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Pengyu Zhu (朱鹏宇) is currently a Master’s student in Artificial Intelligence at North China Electric Power University (NCEPU), working closely with Prof. Zhenyu Wang from North China Electric Power University and Prof. Bing Li from the Institute of Automation, Chinese Academy of Sciences (CASIA). He is a member of The Chinese Association for Artificial Intelligence(中国人工智能学会) and The China Society of Image and Graphics(中国图像图形学会). His research focuses on Pattern Recognition, Time Series Foundational Model, AI for science, and Brain-Computer Interfaces.
He received his Bachelor’s degree in Artificial Intelligence from NCEPU in 2025, under the supervision of Prof. Zhenyu Wang.He studied as an exchange undergraduate at the University of Pisa, Italy, under the “Excellence Engineer Program,” funded by the China Scholarship Council, under the guidance of Prof. Umberto Desideri, Prof. Pietro Ducange, and Researcher Fabrizio Ruffini.
📖 Educations
- 2025.09 - (now), North China Electric Power University, Artificial Intelligence Integrated Bachelor–Master Program, Master’s Degree in Artificial Intelligence.
- 2025.02 - 2025.06, University of Pisa, “Excellence Engineer Program” funded by the China Scholarship Council, Undergraduate Transfer Student.
- 2021.09 - 2025.06, North China Electric Power University, Artificial Intelligence Integrated Bachelor–Master Program, Bachelor’s Degree in Artificial Intelligence.
🎖 Honors and Awards
- 2026.02: Received “The AI Time 2025 Outstanding Contribution Award” .
🔥 News
- 2026.04: One paper accepted by Pattern Recognition Letters.
📄 Pre-Prints
(†Equal contribution, *Corresponding authors)
📝 Publications
(†Equal contribution, *Corresponding authors)
A Jade Image Retrieval Method Based on Self-Supervised Learning and Dynamically Composable Attention [Paper]
Wenjia Li, Shangxiao Qiao, Pengyu Zhu, Yixuan Zhao, Zhenyu Wang*
Pattern Recognition Letters 2026
💻 Internships
- Sep 2024 – Jun 2025, Shanghai Artificial Intelligence Laboratory, China
- Position: Algorithm Intern, AI for Science Center, Research Tasks Department
- With the rapid development of AI, brain–computer interfaces (BCIs) have attracted growing attention, inspiring interest in building direct information interaction systems between the human brain and computers or external devices. Researchers have focused on constructing universal foundation models for brain science based on EEG signals to improve generalization across brain decoding tasks. These models are typically trained on multiple large-scale datasets to enhance adaptability and robustness in diverse tasks.
- Proposed a comprehensive evaluation benchmark to assess state-of-the-art EEG foundation models across six key decoding tasks, covering health monitoring, cognitive and affective analysis, and brain–computer interaction. Responsible for preprocessing all EEG datasets, reproducing and fine-tuning EEG foundation models such as LaBraM and CBraMod, as well as manuscript preparation and figure creation.
- Developed a universal EEG foundation model, UniMind, leveraging large language models (LLMs) to understand complex neural patterns, addressing the limitations of existing models in generalization across heterogeneous decoding tasks without task-specific fine-tuning. Responsible for constructing all EEG instruction datasets, model design and evaluation, as well as manuscript preparation and figure creation.
💼 Entrepreneurship
- 2025.09 – Present, Beijing Beiqing Artificial Intelligence Technology Research Co., Ltd., Beijing, China
Position: Director & General Manager - In recent years, national and local governments have introduced policies promoting the integration of AI with traditional culture, supporting cultural inheritance, innovation, and dissemination. The State Council’s “Opinions on Deeply Implementing the ‘AI+’ Action” explicitly encourages AI applications in cultural creation and dissemination, promoting content that incorporates Chinese cultural elements and driving high-quality development of the cultural industry. Additionally, the “Beijing Action Plan for Technology Empowering Cultural Innovation (2025–2027)” focuses on breakthroughs in key technologies such as digital human interaction, advancing the intelligent upgrade of museums.
- Led the development of the “YiPaiJiShi(艺拍即识)” mini-program, overseeing product planning, front-end & back-end architecture design, and core algorithm optimization to enable scenario-based artwork recognition and automatic knowledge display. The platform promotes innovative applications of AI in Chinese cultural heritage, enhances user interaction, and contributes to digital preservation of traditional culture. The product has been deployed multiple times in Beijing’s 798 Art District, fostering the integration of culture and technology.