postgraduate | Soochow University | China
Penghao Wu is a dedicated postgraduate student specializing in Control Science and Engineering at Suzhou University, where he is transitioning from the first to the second year of his master’s program. His research centers on explainable neural networks, fault diagnosis in large-scale systems, and multidimensional data analysis, leveraging advanced AI and machine learning methodologies. He has a strong foundation in academic research, evidenced by three high-quality publications and extensive experience with state-of-the-art algorithms. His career goal is to contribute to AI-driven solutions in fields such as large model algorithms, autonomous driving, and data analysis, aligning closely with his expertise.
Profile
Scopus
Education
Penghao Wu began his academic journey with a Bachelor’s degree in Automation from Inner Mongolia University of Technology, graduating in 2023. Excelling academically, he ranked 3rd in his major (top 3%), achieved a GPA of 4.2/5.0, and earned an average credit score of 98.94. Continuing his pursuit of excellence, he joined Suzhou University in 2023 to pursue a master’s degree in Control Science and Engineering. Currently maintaining a GPA of 3.5/4.0 and an average credit score of 87, he has undertaken courses like Advanced Mathematics, Matrix Theory, Modern Control Theory, and Mobile Robot Autonomous Navigation, building a robust technical foundation.
Experience
Penghao Wu has been actively involved in research and development throughout his academic career. His undergraduate graduation project on deep learning-based building change detection algorithms using remote sensing imagery was recognized as one of only three “Outstanding Graduation Designs” in his college. He has also participated in several impactful projects, including vehicle battery fault diagnosis using Variational Mode Decomposition and spiking neural networks for lithium-ion battery fault detection. His practical expertise extends to software systems, having developed a multifunctional intelligent control device awarded a computer software copyright.
Research Interests
Penghao’s research interests revolve around explainable artificial intelligence (XAI), deep learning, and large-scale system fault diagnosis. He focuses on designing interpretable neural network algorithms for critical applications such as autonomous vehicles and aerospace systems. By integrating data-driven approaches with domain knowledge, he aims to enhance the transparency and reliability of AI systems. His work also extends to multidimensional data analysis, with applications in remote sensing and industrial fault detection, underlining his commitment to addressing real-world challenges through cutting-edge technologies.
Awards
Penghao Wu has received multiple accolades for his academic and extracurricular achievements. Notable awards include the Graduate First-Class Scholarship (2023), recognition as an “Outstanding Student” for three consecutive years during his undergraduate studies, and a top-four finish in the CIMC China Intelligent Manufacturing Challenge (university level). His graduation project on remote sensing image analysis earned distinction as one of only three outstanding projects in his college. Additionally, he won third place in the North China University Computer Application Competition.
Publications
Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries
Published in Data-driven Control and Learning Systems Conference (EI Indexed, 2024).
Cited by: 15 articles.
Data-Driven Spiking Neural Networks for Explainable Fault Detection in Vehicle Lithium-Ion Battery Systems
Under major revision in a Tier-2 SCI journal (2024).
Cited by: 10 articles.
Multi-modal Intelligent Fault Diagnosis for Large Aviation Aircraft Based on Mamba-2
Submitted as an invited article to a Tier-1 SCI journal (2024).
Cited by: 8 articles.
Conclusion
Penghao Wu is a driven researcher and engineer, blending academic excellence with practical expertise in artificial intelligence and control systems. His strong background in fault diagnosis, deep learning, and explainability positions him as an ideal candidate for AI algorithm roles. With a proven track record of research, publications, and accolades, he is poised to make significant contributions to advancing technology in areas such as autonomous systems and intelligent data analysis.