Lecturer at Southeast University, China.
Dr. Jingxin Zhang is a Lecturer at Southeast University with expertise in fault detection, diagnosis, and process monitoring. She has made significant strides in photovoltaic power forecasting and the application of advanced data-driven methodologies in industrial processes. With a robust academic background and industry experience, Dr. Zhang has established a reputation for blending theoretical research with practical implementation. Her work on multimode process monitoring, which incorporates continual learning, sets her apart as an innovator in her field. Dr. Zhang’s research has resulted in 17 journal publications, nine patents, and significant collaborations with global institutions, enhancing her impact in both academia and industry. She has also led several research and industry projects, showcasing her ability to tackle real-world challenges effectively. Dr. Zhang is an active member of professional societies such as IEEE and the Chinese Association of Automation, further emphasizing her commitment to advancing her field.
📚 Profile
ORCID
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🎓 Education
Dr. Jingxin Zhang’s academic journey began at Harbin Engineering University, where she earned her Bachelor of Engineering degree in Electrical Engineering and Automation. She furthered her studies with a Master’s degree in Control Science and Engineering from Harbin Institute of Technology. Her academic pursuits culminated in a Ph.D. in Control Science and Engineering from Tsinghua University, one of China’s most prestigious institutions. Throughout her education, Dr. Zhang focused on developing advanced methodologies for process control, fault detection, and automation, laying the foundation for her impactful career. Her strong educational background has enabled her to contribute to both theoretical advancements and practical applications in her field. As a lifelong learner, Dr. Zhang continues to push the boundaries of her expertise, applying her knowledge to cutting-edge research in data-driven process monitoring and photovoltaic power forecasting.
💼 Experience
Dr. Jingxin Zhang has accumulated extensive experience as both an academic and a researcher. Currently serving as a Lecturer at Southeast University, she has been involved in teaching and mentoring students while advancing her research in fault detection, process monitoring, and renewable energy forecasting. Her academic career has been complemented by active collaboration with industry leaders, making her research highly applicable to real-world industrial processes. Dr. Zhang has led multiple research projects, including five ongoing and four completed ones, focusing on advanced control systems and data-driven fault detection. With nine patents to her name and numerous publications in high-impact journals, her contributions are recognized both in China and internationally. Her involvement in consultancy projects has also strengthened her ability to transfer theoretical knowledge to practical, industry-relevant innovations, positioning her as a rising star in the field of control science and engineering.
🔬 Research Interests
Dr. Jingxin Zhang’s research interests span fault detection, diagnosis, process monitoring, and renewable energy forecasting. A core area of her work lies in developing advanced data-driven approaches to fault detection and diagnosis in complex systems. Her expertise extends to photovoltaic power forecasting, where she applies control science and machine learning techniques to predict energy outputs in industrial-scale solar power systems. Dr. Zhang is particularly interested in continual learning in process monitoring, a novel approach that enables systems to adapt to new data without losing previously learned knowledge. This research is groundbreaking in its ability to improve long-term system efficiency and stability in industries such as energy, manufacturing, and automation. Her work bridges the gap between theoretical advancements in data science and real-world industrial applications, making her research highly impactful in both academic and practical contexts.
🏆 Awards and Honors
Dr. Jingxin Zhang has been recognized for her contributions to control science and engineering through various awards and honors. While specific details of her awards have yet to be widely publicized, her achievements in research and industry collaborations have earned her a solid reputation within the academic community. She has led several prestigious research projects funded by national and provincial organizations, showcasing her leadership and innovation in process monitoring and fault detection. In addition to her research accomplishments, Dr. Zhang has been awarded nine patents, further solidifying her impact on industrial applications. As an active member of professional societies such as IEEE and the Chinese Association of Automation, Dr. Zhang is well-regarded by her peers and continues to be recognized for her groundbreaking work in data-driven approaches to process control. She remains committed to advancing her field and earning additional accolades as her career progresses.
🔚 Conclusion
Jingxin Zhang is a strong candidate for the Best Researcher Award, particularly due to her innovative contributions to fault detection, data-driven approaches, and industry collaborations. With a focus on continual learning and industrial relevance, her research aligns well with the award’s criteria. Enhancing her academic visibility through more editorial roles and publications could further strengthen her application, positioning her as a leading researcher in her field.
Publications Top Notes 📚
Title: An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
Author: J Zhang, H Chen, S Chen, X Hong
Year: 2017
Citation: 68
Title: Monitoring multimode processes: A modified PCA algorithm with continual learning ability
Author: J Zhang, D Zhou, M Chen
Year: 2021
Citation: 56
Title: A data-driven learning approach for nonlinear process monitoring based on available sensing measurements
Author: S Yin, C Yang, J Zhang, Y Jiang
Year: 2016
Citation: 47
Title: Multimode process monitoring based on fault dependent variable selection and moving window-negative log likelihood probability
Author: D Wu, D Zhou, J Zhang, M Chen
Year: 2020
Citation: 29
Title: Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings
Author: J Zhang, M Chen, X Hong
Year: 2021
Citation: 28
Title: Continual learning for multimode dynamic process monitoring with applications to an ultra–supercritical thermal power plant
Author: J Zhang, D Zhou, M Chen, X Hong
Year: 2022
Citation: 24
Title: Self-learning sparse PCA for multimode process monitoring
Author: J Zhang, D Zhou, M Chen
Year: 2022
Citation: 16
Title: Adaptive cointegration analysis and modified RPCA with continual learning ability for monitoring multimode nonstationary processes
Author: J Zhang, D Zhou, M Chen
Year: 2022
Citation: 15
Title: Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation
Author: J Zhang, M Chen, H Chen, X Hong, D Zhou
Year: 2019
Citation: 14
Title: Continual learning-based probabilistic slow feature analysis for monitoring multimode nonstationary processes
Author: J Zhang, D Zhou, M Chen, X Hong
Year: 2022
Citation: 11