Qiuju Yang is an Associate Professor at the School of Physics & Information Technology, Shaanxi Normal University, China. She holds a Ph.D. in Pattern Recognition and Intelligent System from Xidian University, Xi’an, China. Her research primarily revolves around computer vision, machine learning, auroral image processing, and AI-assisted diagnosis for medical imaging. With a focus on leveraging advanced technologies to understand and analyze complex phenomena, her work spans various interdisciplinary domains, including space physics, geoscience, and medical imaging.
Author Metrics
Scopus Profile
Qiuju Yang has established herself as a prolific researcher with a significant impact in her field. Her contributions are evidenced by a substantial number of publications in reputable journals and conferences, demonstrating both the quality and quantity of her work. Additionally, her research has garnered attention within the scientific community, as evidenced by citations and collaborations with esteemed colleagues and institutions worldwide.
Citations: Qiuju Yang has been cited 129 times according to Scopus, based on 98 documents.
Documents: Qiuju Yang has authored or co-authored 18 documents indexed in Scopus.
h-index: The h-index is a metric that measures both the productivity and impact of a researcher’s publications. In this case, Qiuju Yang’s h-index is 6, indicating that she has published at least 6 papers that have each been cited at least 6 times.
Education
Qiuju Yang pursued her academic journey with a solid foundation in electronic information science and technology, culminating in a Ph.D. in Pattern Recognition and Intelligent System. Her educational background provided her with the necessary skills and knowledge to delve into complex research areas, including computer vision and machine learning, which are central to her current work.
Research Focus
Qiuju Yang’s research interests encompass a diverse range of topics, with a primary focus on:
- Computer vision
- Machine learning
- Auroral image processing
- AI-assisted diagnosis for medical imaging
Her research endeavors aim to advance our understanding of complex phenomena, such as auroral events and medical imaging diagnostics, through the application of cutting-edge technologies and methodologies.
Professional Journey
Qiuju Yang’s professional journey has been marked by continuous growth and achievement. Starting as a visiting student and progressing through lecturer to her current position as an associate professor, she has demonstrated expertise in both research and teaching. Her roles have allowed her to contribute significantly to academia while also fostering collaborations and mentorship opportunities with students and colleagues.
Honors & Awards
Qiuju Yang has received recognition for her contributions to her field through various honors and awards. These accolades underscore her excellence in research and her impact on advancing knowledge in computer vision, machine learning, and related disciplines.
Publications Noted & Contributions
Qiuju Yang’s publications represent a significant contribution to the scientific community, covering diverse topics such as auroral image classification, machine learning applications in medical imaging, and space physics. Her research output demonstrates innovative approaches and methodologies, providing valuable insights into complex phenomena and advancing the state-of-the-art in her field.
Auroral Image Classification with Very Limited Labeled Data Using Few-Shot Learning
- Authors: Q. Yang, Y. Wang, J. Ren
- Published in: IEEE Geoscience and Remote Sensing Letters, 2022, 19
- Abstract: The article discusses auroral image classification utilizing few-shot learning techniques, particularly when labeled data is scarce. The method proposed offers a solution for effective classification despite limited labeled data availability.
- Citations: 3
Unsupervised Learning of Auroral Optical Flow for Recognition of Poleward Moving Auroral Forms
- Authors: Q. Yang, H. Xiang
- Published in: IEEE Transactions on Geoscience and Remote Sensing, 2022, 60
- Abstract: This article presents an unsupervised learning approach for recognizing poleward moving auroral forms by analyzing auroral optical flow patterns. The method aids in identifying specific auroral phenomena without the need for manually labeled training data.
- Citations: 2
Unsupervised Automatic Classification of All-Sky Auroral Images Using Deep Clustering Technology
- Authors: Q. Yang, C. Liu, J. Liang
- Published in: Earth Science Informatics, 2021, 14(3), pp. 1327–1337
- Abstract: This study introduces a method for unsupervised automatic classification of all-sky auroral images through deep clustering technology. The approach enables efficient classification without the requirement of labeled data, facilitating automated analysis of auroral phenomena.
- Citations: 4
Representation and Classification of Auroral Images Based on Convolutional Neural Networks
- Authors: Q. Yang, P. Zhou
- Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13, pp. 523–534, 8970288
- Abstract: This article explores the representation and classification of auroral images using convolutional neural networks (CNNs). The utilization of CNNs facilitates effective feature extraction and classification, enhancing the understanding of auroral phenomena.
- Citations: 8
Extracting Auroral Key Local Structures From All-Sky Auroral Images by Artificial Intelligence Technique
- Authors: Q. Yang, D. Tao, H. Xiang, J. Liang
- Published in: Journal of Geophysical Research: Space Physics, 2019, 124(5), pp. 3512–3521
- Abstract: This research presents a method for extracting key local structures from all-sky auroral images using artificial intelligence techniques. By leveraging advanced algorithms, the study contributes to a deeper understanding of auroral dynamics and morphology.
- Citations: 12
Research Timeline
Qiuju Yang’s research timeline showcases a trajectory of continuous exploration and innovation. From her early contributions as a graduate student to her current role as an associate professor, she has consistently pursued research avenues that push the boundaries of knowledge and contribute to solving real-world problems.
Collaborations and Projects
Qiuju Yang has been involved in numerous collaborative projects, both nationally and internationally, which have enriched her research endeavors. These collaborations have enabled her to leverage diverse expertise and resources, leading to impactful outcomes and fostering a network of colleagues and collaborators across various disciplines and institutions.
