AANN 2025
2025 5th International Conference on Advanced Algorithms and Neural Networks
(AANN 2025) wraps up successfully！
2025 5th International Conference on Advanced Algorithms and Neural Networks(AANN 2025) is held in Qingdao, China on August 15-17, 2025. It mainly focuses on Advanced Algorithms and Neural Networks and other research fields to discuss.
AANN 2025 is technical sponsored by IEEE, IEEE CIS and host by China University of Petroleum (East China). The conference aims to provide a diverse academic platform to promote in-depth exchanges and cooperation among participants, allowing participants to explore specific topics and research fields in greater depth, promoting academic exchanges and cooperation. This conference featured a main plenary session complemented by two dedicated breakout sessions. A total of 5 experts at home and abroad have been invited to give Keynote speeches, as well as oral presentations, poster presentations.
Opening Remarks
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welcome speech from Peiyong Liu, Deputy Dean of College of Qingdao Institute Software College of Computer Science and Technology, China University of Petroleum (East China), China |
Keynote Speech
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Prof. Zhipeng Cai, IEEE Fellow, AAIA Fellow, Georgia State University, USA Speech Title: Towards Privacy Inference and Protection of Federated Learning | |
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Prof. Chanjuan Liu, National Young Talent, Dalian University of Technology, China Speech Title: Intelligent Game-Theoretic Decision-Making: From Cognitive Agent Modeling to Cross-Domain Applications | |
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Prof. Xu Cheng, National Young Talent, Tianjin University of Technology, China Speech Title: Ship As Wave buoy: Data-driven Sea State Estimation Based On Ship Motion Data | |
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Prof. Jiangtao Wang, National Young Talent, University of Science and Technology of China, China Speech Title: Data-Knowledge Dual-Driven AI and its Applications in Population Health | |
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Prof. Xiaoli Li, IEEE Fellow, AAIA Fellow, Nanyang Technological University, Singapore Speech Title: Advanced Time Series Foundation Models and Scalable Preference
Optimization for Large Language Models |
Oral Presentation
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Award Ceremony
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