Jing Li | Deep Learning for Cybersecurity | Best Paper Award

Dr. Jing Li | Deep Learning for Cybersecurity | Best Paper Award

 Researcher at University Technology Malaysia, China

Summary:

Dr. Jing Li is a dedicated computer scientist currently pursuing a PhD in Computer Science at University Technology Malaysia. His research interests encompass networking, Internet of Things, machine learning/deep learning, cybersecurity, and big data. He has achieved academic excellence, being awarded the International Doctoral Scholarship (IDF) for the periods 2022-2023 and 2023-2024. Dr. Li holds a Master’s degree in Information Management from Zhejiang University, where he was recognized with the First Prize for his entrance essay submission and conducted research on “Hangzhou Wireless City Construction Planning.” He completed his Bachelor’s degree in Computer Science and Technology at China Jiliang University, distinguished as a four-time scholarship recipient.

Profile:

Education:

Dr. Jing Li is currently pursuing a PhD in Computer Science at University Technology Malaysia, focusing on networking, Internet of Things, machine learning/deep learning, cybersecurity, and big data. He has been awarded the prestigious International Doctoral Scholarship (IDF) for the academic years 2022-2023 and 2023-2024. Prior to this, he earned a Master’s degree in Information Management from Zhejiang University, where he received the First Prize for his entrance essay submission and completed a thesis titled “Research on Hangzhou Wireless City Construction Planning.” Dr. Li also holds a Bachelor’s degree in Computer Science and Technology from China Jiliang University, where he was a four-time scholarship winner.

Professional Experience:

Dr. Jing Li brings a wealth of professional experience in the technology sector, having held significant roles across various organizations. He started his career as a Software Engineer at UTStarcom (China) Co., Ltd., where he gained foundational experience from June 2003 to June 2006. He then progressed to roles with increasing responsibility, serving as a Software Engineer/Product Architect at Aerohive Networks, Inc. from June 2006 to September 2014, focusing on networking solutions. Subsequently, Dr. Li joined ArcSoft (Hangzhou) Technology Co., Ltd. as a Product Architect from June 2014 to June 2018, where he contributed to product development and architecture. His entrepreneurial spirit led him to co-found Hangzhou Zijie Technology Co., Ltd., where he served as Technical Co-founder from June 2018 to 2021, involved in the strategic and technical leadership of the company. Throughout his career, Dr. Li has demonstrated expertise in Python, C++, and C, alongside a profound understanding of networking, Internet of Things, machine learning/deep learning, cybersecurity, and big data applications.

Research Interests:

Dr. Jing Li’s research interests are centered around several key areas in computer science and technology. His primary focus lies in networking, where he explores advancements in network protocols, architectures, and performance optimization. Dr. Li is also deeply engaged in the Internet of Things (IoT), investigating methods to enhance IoT device connectivity, security, and data management. His expertise extends to machine learning and deep learning applications, particularly in developing intelligent algorithms for data analysis and decision-making processes. Additionally, Dr. Li is passionate about cybersecurity, researching techniques to safeguard networks and IoT ecosystems from emerging threats. Finally, he explores big data analytics, aiming to develop scalable and efficient algorithms for processing and extracting valuable insights from large datasets.

Skills:

Dr. Jing Li is equipped with a comprehensive skill set that spans diverse areas within computer science and technology. His expertise includes advanced proficiency in networking, encompassing thorough knowledge of network protocols, architectures, and optimization strategies. Dr. Li demonstrates a deep understanding of Internet of Things (IoT), where he excels in enhancing device connectivity, ensuring robust security measures, and optimizing data management systems. His proficiency extends to machine learning and deep learning, where he develops and implements sophisticated algorithms for data analysis and decision-making tasks. Additionally, Dr. Li is adept in cybersecurity practices, employing effective techniques to safeguard networks and IoT environments from evolving threats. His skills in big data analytics enable him to design and implement scalable algorithms that efficiently process and derive valuable insights from large datasets. With fluency in programming languages such as Python, C++, and C, Dr. Li leverages his technical acumen to drive innovation and contribute significantly to research and development initiatives in his field.

Peer Reviewer in Journals:

Dr. Jing Li has served as a peer reviewer for prestigious journals in the fields of computer and electrical engineering. His expertise as a reviewer extends to journals such as Expert Systems With Applications, Knowledge-Based Systems, Journal of Ambient Intelligence and Humanized Computing, Journal on Internet of Things, International Journal of Electrical and Computer Engineering, International Journal on Data Science and Technology, and Journal of Applied Engineering and Technological Science. Through his role, Dr. Li contributes to maintaining the quality and integrity of research in these specialized domains, ensuring rigorous evaluation and feedback for scholarly publications.

Awards & Honors:

Dr. Jing Li has garnered significant recognition for his contributions in computer science and technology. He has served as a peer reviewer for esteemed journals including Expert Systems With Applications, Knowledge-Based Systems, and Journal of Ambient Intelligence and Humanized Computing, among others. In 2023, Dr. Li was appointed as a Professor Assistant for AI-SLR courses at UTM, showcasing his expertise and leadership in the field. His commitment to academic excellence is further underscored by his involvement in committees such as the EGE Committee Technical Team and PARS2023/2024 Committee Publication and Technical Team. In 2024, Dr. Li was honored with the Asian Youth Leaders Scholarship Award, reflecting his outstanding achievements and dedication to advancing research and education in computer and electrical engineering.

Publications:

Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning

  • Authors: J. Li, M.S. Othman, H. Chen, L.M. Yusuf
  • Journal: Journal of Big Data
  • Year: 2024
  • Volume and Issue: 11(1)
  • Pages: 36
  • Citations: 3

Enhancing IoT security: A comparative study of feature reduction techniques for intrusion detection system

  • Authors: J. Li, H. Chen, M.O. Shahizan, L.M. Yusuf
  • Journal: Intelligent Systems with Applications
  • Year: 2024
  • Volume and Issue: 23
  • Article Number: 200407
  • Citations: 0

A critical review of feature selection methods for machine learning in IoT security

  • Authors: J. Li, M.S. Othman, H. Chen, L.M. Yusuf
  • Journal: International Journal of Communication Networks and Distributed Systems
  • Year: 2024
  • Volume and Issue: 30(3)
  • Pages: 264-312
  • Citations: 0

Zexiao Liang | Machine Learning | Best Researcher Award

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