Khrystyna Lipianina-Honcharenko | Computer Science | Young Scientist Award

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Dr. Khrystyna Lipianina-Honcharenko | Computer Science | Young Scientist Award

Associate professor, Ph.D. in information technologies at West Ukrainian National University, Ukraine

Khrystyna Lipianina-Honcharenko is a promising candidate for the Young Scientist Award due to her strong academic background and substantial contributions to research in information technology, machine learning, and socio-economic modeling. Holding a PhD in Technical Sciences and serving as an Associate Professor at the West Ukrainian National University, she has extensive experience in both teaching and research. Khrystyna is involved in high-impact international projects, such as TruScanAI and Erasmus+ initiatives, demonstrating her leadership and collaboration in cutting-edge technological advancements. Her research on data analysis, simulation, and machine learning positions her at the forefront of modern scientific inquiry. While her proficiency in English and publication presence are notable, further enhancement of her language skills and expanding her network in global research circles could increase her influence. Overall, Khrystyna’s innovative research and leadership make her a strong contender for the award, with significant potential for future contributions to the scientific community.

Professional Profile 

Education🎓

Khrystyna Lipianina-Honcharenko has an extensive educational background, primarily from West Ukrainian National University, where she has completed multiple degrees. She holds a Bachelor’s degree in Economic Cybernetics (2007–2011), followed by a Master’s in Information Technologies in Economics (2011–2012). Khrystyna continued her academic journey as a postgraduate student at the Department of Economic Cybernetics and Informatics, earning a PhD in Technical Sciences in Information Technology (2019). Her academic pursuits are ongoing, as she is currently working towards her Doctor of Technical Sciences degree in the Department of Information Computer Systems and Control at the same university, which she is expected to complete in 2025. Her education reflects a strong foundation in both the technical and economic aspects of information systems, further enhanced by her focus on machine learning and data analysis. This solid academic background has significantly contributed to her research and teaching expertise.

Professional Experience📝

Khrystyna Lipianina-Honcharenko has a rich professional experience in academia, primarily at West Ukrainian National University (WNU). She began her career as a Laboratory Assistant in the Department of Economic Cybernetics and Informatics from 2012 to 2014, where she gained foundational experience in research and teaching. Khrystyna then advanced to the role of Lecturer in the same department from 2013 to 2020, and later became a Senior Lecturer in the Department of Information Computer Systems and Control from 2020 to 2021. Her expertise was further recognized when she was promoted to Associate Professor in 2021, a position she holds currently. Throughout her career, Khrystyna has not only contributed to teaching but has also been actively involved in research, particularly in areas such as machine learning, data analysis, and socio-economic modeling. Her experience spans both academic instruction and hands-on involvement in high-impact international research projects, highlighting her leadership and expertise.

Research Interest🔎

Khrystyna Lipianina-Honcharenko’s research interests lie at the intersection of information technology, machine learning, and socio-economic modeling. She is particularly focused on data analysis, simulation, and the application of artificial intelligence methods in cyber-physical systems. Her work explores the use of machine learning techniques to model and forecast socio-economic processes, aiming to improve decision-making in various fields, including economics and technology. Khrystyna has also contributed to innovative projects like TruScanAI, which uses AI to detect fake information, and Auralisation of Acoustic Heritage Sites, which combines augmented and virtual reality to preserve cultural heritage. Her research interests extend to structural and statistical identification of hierarchical objects, as well as the development of tools for analyzing complex systems. Through these endeavors, Khrystyna seeks to advance the integration of technology and data-driven methods in solving real-world challenges, particularly in the context of socio-economic systems and information technologies.

Award and Honor🏆

Khrystyna Lipianina-Honcharenko has been recognized for her significant contributions to research and education, particularly in the fields of information technology and machine learning. While specific awards and honors are not detailed in the available information, her involvement in prestigious international projects such as Erasmus+ and her participation in high-impact research initiatives like TruScanAI and Auralisation of Acoustic Heritage Sites underscore her academic and professional recognition. These projects highlight her leadership and innovation, earning her respect within the academic community. Additionally, her active role in the Erasmus+ KA2 Work4CE program demonstrates her commitment to advancing higher education and interdisciplinary collaboration. Khrystyna’s extensive publication record and contributions to scientific advancements further demonstrate her growing influence in her field. As she continues to contribute to international collaborations and projects, it is likely that her efforts will lead to more formal recognitions and awards, further solidifying her place as a leader in her research domain.

Research Skill🔬

Khrystyna Lipianina-Honcharenko possesses a diverse and robust set of research skills, particularly in the areas of data analysis, machine learning, and modeling of socio-economic processes. She is proficient in programming languages such as R and Python, which are essential for data processing, algorithm development, and machine learning applications. Her expertise extends to using various application packages like MS Excel, Mathcad, AnyLogic, and GeoDa, allowing her to model complex systems and analyze large datasets effectively. Khrystyna is well-versed in both qualitative and quantitative research methodologies, including structural and statistical identification of hierarchical objects, a skill she applied in projects related to cyber-physical systems. Her ability to combine technical knowledge with socio-economic modeling enables her to tackle interdisciplinary research challenges. Moreover, her involvement in international projects showcases her capacity for collaborative, cross-cultural research, further enhancing her adaptability and competence in applying advanced research techniques in diverse contexts.

Conclusion💡

Khrystyna Lipianina-Honcharenko is a strong candidate for the Young Scientist Award, thanks to her academic accomplishments, innovative research projects, and leadership in international collaborations. Her dedication to the field of information technology, machine learning, and socio-economic modeling positions her as an emerging scientist with significant potential for future contributions. With continued professional development in areas such as language proficiency and broader networking, Khrystyna could enhance her impact and further distinguish herself in her field.

Publications Top Noted✍️

  • Title: Decision tree based targeting model of customer interaction with business page
    Authors: H Lipyanina, A Sachenko, T Lendyuk, S Nadvynychny, S Grodskyi
    Year: 2020
    Citations: 37

  • Title: Economic Crime Detection Using Support Vector Machine Classification
    Authors: A Krysovatyy, H Lipyanina-Goncharenko, S Sachenko, O Desyatnyuk
    Year: 2021
    Citations: 25

  • Title: Assessing the investment risk of virtual IT company based on machine learning
    Authors: H Lipyanina, V Maksymovych, A Sachenko, T Lendyuk, A Fomenko, I Kit
    Year: 2020
    Citations: 24

  • Title: Targeting Model of HEI Video Marketing based on Classification Tree
    Authors: H Lipyanina, S Sachenko, T Lendyuk, A Sachenko
    Year: 2020
    Citations: 22

  • Title: Concept of the intelligent guide with AR support
    Authors: K Lipianina-Honcharenko, R Savchyshyn, A Sachenko, A Chaban, I Kit
    Year: 2022
    Citations: 19

  • Title: Intelligent Method of a Competitive Product Choosing based on the Emotional Feedbacks Coloring
    Authors: R Gramyak, H Lipyanina-Goncharenko, A Sachenko, T Lendyuk
    Year: 2021
    Citations: 19

  • Title: Method of detecting a fictitious company on the machine learning base
    Authors: H Lipyanina, S Sachenko, T Lendyuk, V Brych, V Yatskiv, O Osolinskiy
    Year: 2021
    Citations: 17

  • Title: Multiple regression method for analyzing the tourist demand considering the influence factors
    Authors: V Krylov, A Sachenko, P Strubytskyi, D Lendiuk, H Lipyanina
    Year: 2019
    Citations: 13

  • Title: Recognizing the Fictitious Business Entity on Logistic Regression Base
    Authors: A Krysovatyy, K Lipianina-Honcharenko, S Sachenko, O Desyatnyuk
    Year: 2022
    Citations: 9

  • Title: Сучасні інформаційні технології
    Authors: ОВ Вовкодав, ХВ Ліп’яніна
    Year: 2017
    Citations: 9

  • Title: Classification Method of Fictitious Enterprises Based on Gaussian Naive Bayes
    Authors: A Krysovatyy, H Lipyanina-Goncharenko, O Desyatnyuk, S Sachenko
    Year: 2021
    Citations: 8

  • Title: Intelligent information system for product promotion in internet market
    Authors: K Lipianina-Honcharenko, C Wolff, A Sachenko, O Desyatnyuk
    Year: 2023
    Citations: 7

  • Title: An intelligent method for forming the advertising content of higher education institutions based on semantic analysis
    Authors: K Lipianina-Honcharenko, T Lendiuk, A Sachenko, O Osolinskyi
    Year: 2021
    Citations: 7

  • Title: Intelligent waste-volume management method in the smart city concept
    Authors: K Lipianina-Honcharenko, M Komar, O Osolinskyi, V Shymanskyi
    Year: 2023
    Citations: 6

  • Title: Intelligent method for classifying the level of anthropogenic disasters
    Authors: K Lipianina-Honcharenko, C Wolff, A Sachenko, I Kit, D Zahorodnia
    Year: 2023
    Citations: 6

Hussain A. Younis | Computer Science | Best Researcher Award

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Mr. Hussain A. Younis | Computer Science | Best Researcher Award

College of Education at University of Basrah, Iraq

Hussain A. Younis is a dedicated researcher specializing in Artificial Intelligence, Security, Digital Image Processing, and Robotics. With a strong academic background from India and Malaysia and an affiliation with the University of Basrah, he has published impactful research in high-ranking journals and IEEE conferences. His work demonstrates interdisciplinary expertise, particularly in AI applications, human-robot interaction, and digital security. As an active IEEE member and potential reviewer, he is engaged in professional research communities. While his contributions are commendable, completing his Ph.D., increasing Q1/Q2 journal publications, securing research grants, and enhancing international collaborations would further strengthen his research profile. His growing citation impact and involvement in digital transformation research make him a strong candidate for the Best Researcher Award. With continued contributions in leadership, industry collaborations, and high-impact research, Hussain A. Younis is well-positioned to make significant advancements in the field of computer science and engineering.

Professional Profile 

Education

Hussain A. Younis has a strong academic background in computer science, with a Master’s degree earned in 2012 from India and ongoing Ph.D. studies since 2019 in Malaysia. His educational journey reflects a commitment to advanced research in Artificial Intelligence, Security, Digital Image Processing, and Robotics. His affiliation with the University of Basrah further strengthens his academic and research foundation, allowing him to contribute significantly to the field. Throughout his studies, he has focused on interdisciplinary research, exploring innovative solutions in AI-driven security systems, pattern recognition, and human-robot interaction. His academic pursuits have been complemented by active participation in professional organizations like IEEE, where he is a member and a prospective reviewer. While his research credentials are impressive, completing his Ph.D. will further solidify his expertise and credibility. His educational background positions him as a promising researcher with the potential to make impactful contributions to the scientific community.

Professional Experience

Hussain A. Younis has extensive professional experience in research and academia, with a focus on Artificial Intelligence, Security, Digital Image Processing, and Robotics. He is affiliated with the University of Basrah, where he contributes to both teaching and research in computer science. His work spans various interdisciplinary areas, including AI-driven security systems, pattern recognition, and human-robot interaction. As an IEEE member, he actively participates in academic conferences and serves as a prospective reviewer, further demonstrating his engagement in the global research community. His publications in high-impact journals and IEEE conferences highlight his contributions to advancing technology, particularly in robotics education, cybersecurity, and digital transformation. While his professional experience is commendable, taking on leadership roles in research projects, securing grants, and fostering international collaborations would further enhance his impact. His commitment to innovation and academic excellence makes him a valuable contributor to the scientific and technological landscape.

Research Interest

Hussain A. Younis’s research interests lie at the intersection of Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His work explores innovative AI-driven solutions for enhancing security, improving human-robot interaction, and advancing digital transformation. He is particularly interested in speech recognition models, robotics in education, and secure cryptographic systems, contributing to cutting-edge developments in these fields. His research also addresses challenges in cybersecurity, focusing on encryption techniques and stream cipher systems to enhance data protection. Additionally, he investigates distinguishable patterns in image processing, applying AI techniques to optimize pattern recognition for various applications. Through his active participation in IEEE conferences and high-impact journal publications, he continuously contributes to technological advancements. His interdisciplinary approach and commitment to innovation position him as a promising researcher in AI and security, with the potential to make significant contributions to both academic research and real-world applications.

Award and Honor

Hussain A. Younis has been recognized for his contributions to research in Artificial Intelligence, Security, Digital Image Processing, and Robotics through various academic achievements and honors. His publications in high-impact journals and IEEE conferences reflect his dedication to advancing knowledge in these fields. As an active IEEE member, he has gained recognition within the global research community and has been invited to serve as a reviewer for IEEE conferences in Iraq. His work on robotics in education, cybersecurity, and encryption systems has earned significant attention, highlighting his expertise in interdisciplinary research. While his achievements are commendable, securing prestigious research grants, international fellowships, and industry collaborations would further enhance his profile. His commitment to innovation and scientific excellence makes him a strong contender for research awards, and with continued contributions, he is poised to receive greater recognition for his impact on the technological and academic landscape.

Research Skill

Hussain A. Younis possesses strong research skills in Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His expertise lies in developing AI-driven solutions for security, speech recognition, and human-robot interaction, showcasing his ability to integrate multiple disciplines. He is proficient in data analysis, algorithm development, cryptographic security, and digital transformation technologies, enabling him to conduct high-quality research with practical applications. His experience in publishing in high-impact journals and IEEE conferences reflects his ability to conduct rigorous academic research and communicate findings effectively. As an active IEEE member and prospective reviewer, he demonstrates critical analysis and evaluation skills essential for scholarly contributions. Additionally, his research involves problem-solving, programming, and system design, particularly in robotics education and cybersecurity. To further enhance his research impact, focusing on international collaborations, advanced machine learning techniques, and securing research grants would strengthen his expertise and academic contributions.

Conclusion

Hussain A. Younis demonstrates strong research potential with impactful publications in AI, Robotics, and Security. His IEEE membership, interdisciplinary research, and international exposure make him a strong candidate for the Best Researcher Award. However, completing the Ph.D., increasing high-impact publications, and engaging in leadership roles would significantly enhance his eligibility for this prestigious award.

Publications Top Noted

  1. Hussain A. Younis, TAE Eisa, M Nasser, TM Sahib, AA Noor, OM Alyasiri, … (2024)

    • A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges
    • Citations: 114

  2. Hussain A. Younis, NIR Ruhaiyem, W Ghaban, NA Gazem, M Nasser (2023)

    • A systematic literature review on the applications of robots and natural language processing in education
    • Citations: 48

  3. IM Hayder, TA Al-Amiedy, W Ghaban, F Saeed, M Nasser, GA Al-Ali, HA Younis, … (2023)

    • An intelligent early flood forecasting and prediction leveraging machine and deep learning algorithms with advanced alert system
    • Citations: 40

  4. OM Alyasiri, K Selvaraj, Hussain A. Younis, TM Sahib, MF Almasoodi, IM Hayder (2024)

    • A survey on the potential of artificial intelligence tools in tourism information services
    • Citations: 38

  5. S Salisu, NIR Ruhaiyem, TAE Eisa, M Nasser, F Saeed, HA Younis (2023)

    • Motion capture technologies for ergonomics: A systematic literature review
    • Citations: 25

  6. IM Hayder, GANA Ali, Hussain A. Younis (2023)

    • Predicting reaction based on customer’s transaction using machine learning approaches
    • Citations: 20

  7. Hussain A. Younis, ASA Mohamed, R Jamaludin, MNA Wahab (2021)

    • Survey of robotics in education, taxonomy, applications, and platforms during COVID-19
    • Citations: 20

  8. OM Alyasiri, AM Salman, S Salisu (2024)

    • ChatGPT revisited: Using ChatGPT-4 for finding references and editing language in medical scientific articles
    • Citations: 18

  9. Hussain A. Younis, OM Alyasiri, Muthmainnah, TM Sahib, IM Hayder, S Salisu, … (2023)

    • ChatGPT Evaluation: Can It Replace Grammarly and Quillbot Tools
    • Citations: 16

  10. MA Hussain, Hussain A. Younis, Iznan H. Hasbullah, Ghofran Kh. Shraida, Hameed A … (2023)

  • An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher
  • Citations: 14
  1. Hussain A. Younis, ASA Mohamed, MN Ab Wahab, R Jamaludin, S Salisu (2021)
  • A new speech recognition model in a human-robot interaction scenario using NAO robot: Proposal and preliminary model
  • Citations: 11
  1. Hussain A. Younis, TY Abdalla, AY Abdalla (2009)
  • Vector quantization techniques for partial encryption of wavelet-based compressed digital images
  • Citations: 11

Siliang Ma | Computer Science | Best Researcher Award

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Dr. Siliang Ma | Computer Science | Best Researcher Award

Senior Algorithm Engineer at School of Computer Science and Engineering, South China University of Technology, China

Dr. Siliang Ma, a Ph.D. candidate at South China University of Technology, is an accomplished researcher specializing in computer science with a focus on image processing and machine learning. With an excellent academic record, including a bachelor’s degree from South China Agricultural University (GPA: 3.99/5), Dr. Ma has made significant contributions to cutting-edge research. His works, published in esteemed journals such as Acta Automatica Sinica and Image and Vision Computing, address topics like calligraphy character recognition, multilingual scene text spotting, and efficient bounding box regression through novel loss functions like MPDIoU and FPDIoU. A skilled programmer proficient in Python, Java, and C#, he has developed robust image processing algorithms and software applications. Dr. Ma also contributes as a reviewer for leading conferences like ICRA and ICASSP, reflecting his commitment to advancing the research community. His innovative and impactful work positions him as a rising talent in computational science.

Professional Profile 

Education

Dr. Siliang Ma has a strong educational background in computer science and engineering. He is currently pursuing a Ph.D. at the South China University of Technology, where he has maintained an excellent GPA of 86.33/100. His doctoral research focuses on cutting-edge topics in image processing, machine learning, and computational algorithms, demonstrating both theoretical depth and practical relevance. Prior to this, Dr. Ma earned his bachelor’s degree from South China Agricultural University, graduating with a remarkable GPA of 3.99/5. His undergraduate studies in mathematics and informatics laid a solid foundation for his advanced research pursuits, equipping him with the analytical and technical skills essential for solving complex computational problems. Through rigorous academic training and dedication, Dr. Ma has excelled in his education, which is further reflected in his extensive publications in high-impact journals and his active engagement in academic conferences and peer reviews.

Professional Experience

Dr. Siliang Ma has gained valuable professional experience through diverse roles in research and industry, complementing his academic achievements. He interned as a Data Analyst at the China Construction Bank Guangdong Branch Technology Center, where he conducted financial data analysis using PostgreSQL, mastering database operations and complex linked table queries. As a Quality Engineer at the China Mobile Guangdong Branch Business Support Center, he developed a JavaWeb-based minimum feature set for user registration, login, and management, and implemented automated quality testing workflows using Jenkins. These roles allowed Dr. Ma to hone his skills in software development, data analysis, and quality assurance, showcasing his ability to translate theoretical knowledge into practical applications. Additionally, his expertise in programming and image processing has led to impactful contributions in academia, particularly in algorithm development. This blend of industrial and research experience positions Dr. Ma as a versatile professional in computer science and engineering.

Research Interest

Dr. Siliang Ma’s research interests lie at the intersection of computer vision, machine learning, and image processing. He is particularly focused on developing innovative algorithms and techniques for efficient and accurate object detection, scene text recognition, and character recognition. His work explores advanced loss functions, such as MPDIoU and FPDIoU, to optimize bounding box regression for both traditional and rotated object detection. Additionally, Dr. Ma has a keen interest in multilingual scene text spotting, where he leverages character-level features and benchmarks to improve the accuracy of text recognition across diverse languages. His research extends to robust graph learning and hypergraph-enhanced self-supervised models for social recommendation systems, showcasing his ability to address complex, real-world challenges. Through his work, Dr. Ma aims to bridge theoretical advancements with practical applications, contributing to the broader fields of artificial intelligence, data analysis, and computational optimization.

Award and Honor

Dr. Siliang Ma has been recognized for his academic and research excellence through various accolades and contributions. As a Ph.D. candidate at South China University of Technology, his consistent high performance, reflected in his impressive GPA, underscores his dedication to academic rigor. Although specific awards or honors are not explicitly listed in his profile, his role as a reviewer for prestigious conferences such as ICRA and ICASSP highlights his esteemed position within the research community. Dr. Ma’s impactful publications in top-tier journals and conferences, including Acta Automatica Sinica and Image and Vision Computing, further demonstrate the high regard in which his work is held. His innovative contributions to image processing and machine learning have earned him recognition as a rising talent in his field. These achievements reflect Dr. Ma’s commitment to advancing computational science and his growing influence in academic and professional circles.

Conclusion

Siliang Ma is a strong candidate for the Best Researcher Award due to his impressive academic record, significant publications, and technical expertise. His contributions to advanced image processing algorithms and innovative loss functions for object detection demonstrate technical ingenuity and research excellence. To further strengthen his profile, he could expand his research impact through interdisciplinary work, mentorship roles, and greater industry engagement.

Publications Top Noted

  • Title: FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection
    Authors: Siliang Ma, Yong Xu
    Year: 2024
    Citation: Ma, S., & Xu, Y. (2024). FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection. Image and Vision Computing. https://doi.org/10.1016/j.imavis.2024.105381
  • Title: Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach
    Authors: Siliang Ma, Yong Xu
    Year: 2024
    Citation: Ma, S., & Xu, Y. (2024). Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach. American Journal of Computer Science and Technology. https://doi.org/10.11648/j.ajcst.20240703.12

Qiuju Yang | Machine learning | Best Researcher Award

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🌟Assoc Prof Dr. Qiuju Yang, Machine learning, Best Researcher Award🏆

  •  Associate Professor at Shaanxi Normal University, China

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.