Keynote Speaker

 焦李成，Licheng Jiao，Xidian University, China  

焦李成，欧洲科学院院士，IEEE Life Fellow。现任西安电子科技大学华山杰出教授、人工智能研究院院长，教育部科技委学部委员、教育部人工智能科技创新专家组专家、国家级领军人才首批入选者、教育部长江学者计划创新团队负责人、"一带一路"人工智能创新联盟理事长，陕西省人工智能产业技术创新战略联盟理事长，中国人工智能学会第六-七届副理事长，全国高校人工智能与大数据创新联盟副理事长，亚洲计算智能学会主席，IEEE/IET/CSIG/CAAI/CAA/CIE/CCF/AAIA/ACIS/AIIA Fellow，连续十二年入选爱思唯尔高被引学者榜单。主要研究方向为智能感知与图像理解、深度学习与类脑计算、进化优化与遥感解译。曾获国家自然科学奖二等奖、吴文俊人工智能杰出贡献奖、霍英东青年教师奖、全国模范教师称号、中国青年科技奖、及省部级一等奖以上科技奖励十余项。

Licheng Jiao, Member of Academia Europaea, IEEE Life Fellow, currently serves as Huashan Distinguished Professor at Xidian University, Director of the Artificial Intelligence Research Institute, Director of the Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, Director of the International Joint Research Center for Intelligent Perception and Computing, Director of the International Cooperation Joint Laboratory for Intelligent Perception and Computing, Director of the National Innovation and Intelligence Base for Intelligent Information Processing Science and Technology, Member of the Academic Division of the Ministry of Education's Science and Technology Committee, Expert of the Ministry of Education's Artificial Intelligence Science and Technology Innovation Expert Group, among the first batch of national leading talents, Leader of the Innovation Team under the Chang Jiang Scholars Program of the Ministry of Education, Chairman of the "Belt and Road" Artificial Intelligence Innovation Alliance, Chairman of the Shaanxi Artificial Intelligence Industry Technology Innovation Strategic Alliance, Vice Chairman of the 6th-7th Chinese Association for Artificial Intelligence, Vice Chairman of the National University Artificial Intelligence and Big Data Innovation Alliance, Chairman of the Asian Society of Computational Intelligence, and Fellow of IEEE/IET/CAAI/CAA/CIE/CCF/CSIG/AAIA/ACIS/AIIA. He has been consecutively listed in the Elsevier Highly Cited Researchers list for ten years. His main research areas include intelligent perception and image understanding, deep learning and brain-like computing, evolutionary optimization and remote sensing interpretation. He has received numerous awards, including the Second Prize of the National Natural Science Award, the Wu Wenjun Artificial Intelligence Outstanding Contribution Award, the "Qiushi Artificial Intelligence" Outstanding Contribution Award, the Huo Yingdong Young Teachers Award, the National Model Teacher title, the China Youth Science and Technology Award, and over ten provincial and ministerial-level first prizes in science and technology awards.

报告题目 (Speech Title): 物理和类脑双轮驱动的下一代感知与识别

Next-Generation Perception and Recognition Driven by Physics and Brain-Inspired Intelligence

报告摘要 (Abstract):

以大模型为代表的数据驱动人工智能在复杂环境感知、逻辑推理、可解释性及能效比等方面仍面临诸多挑战。本报告旨在深入剖析物理机理与类脑智能双轮驱动的下一代感知与识别技术的科学内涵与发展范式。报告将系统梳理物理启发与类脑智能方法在神经网络架构、物理约束学习、多尺度几何分析、神经形态感知及高效学习机制等方向的研究进展；并结合团队在物理可解释深度网络理论、类脑学习算法和复杂环境感知等方面的长期积累，分享相关代表性成果。最后，围绕如何融合物理规律、类脑机制与数据智能，构建高效、鲁棒、可解释的下一代感知与识别架构，提出若干思考与展望。

Current data-driven artificial intelligence, represented by large-scale models, still faces significant challenges in complex-environment perception, logical reasoning, interpretability, and energy efficiency. This talk explores the scientific foundations and emerging paradigms of next-generation perception and recognition jointly driven by physical principles and brain-inspired intelligence. It reviews recent advances in physics-inspired and brain-inspired approaches, including neural network architectures, physics-constrained learning, multiscale geometric analysis, neuromorphic perception, and efficient learning mechanisms. Drawing on our team's long-term research in physically interpretable deep-network theory, brain-inspired learning algorithms, and complex-environment perception, the talk will also present several representative achievements. Finally, it will discuss future directions for integrating physical laws, brain-inspired mechanisms, and data intelligence to build more efficient, robust, and interpretable architectures for next-generation perception and recognition.