Mrs. Guoqin Chang | AI Security | Excellence in Research Award

Mrs. Guoqin Chang | AI Security | Excellence in Research Award 

Mrs. Guoqin Chang | AI Security | Shaanxi Science and Technology Holding Institute | China

Mrs. Guoqin Chang holds a Ph.D. in Cyberspace Security from the School of Cybersecurity at Xidian University and currently leads the Artificial Intelligence Research Group at Shaanxi KeKong Technology Industry Research Institute, while also serving as Technical Advisor to the Shaanxi Printing Science and Technology Research Institute. Over her career, Chang Guoqin has built expertise in natural language processing (NLP), domain-specific large language model deployment, prompt engineering, fine-tuning and adaptation of models, AI model security, detection of text generated by large language models, adversarial text attacks/defenses, and multimodal information fusion. She is skilled in designing and evaluating robust NLP pipelines, adversarial-robust model architectures, secure model deployment, domain adaptation techniques, and text classification systems with security-aware defenses. Her professional experience spans research and development, project leadership, and peer-reviewing for major journals including Information Sciences and Computer Engineering and Design, and she has contributed to both academic publications and applied software/patents through her inventive activity. Through her publication record, patent filings, software copyrights, and R&D project involvement, Chang Guoqin has demonstrated a strong commitment to improving the security, robustness, and domain-adaptability of modern AI. In conclusion, Chang Guoqin brings together advanced academic training, deep technical skills in NLP security and model adaptation, and real-world engineering experience  positioning her as a leading researcher/engineer in secure, domain-specific artificial intelligence and robust natural language processing solutions.

Professional Profiles: ORCID | Google scholar

Selected Publications 

  • Chang, G., Gao, H., Yao, Z., & Xiong, H. (2023). TextGuise: Adaptive adversarial example attacks on text classification model.

  • Chang, G., Gao, H., Pei, G., … & Guo, Q. (2024). The robustness of behavior-verification-based slider CAPTCHAs. Journal of Information Security and Applications.

  • Cheng, L., Zhang, Z., & Chang, G. (2019). Multimedia Social Network Authorization Scheme of Comparison-based Encryption.

  • Cheng, N., Chang, G., & Gao, H. (2020). WordChange: Adversarial Examples Generation Approach for Chinese Text Classification.

  • Chang, G., Gao, H., & Li, B. (2025). TextShelter: Text Adversarial Example Defense Based on Input Reconstruction.

  • Chang, G., & colleagues. (2019). A Survey of Research on CAPTCHA Designing and Breaking Techniques.

  • Cheng, N., Chang, G., Gao, H., … & Zhang, Y. (2020). WordChange: Adversarial Examples Generation Approach for Chinese Text Classification.

Prof. Dr. Haiyan Kang | Network Security Awards | Best Researcher Award

Prof. Dr. Haiyan Kang | Network Security Awards | Best Researcher Award

Prof. Dr. Haiyan Kang , Beijing Information Science and Technology University, China

Prof. Dr. Kang Haiyan is a prominent academic in the field of Information Security, currently serving as a professor in the Department of Information Security at Beijing Information Science and Technology University, China. He earned his Ph.D. in Computer Application Technology from Beijing Institute of Technology in 2005. A senior member of the China Computer Federation and an ACM member, Prof. Kang has received recognition as an outstanding talent in Beijing and serves as an executive member of the Privacy Protection Committee of the China Confidentiality Association. He is the chief of the national first-class major in Information Security and has contributed significantly to the field through over 110 academic publications and six monographs. His research focuses on network security, blockchain technology, and privacy protection, resulting in six provincial and ministerial awards and 25 invention patents. Committed to education, he has been honored with national and local teaching achievement awards and recognized as an Excellent Graduate Thesis Supervisor in Beijing. Additionally, he holds editorial board positions with several academic journals in the field.

Professional Profile:

SCOPUS

Prof. Dr. Kang Haiyan for the Best Researcher Award

Prof. Dr. Kang Haiyan is a remarkable candidate for the Best Researcher Award due to his extensive contributions to the fields of network security, blockchain, and privacy protection, alongside his dedication to academic excellence and leadership in education.

Education

  • Ph.D. in Computer Application Technology
    Beijing Institute of Technology, China (2005) 🎓

Work Experience

  • Professor
    Department of Information Security, Beijing Information Science and Technology University, Beijing, China
  • Chief
    National First-Class Major (Information Security) and Electronic Information (Network and Information Security) 📚
  • Editorial Board Member
    • Journal of Information Security Research
    • Cyberspace Security
    • Journal of Cyberspace Security Science
    • Journal of Beijing University of Information Science and Technology

Achievements

  • Research Awards: 6 provincial and ministerial level awards 🏆
  • Invention Patents: 25 patents for innovative contributions 💡
  • Publications: Over 110 academic papers and 6 monographs 📖

Awards and Honors

  • National Teaching Achievements: 2 Third Prizes 🥉
  • Beijing Teaching Achievements: 1 Second Prize 🥈
  • Title: Excellent Graduate Thesis Supervisor in Beijing 🌟

Publication Top Notes:

Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network

Community overlap discovery algorithm based on industrial big data

Research on the Deep Learning Method Based on Data Feature Relevance and Adaptive Differential Privacy

Enhancing data security in massive data sets using blockchain and federated learning: a loosely coupled approach

Research on Federated Sharing Methods for Massive Data in Blockchain