Dr. Jingya Wang | Dynamics Analysis | Best Researcher Award
Student/Member of University of Electronic Science and Technology of China, China.
Dr. Jingya Wang, a Ph.D. candidate in Software Engineering at the University of Electronic Science and Technology of China, excels in advancing neural network architectures and their applications. Her doctoral research focuses on control strategies and verification methods for nonlinear systems using neural networks. She has worked on projects involving memristor networks, Tibetan language recognition, and autonomous driving technology. Dr. Wang has made significant contributions with numerous publications in high-impact journals and several patents, reflecting her expertise in neural network control, optimization, and machine learning. She has been recognized with various awards, including the “Emerging Scholar Award” from her university and a Best Oral Presentation Award at the 9th International Conference on Control and Robotics Engineering. Her diverse research interests and achievements demonstrate her strong potential for the Best Researcher Award.
Dr. Jingya Wang is a highly accomplished academic currently pursuing a Ph.D. in Software Engineering at the University of Electronic Science and Technology of China, where she has achieved an impressive GPA of 3.98/4.0. She completed her Master’s degree in Software Engineering at the same institution, graduating with a GPA of 3.85/4.0. Prior to this, she earned her Bachelor’s degree in Computer Science and Technology from Shandong University of Finance and Economics, graduating with a GPA of 3.73/4.0. Her educational background reflects a consistent commitment to academic excellence and a strong foundation in computer science and engineering. Dr. Wang’s advanced studies and research in neural networks, nonlinear systems, and memristor networks highlight her dedication to pushing the boundaries of technology and software engineering.
Dr. Jingya Wang is a dedicated researcher specializing in neural networks and their applications in various fields. Currently, she is pursuing her Ph.D. in Software Engineering at the University of Electronic Science and Technology of China, where her doctoral research focuses on nonlinear system control strategies and verification methods based on neural networks. Prior to her Ph.D., she completed her M.S. in Software Engineering at the same institution, where she worked on optimizing neural networks and crowd intelligence. Dr. Wang has been involved in significant projects, including the development of Tibetan language recognition technologies and the optimization of memristive neural networks. She has also contributed to advancements in autonomous driving technology using 5G communication. Her expertise extends to neural network control, deep learning, and data analysis, and she has been recognized for her achievements with multiple awards and scholarships throughout her academic career.
Dr. Jingya Wang’s research primarily focuses on advancing neural network architectures and their applications across various domains. Her doctoral research at the University of Electronic Science and Technology of China centers on developing control strategies and verification methods for nonlinear systems using neural networks. She aims to enhance system dynamics modeling, intelligent control, and autonomous verification through innovative neural network designs. Her work extends to optimizing neural computing and crowd intelligence via memristor networks, addressing stability and convergence issues. Additionally, she explores automatic recognition technologies for Tibetan language and script, utilizing deep learning and multimodal data analysis. Dr. Wang’s interests also include memristive neural networks optimized by swarm intelligence algorithms to process large-scale heterogeneous data. Her research integrates theoretical analysis with practical applications, striving to improve computational efficiency, control system performance, and data processing capabilities.
Dr. Jingya Wang has demonstrated a robust set of research skills through her work in software engineering and neural networks. Her expertise includes designing and optimizing neural network architectures, implementing control strategies for nonlinear systems, and developing advanced verification methods using deep learning techniques. Dr. Wang excels in integrating complex system dynamics with neural network frameworks, ensuring both high-performance modeling and control. She is proficient in applying various neural network models and optimization algorithms, including reinforcement learning and fuzzy control, to real-world problems. Her research involves using dynamic driving mechanisms and memristor-based models to enhance neural network stability and efficiency. Additionally, Dr. Wang has strong experimental skills in data analysis, simulation, and algorithm design, supported by her proficiency in programming with Python, Matlab, and other tools. Her ability to adapt and apply advanced computational methods highlights her significant contributions to the field of software engineering and neural networks.
Dr. Jingya Wang has received notable recognition for her outstanding contributions in her field. Her impressive academic and research achievements have earned her several prestigious awards. She was honored with the “University of Electronic Science and Technology of China ‘Excellent Graduate Student'” award in both November 2021 and November 2023. Additionally, Dr. Wang was recognized with the “Emerging Scholar Award” in March 2024, underscoring her rising prominence in her research area. Her exceptional presentation skills were acknowledged with the “Best Oral Presentation Award” at the 9th International Conference on Control and Robotics Engineering in May 2024. Dr. Wang’s innovative contributions to technology and research have also been highlighted by numerous scholarships, including the School-level First-class Scholarship, Luxin Scholarship, and various academic scholarships. Her accolades reflect her dedication and significant impact in the realm of software engineering and neural networks.
Wang Jingya is a highly qualified candidate for the Best Researcher Award due to her exceptional academic achievements, innovative research contributions, and significant impact through publications and patents. Her research projects demonstrate a strong ability to address complex problems using advanced technologies, which is a crucial criterion for this award. Focusing on broader collaboration and increasing public engagement could enhance her profile further. Overall, Wang Jingya’s accomplishments and ongoing contributions make her a strong contender for the award.
Publications Top Notes
- “Function-dependent neural-network-driven state feedback control and self-verification stability for discrete-time nonlinear system”
- Journal: Neurocomputing
- Year: 2024
- DOI: 10.1016/j.neucom.2024.128422
- “A hybrid search mode-based differential evolution algorithm for auto design of the interval type-2 fuzzy logic system”
- Journal: Expert Systems with Applications
- Year: 2024
- DOI: 10.1016/j.eswa.2023.121271
- “A Matrix Coding Genetic Algorithm Based on Memristor for Image Edge Detection”
- Conference: Conference on Neural Information Processing (ICONIP)
- Year: 2023
- Date: December 14, 2023
- DOI: 10.1145/3638884.3638895
- “Impulsive Stabilization of Unconstrained Multilayer Recurrent Neural Networks with Node-Based Time-varying Delays”
- Conference: IEEE International Conference for Convergence in Technology (I2CT)
- Year: 2023
- DOI: 10.1109/I2CT57861.2023.10126392
- “Driver Action Recognition Using Federated Learning”
- Conference: 7th International Conference on Communication and Information Processing (ICCIP)
- Year: 2021
- Date: December 16, 2021
- DOI: 10.1145/3507971.3507985
- “Problems and solutions of MOOC application in provincial colleges and universities”
- Conference: 10th International Conference on Computer Science & Education (ICCSE)
- Year: 2019
- DOI: 10.1109/ICCSE.2019.8845338
