Education:
Prof. Lidan Wang earned her Bachelor of Science degree in Electrical Engineering from Nanjing University of Science and Technology, China, in 1996. She pursued her Master’s degree and Ph.D. in Electrical Engineering at Chongqing University, China, completing her Ph.D. in 2008. Additionally, Prof. Wang conducted post-doctoral research at Chongqing University from 2012 to 2014, further advancing her expertise in the field of artificial intelligence and neural networks.
Professional Experience:
Prof. Lidan Wang began her academic career as an Associate Professor at Southwest University, Chongqing, China, serving from 2008 to 2012. She was promoted to Professor in 2013, a position she continues to hold. In addition to her role at Southwest University, Prof. Wang has gained international experience through various visiting professorships, including at Imperial College London, Nanyang Technological University, Texas A&M University at Qatar, and the University of Tasmania. Her leadership extends beyond teaching and research, as she currently serves as the deputy director of the Chongqing Key Laboratory of Brain-like Computing and Intelligent Control, Secretary General of the Chongqing Artificial Intelligence Society, and Director of the Chongqing Young Science and Technology Leaders Association.
Research Interests:
Prof. Lidan Wang’s research interests lie primarily in the realm of artificial intelligence, with a strong focus on artificial neural networks and neural morphological systems. Her work explores memristor devices and systems, chaotic systems, and nonlinear circuit design. Prof. Wang is particularly engaged in advancing the understanding and application of these technologies, aiming to develop innovative solutions and systems that integrate these complex components. Her research contributes significantly to the fields of artificial intelligence and neural network technologies.
Skills:
Prof. Lidan Wang possesses advanced skills in artificial intelligence, encompassing artificial neural networks and neural morphological systems. She is proficient in the design and implementation of memristor devices and systems, as well as in the analysis and development of chaotic systems and nonlinear circuits. Her expertise extends to leading and managing research projects, having successfully undertaken numerous high-profile projects including National Key R&D Program subprojects and various funding initiatives. Additionally, Prof. Wang has a strong background in patent development and academic publishing, contributing to her distinguished reputation in the scientific community.
Conclution:
Given her exceptional research contributions, extensive publication record, numerous awards, and significant leadership roles, Prof. Lidan Wang is a highly deserving candidate for the Best Researcher Award. Her work not only advances the field of artificial intelligence but also inspires and influences the global research community.
Publication Tob Noted:
Memristor-based cellular nonlinear/neural network: design, analysis, and applications
- Authors: S. Duan, X. Hu, Z. Dong, L. Wang, P. Mazumder
- Journal: IEEE Transactions on Neural Networks and Learning Systems
- Volume: 26, Issue 6
- Pages: 1202-1213
- Year: 2014
- Citations: 297
Electronic nose feature extraction methods: A review
- Authors: J. Yan, X. Guo, S. Duan, P. Jia, L. Wang, C. Peng, S. Zhang
- Journal: Sensors
- Volume: 15, Issue 11
- Pages: 27804-27831
- Year: 2015
- Citations: 296
Exponential stability of complex-valued memristive recurrent neural networks
- Authors: H. Wang, S. Duan, T. Huang, L. Wang, C. Li
- Journal: IEEE Transactions on Neural Networks and Learning Systems
- Volume: 28, Issue 3
- Pages: 766-771
- Year: 2016
- Citations: 159
A novel memristive Hopfield neural network with application in associative memory
- Authors: J. Yang, L. Wang, Y. Wang, T. Guo
- Journal: Neurocomputing
- Volume: 227
- Pages: 142-148
- Year: 2017
- Citations: 158
Memristor model and its application for chaos generation
- Authors: L. Wang, E. Drakakis, S. Duan, P. He, X. Liao
- Journal: International Journal of Bifurcation and Chaos
- Volume: 22, Issue 08
- Article Number: 1250205
- Year: 2012
- Citations: 147
Volatile and nonvolatile memristive devices for neuromorphic computing
- Authors: G. Zhou, Z. Wang, B. Sun, F. Zhou, L. Sun, H. Zhao, X. Hu, X. Peng, J. Yan, …
- Journal: Advanced Electronic Materials
- Volume: 8, Issue 7
- Article Number: 2101127
- Year: 2022
- Citations: 129
Resistive switching memory integrated with amorphous carbon-based nanogenerators for self-powered device
- Authors: G. Zhou, Z. Ren, L. Wang, J. Wu, B. Sun, A. Zhou, G. Zhang, S. Zheng, S. Duan, …
- Journal: Nano Energy
- Volume: 63
- Article Number: 103793
- Year: 2019
- Citations: 126
Artificial and wearable albumen protein memristor arrays with integrated memory logic gate functionality
- Authors: G. Zhou, Z. Ren, L. Wang, B. Sun, S. Duan, Q. Song
- Journal: Materials Horizons
- Volume: 6, Issue 9
- Pages: 1877-1882
- Year: 2019
- Citations: 123
Capacitive effect: An original of the resistive switching memory
- Authors: G. Zhou, Z. Ren, B. Sun, J. Wu, Z. Zou, S. Zheng, L. Wang, S. Duan, Q. Song
- Journal: Nano Energy
- Volume: 68
- Article Number: 104386
- Year: 2020
- Citations: 118