machine Learning | Best Researcher Award

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