Zhang Rui is a PhD candidate specializing in Computer Software and Theory at the Computer Network Information Center, Chinese Academy of Sciences. With a background in Information Management and Information Systems, Zhang’s research focuses on Natural Language Processing, Automatic Text Summarization, Code Intelligence, Code Comment Generation, and Automatic Program Repair. Zhang possesses extensive experience in machine learning and deep learning theory, Python programming language, and frameworks like Pytorch. They have contributed to various projects, including the development of the “Yuanke + Digital Management System” and keyword similarity libraries. Zhang’s innovative spirit and dedication to knowledge make them a valuable asset to the field.
Author Metrics
Scopus Profile
Zhang Rui has demonstrated notable author metrics, evidenced by their publication record, conference presentations, and contributions to the field of computer science. With publications in prestigious journals such as Expert Systems with Applications and Int. J. Intell. Syst., Zhang’s work showcases their expertise in areas like code comment generation, text summarization, and grammatical correctness improvement. Additionally, abstracts of their research have been accepted by esteemed conferences like the IEEE International Conference on Big Data.Zhang Rui, affiliated with the University of Chinese Academy of Sciences in Beijing, China. They have 6 documents indexed, but no citations yet.
Education
Zhang Rui holds a Ph.D. candidacy in Computer Software and Theory from the Computer Network Information Center, Chinese Academy of Sciences (CNIC). They completed their undergraduate studies in Information Management and Information Systems at Xi’an University of Architecture and Technology. Zhang’s academic journey has equipped them with a strong foundation in computer science and positioned them as a promising researcher in their field.
Research Focus
Zhang Rui’s research primarily revolves around Natural Language Processing (NLP) and its applications in various domains. Their focus areas include Automatic Text Summarization, Code Intelligence, Code Comment Generation, and Automatic Program Repair. By leveraging machine learning and deep learning techniques, Zhang aims to develop innovative solutions to complex problems in software development and information processing.
Professional Journey
Zhang Rui’s professional journey is marked by a series of impactful projects, internships, and academic achievements. From contributing to the development of mapping applications at Shaanxi Provincial Bureau of Surveying, Mapping and Geographic Information to undertaking data management and analysis tasks at Shaanxi Provincial Resource Science and Technology Coordination Center, Zhang has honed their skills and expertise in diverse areas of computer science.
Honors & Awards
Throughout their academic and professional career, Zhang Rui has received numerous honors and awards, recognizing their outstanding achievements and contributions. These include prizes in postgraduate year-end assessments, doctoral scholarships, and awards in mathematical modeling competitions. Zhang’s dedication to excellence and innovation has been consistently acknowledged by their peers and academic institutions.
Publications Noted & Contributions
Zhang Rui’s publications have made significant contributions to the field of computer science, particularly in the areas of code comment generation, text summarization, and data storage security management. Their research papers have been published in prestigious journals and presented at renowned conferences, demonstrating their expertise and impact on advancing knowledge in their research domains.
“KFCC: A differentiation-aware and keyword-guided fine-grain code comment generation model”
- Authors: Zhang, R., Qiao, Z., Zhang, C., Yu, J.
- Published in Expert Systems with Applications in 2024
- Introduces KFCC, a model for generating fine-grained code comments that is differentiation-aware and keyword-guided. The model aims to enhance the quality and relevance of generated comments in software development.
“MESED: A Multi-Modal Entity Set Expansion Dataset with Fine-Grained Semantic Classes and Hard Negative Entities”
- Authors: Li, Y., Lu, T., Zheng, H.-T., Yuan, J., Zhang, R.
- Presented at the AAAI Conference on Artificial Intelligence in 2024
- Describes the MESED dataset, which focuses on multi-modal entity set expansion with fine-grained semantic classes and hard negative entities. This dataset contributes to research in entity set expansion and multimodal learning.
“Large Language Models for Mathematical Reasoning: Progresses and Challenges”
- Authors: Ahn, J., Verma, R., Lou, R., Zhang, R., Yin, W.
- Presented at the EACL 2024 – 18th Conference of the European Chapter of the Association for Computational Linguistics
- Discusses the progress and challenges in using large language models for mathematical reasoning, highlighting advancements and areas for improvement in this field.
“AFE-Net: Attention-Guided Feature Enhancement Network for Infrared Small Target Detection”
- Authors: Wang, K., Wu, X., Zhou, P., Yang, L., Li, Y.
- Published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing in 2024
- Presents AFE-Net, a network designed for infrared small target detection, which utilizes attention-guided feature enhancement to improve detection accuracy in remote sensing applications.
“SentMask: A Sentence-Aware Mask Attention-Guided Two-Stage Text Summarization Component”
- Authors: Zhang, R., Zhang, N., Yu, J.
- Published in the International Journal of Intelligent Systems in 2023
- Introduces SentMask, a component for two-stage text summarization that incorporates sentence-aware mask attention guidance. The model aims to improve the efficiency and effectiveness of text summarization tasks.
“AuxPOS: Improving Grammatical Correctness with Big Data Based Text Summarization”
- Authors: Zhang, R., Yu, J., Zhou, Y.
- Presented at the 2022 IEEE International Conference on Big Data
- Discusses AuxPOS, a method for improving grammatical correctness in text summarization using big data-based approaches. The paper explores techniques to enhance the quality of automatically generated summaries.
Research Timeline
Zhang Rui’s research timeline illustrates their progression and contributions in the field of computer science. Starting from their undergraduate studies at Xi’an University of Architecture and Technology to their current pursuit of a Ph.D. in Computer Software and Theory at CNIC, Zhang’s journey reflects a consistent dedication to academic excellence and research innovation.
Collaborations and Projects
Zhang Rui has actively collaborated on various projects, both academic and industrial, showcasing their ability to work in diverse teams and environments. From developing digital management systems to implementing data traceability systems based on blockchain technology, Zhang’s collaborative efforts have resulted in impactful solutions addressing real-world challenges in software development and information management.
