Prof. Alessandro Astolfi, Imperial College London, U.K., IEEE Fellow, Member of the Academy of Europe

Alessandro Astolfi (IEEE Fellow) graduated in electronic engineering from the University of Rome in 1991. In 1992 he joined ETH-Zurich where he obtained a M.Sc. in Information Theory in 1995 and the Ph.D. degree with Medal of Honor in 1995 with a thesis on discontinuous stabilization of nonholonomic systems. In 1996 he was awarded a Ph.D. from the University of Rome “La Sapienza” for his work on nonlinear robust control. Since 1996 he has been with the Electrical and Electronic Engineering Department of Imperial College London, London (UK), where he is currently Professor of Nonlinear Control Theory and College Consul for the Faculty of Engineering and Business School. From 2010 to 2022 he served as Head of the Control and Power Group at Imperial College London and from 1998 to 2003 he was an Associate Professor at the Dept. of Electronics and Information of the Politecnico of Milano. Since 2005 he has also been a Professor at Dipartimento di Ingegneria Civile e Ingegneria Informatica, University of Rome Tor Vergata. His research interests are focused on mathematical control theory and control applications, with special emphasis for the problems of discontinuous stabilization, robust and adaptive control, observer design and model reduction. He is the author of over 180 journal papers; 30 book chapters; and over 270 papers in refereed conference proceedings. He is the author (with D. Karagiannis and R. Ortega) of the monograph “Nonlinear and Adaptive Control with Applications” (Springer-Verlag). He is the recipient of the IEEE CSS A. Ruberti Young Researcher Prize (2007), the IEEE RAS Googol Best New Application Paper Award (2009), the IEEE CSS George S. Axelby Outstanding Paper Award (2012), the Automatica Best Paper Award (2017), and the IEEE Trans, on Control Systems Technology Best Paper Award (2023). He is a “Distinguished Member” of the IEEE CSS, IEEE Fellow, IFAC Fellow and Member of the Academia Europaea. He served as Associate Editor for Automatica, Systems and Control Letters, the IEEE Trans. on Automatic Control, the International Journal of Control, the European Journal of Control and the Journal of the Franklin Institute; as Area Editor for the Int. J. of Adaptive Control and Signal Processing; as Senior Editor for the IEEE Trans. on Automatic Control; and as Editor-in Chief for the European Journal of Control. He is currently Editor-in-Chief of the IEEE Trans. on Automatic Control (2018–). He served as Chair of the IEEE CSS Conference Editorial Board (2010-2017) and in the IPC of several international conferences. He has served as Chair of the IEEE CSS Antonio Ruberti Young Researcher Prize (2015-2021); he is Vice Chair of the IFAC Technical Board (2020-2023) and he has been/is a Member of the IEEE Fellow Committee (2016), (2019-2022). He is currently a member of the IEEE PSPB Strategic Planning Committee and of the IEEE Fellow Nomination & Appointment Committee.

Title: Geometry, Coordinates, Invariance and Energy in the Control of Unmanned Systems

Abstract: Geometry, coordinates, invariance and energy play a key role in the control of unmanned systems. We illustrate their role in the (adaptive) stabilization of underwater vehicles, surface vessels, tractor-trailer systems, unmanned aircraft and magnetically controlled satellites.

Prof. Marios Polycarpou,  University of Cyprus, IEEE Fellow, Member of the Academy of Europe

Marios Polycarpou is a Professor of Electrical and Computer Engineering and the Founder of the KIOS Research and Innovation Center of Excellence at the University of Cyprus. He is also a Member of the Cyprus Academy of Sciences, Letters, and Arts, an Honorary Professor of Imperial College London, and a Member of Academia Europaea (The Academy of Europe).  He received the B.A degree in Computer Science and the B.Sc. in Electrical Engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, in 1989 and 1992 respectively. His teaching and research interests are in intelligent systems and networks, adaptive and learning control systems, fault diagnosis, machine learning, and critical infrastructure systems. Prof. Polycarpou is the recipient of the 2023 IEEE Frank Rosenblatt Technical Field Award and the 2016 IEEE Neural Networks Pioneer Award. He is a Fellow of IEEE and IFAC. He served as the President of the IEEE Computational Intelligence Society (2012-2013), as the President of the European Control Association (2017-2019), and as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004-2010). Prof. Polycarpou currently serves on the Editorial Boards of the Proceedings of the IEEE and the Annual Reviews in Control. His research work has been funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant, the ERC Synergy Grant and the EU-Widening Teaming program. 

Title:  Connecting AI to the Cyber-Physical World: Risks, Opportunities and Challenges

Abstract: The development of cyber-physical systems with multiple sensor/actuator components and feedback loops has given rise to advanced automation applications, including energy and power, intelligent transportation, water systems, manufacturing, etc. Traditionally, feedback control has focused on enhancing the tracking and robustness performance of the closed-loop system; however, as cyber-physical systems become more complex and interconnected and more interdependent, there is a need to refocus our attention not only on performance but also on the resilience of cyber-physical systems. In situations of unexpected events and faults, artificial intelligence and machine learning can play a key role in improving the fault tolerance of cyber-physical systems and preventing serious degradation or a catastrophic system failure. The goal of this presentation is to provide insight into the design and analysis of intelligent monitoring methods for cyber-physical systems, which will ultimately lead to more resilient societies.

Prof. Ming Cao, University of Groningen, Netherlands, IEEE Fellow

Ming Cao has since 2016 been a professor of networks and robotics with the Engineering and Technology Institute (ENTEG) at the University of Groningen, the Netherlands, where he started as an assistant professor in 2008. Since 2022 he is the director of the Jantina Tammes School of Digital Society, Technology and AI at the same university. He received the Bachelor degree in 1999 and the Master degree in 2002 from Tsinghua University, China, and the Ph.D. degree in 2007 from Yale University, USA. From 2007 to 2008, he was a Research Associate at Princeton University, USA. He worked as a research intern in 2006 at the IBM T. J. Watson Research Center, USA. He is the 2017 and inaugural recipient of the Manfred Thoma medal from the International Federation of Automatic Control (IFAC) and the 2016 recipient of the European Control Award sponsored by the European Control Association (EUCA). He is an IEEE fellow and a distinguished lecturer of the IEEE Control Systems Society. He is a Senior Editor for Systems and Control Letters, an Associate Editor for IEEE Transactions on Automatic Control, IEEE Transaction of Control of Network Systems and IEEE Robotics & Automation Magazine, and was an associate editor for IEEE Transactions on Circuits and Systems and IEEE Circuits and Systems Magazine. He is a member of the IFAC Council. His research interests include autonomous robots and multi-agent systems, complex networks and decision-making processes.

Title: Navigation and Motion Coordination of Platoons of Autonomous Robots

Abstract: Team movements, including platoon navigation and path-following, are fundamental functions for mobile robots to carry out environmental monitoring and sampling tasks or to meet the collective transportation demands. In this context, new challenges arise when control algorithms have to be designed for a team of robots with limited communication capacity and the environment may contain obstacles or have constrained geometric shapes. In this talk, I show how to design guiding vector fields to enable motion coordination among robots that allow individual robots to join and leave a platoon flexibly; I will focus on dealing with possible singularity points of such guiding vector fields. I also show how to construct composite guiding vector fields to avoid colliding with obstacles. Both theoretical guarantees and experimental validations are discussed for practical scenarios. 

陈虹，同济大学长聘特聘教授，国家杰青，IEEE Fellow

Prof. Hong Chen, Tongji University, China, IEEE Fellow

陈虹，同济大学长聘特聘教授，IEEE Fellow，中国自动化学会会士，中国汽车工程学会会士，国家杰出青年科学基金获得者。曾任吉林大学汽车仿真与控制国家重点实验室主任，现任同济大学电子与信息工程学院院长，兼任中国自动化学会副理事长、车辆控制与智能化专业委员会主任委员等。浙江大学过程控制及自动化仪表专业获工学和工学硕士，德国斯图加特大学获工学博士。主要研究方向为模型预测控制、汽车控制与自动驾驶、人工智能与应用。

题目：面向汽车智能的实时预测控制

摘要：模型预测控制（MPC）因目标驱动、兼容数据-物理模型、主动遵守安全约束等特点，是实现安全高效智能的重要路径。MPC滚动优化原理被广泛应用于智能系统的优化决策与规划，以及运动控制，但核心瓶颈在于优化问题的在线实时求解。本报告将系统阐述MPC的预测-滚动优化基本原理、加速计算方法以及在汽车驾驶自动化中的应用。

Hong Chen is a distinguished professor of Tongji University, IEEE Fellow, CAA-China Fellow, and SAE-China Fellow. She currently serves as Dean of the College of Electronic and Information Engineering, and concurrently as CAA Vice President and TC-CVI Chair. She received the B.S. and M.S. degrees in process control from Zhejiang University in China respectively, and the Ph.D. degree in system dynamics and control engineering from the University of Stuttgart in Germany. She was a recipient of the National Science Fund for Distinguished Young Scholars, and served as the Director of the State Key Laboratory of Automotive Simulation and Control at Jilin University. Her current research interests include model predictive control, learning control and applications in mechatronic systems focusing on automotive systems and automated driving.

Title: Real-Time MPC for Automotive Intelligence

Abstract: Model Predictive Control (MPC), with its goal-oriented nature, compatibility with data-driven, physics-based and mixed models, and its proactive enforcement of safety constraints, has become a key pathway to achieving safe, efficient and intelligent systems. While the moving optimization implementation has been widely applied to decision-making, planning and motion control, however, the core bottleneck remains the online real-time solution of the underlying optimization problem. This talk will present the fundamental principle of MPC, highlight recent advances in computational acceleration, and discuss its transformative applications in automated driving.

杨小康教授，上海交通大学，长江学者、国家杰青、IEEE Fellow

Prof. Xiaokang Yang, Shanghai Jiao Tong University, China, IEEE Fellow

杨小康，现任上海交通大学人工智能研究院教授、常务副院长，人工智能教育部重点实验室主任。1994年在厦门大学获理学学士学位。1997年在中科院上海技术物理所获工学硕士学位。2000年在上海交通大学获工学博士学位。2000-2004年在新加坡信息与通信研究院工作。2004年11月回上海交通大学任教至今，期间2007-2008年为德国弗莱堡大学计算机系洪堡学者。主要研究机器学习、计算机视觉、科学智能。主持国家自然科学基金委国家重大科研仪器研制项目、国家重点研发计划课题、上海市人工智能重大专项等项目10项。发表国际学术论文500余篇，引用39000余次，H-index 96，科睿唯安高被引科学家。获国家科技进步二等奖、上海市科技进步一等奖、中国电子学会自然科学一等奖等荣誉。入选长江学者、国家杰青、IEEE Fellow、中国人工智能学会会士（CAAI Fellow）。任中国图象图形学会常务理事、中国人工智能学会元宇宙技术专委会副主任、教育部人工智能科技创新专家组委员。

题目：智能化科学设施的思考与实践

摘要：本报告首先汇报智能化科学设施的思考，智能化科学设施旨在形成科学领域大模型、生成式模拟与反演、自主智能无人实验及大规模可信科研协作等创新功能，构筑数据闭环、模型闭环、操作闭环，加速重大科学发现、变革性物质合成，以及重大工程技术应用；然后，介绍上海交通大学在白玉兰科学大模型、自主无人合成科学平台等若干实践。

Xiaokang Yang is currently a distinguished professor of School of Computer Science, Shanghai Jiao Tong University, Shanghai, China. He received the B. S. degree from Xiamen University, Xiamen, China, in 1994, the M. S. degree from Chinese Academy of Sciences, Shanghai, China, in 1997, and the Ph.D. degree from Shanghai Jiao Tong University, Shanghai, China, in 2000. From September 2000 to March 2002, he worked as a Research Fellow in Centre for Signal Processing, Nanyang Technological University, Singapore. From April 2002 to October 2004, he was a Research Scientist in the Institute for Infocomm Research (I2R), Singapore. From August 2007 to July 2008, he visited the Institute for Computer Science, University of Freiburg, Germany, as an Alexander von Humboldt Research Fellow. He has published over 500 refereed papers, and has filed 60 patents. His current research interests include image processing and communication, computer vision, and machine learning. He received the 2018 Best Paper Award of IEEE Transactions on Multimedia. He was an Associate Editor of IEEE Transactions on Multimedia and Senior Associate Editor of IEEE Signal Processing Letters. He is an IEEE Fellow. 

Title: AI-Enabled Scientific Facility: Concept and Practice

Abstract: This report first introduces the vision of intelligent scientific facilities, which aim to develop innovative capabilities such as large-scale models in scientific fields, generative simulation and inversion, autonomous intelligent unmanned experiments, and large-scale trustworthy research collaboration. These facilities are designed to establish closed-loop data, model, and operational processes, accelerating major scientific discoveries, transformative material synthesis, and significant engineering technology applications. Subsequently, it presents some of Shanghai Jiao Tong University's specific initiatives, including the Yulan Scientific Large Model and the Autonomous Unmanned   Synthesis Science Platform.

肖亮教授，厦门大学，IEEE Fellow

Prof. Liang Xiao,  Xiamen University, China, IEEE Fellow

肖亮：厦门大学教授，入选国家级人才项目，IEEE Fellow，IEEE通信学会杰出讲师，爱思唯尔中国高被引学者。曾担任多个IEEE期刊的编委，包括IEEE TIFS、TCOM、TWC和TDSC等期刊的副编辑，以及IEEE STSP的特邀编辑。主要研究方向为无线安全，隐私保护和无线通信。出版3部英文专著，荣获2024年IEEE通信学会亚太区优秀论文奖，2017年IEEE ICC、2018年IEEE ICCS和2016年IEEE INFOCOM BigSecurity研讨会的最佳论文奖。

题目：面向无人驾驶的车辆协作感知抗数据篡改攻击

摘要：车联网协作感知技术赋能自动驾驶车辆分享点云、图片和行驶状态等感知数据，但在数据篡改攻击下可导致目标虚警和漏报等感知错误。本报告探讨基于强化学习的车辆协作感知方案，提高目标检测任务的感知精度和速度。该方案基于目标空间位置一致性、感知数据分辨率和车辆间信道增益等信息选择协作车辆，最大化感知精度、速度及数据传输最小时延需求加权和的效益函数。此外，介绍了感知数据空间一致性检验机制检测虚假数据，降低如目标位置漏报等感知错误。最后，构建车辆与攻击者之间博弈模型，基于该博弈模型的纳什均衡给出感知精度和速度的性能界，揭示扰动强度、数据量大小及感知数据分辨率对性能的影响机理。

Liang Xiao is an IEEE Fellow and a Professor in the Department of Informatics and Communication Engineering, Xiamen University. She has served in several editorial roles, including an associate editor of IEEE Transactions on Information Forensics & Security, IEEE Transactions on Communication, IEEE Transactions on Wireless Communication and IEEE Transactions on Dependable and Secure Computing, and Guest Editor of IEEE Journal on Selected Topics in Signal Processing. Her research interests include wireless security, privacy protection, and wireless communications. She published three books and three book chapters. She won 2024 IEEE ComSoc Asia-Pacific Outstanding Paper Award, as well as the best paper award for 2017 IEEE ICC, 2018 IEEE ICCS and 2016 IEEE INFOCOM Bigsecurity WS. She was 2022-2023 IEEE ComSoc Distinguished Lecturer.

Title: Collaborative Vehicular Perception for Autonomous Driving Against Data Fabrication Attacks

Abstract: Collaborative vehicular perception enables autonomous connected vehicle (CAV) to share the sensing data such as point clouds, images and driving status, which has performance degradation against data fabrication attacks that share faked sensing data to result in perception errors such as false alarms. In this report, we discuss reinforcement learning-based collaborative vehicular perception scheme to enhance perception accuracy and speed, which chooses collaborative CAVs to enhance the utility as the weighted sum of perception accuracy, speed, and the minimum latency requirement for data sharing. The spatial consistency check is presented to reduce perception errors such as the false positive rate of object locations. The upper performance bound of perception accuracy and speed is provided based on the Nash equilibrium of the game between CAVs and the attacker, revealing the impact of perturbation intensity, data size and point cloud resolution on the perception performance.

田大新教授，北京航空航天大学,长江学者，IEEE Fellow

Prof. Daxin Tian,  Beihang University, China, Changjiang Scholar, IEEE Fellow

田大新：长江学者特聘教授，IEEE Fellow，北京航空航天大学科研院副院长兼前沿创新处处长，“科学探索奖”获得者，国家优青、青年长江、牛顿高级学者、中国工程院首届“中国工程前沿杰出青年学者”，担任中国指挥与控制学会无人系统专委会主任、中国电子学会智能交通信息工程分会副主任、中国计算机学会智能汽车分会副主任、车路协同与安全控制北京市重点实验室主任。发表学术论文100余篇，出版专著7本、教材2本、译著2本，授权发明专利51项；主持国家重点研发计划“揭榜挂帅”重点专项、国家自然基金重点项目等国家项目12项；获国家科技进步奖二等奖等科技奖15项，国家教学成果奖一等奖等教学奖5项。

题目：复杂环境下的全自动驾驶技术

摘要：自动驾驶的终极目标，是让AI系统真正代替人类，实现对复杂环境中驾驶任务的完全自主执行。不同于封闭、可控的任务环境，自动驾驶需持续应对动态、开放、强耦合的交通系统，对其泛化能力、交互理解与自主决策水平提出了更高要求。本报告聚焦车辆自主认知与决策智能，围绕知识获取、联想预判与持续进化三大核心能力，探索融合多模态认知、因果推理与生成学习的端到端自动驾驶系统架构。同时，报告将介绍在多源感知融合、自主决策控制与鲁棒轨迹生成等方面的最新技术进展，为实现面向复杂环境的高智能自动驾驶系统提供思路支撑与方法基础。

Daxin Tian is a distinguished professor and doctoral supervisor, a Changjiang Scholar, and an IEEE Fellow. He currently serves as the Deputy Director of the Research Institute and the Director of the Frontier Innovation Division at Beihang University (BUAA). He is also a recipient of the Scientific Exploration Award, the Excellent Young Scholars Fund from the National Natural Science Foundation, and the Newton Advanced Scholars Fund. Additionally, he has been honored as a Young Changjiang Scholar, an Outstanding Young Scholar in the Frontiers of Chinese Engineering by the Chinese Academy of Engineering. He holds the position of Director at the Beijing Key Laboratory of Vehicle-road Collaborative and Safety Control, as well as the Chairman of the Unmanned Systems Committee of the Chinese Society of Command and Control. Tian has led 12 national-level projects, including the "Challenge-oriented" project of the National Key R&D Program, key projects funded by the National Natural Science Foundation, and international cooperation projects, along with 4 provincial and ministerial-level projects. He has published over 100 academic papers, authored 11 books, and holds more than 50 patents for inventions. He has received 15 awards, including the Second Prize of the National Science and Technology Progress Award.

Title: Fully Autonomous Driving Technology in Complex Environments

Abstract: The ultimate goal of autonomous driving is to enable AI systems to completely replace human drivers in performing driving tasks within complex environments. Unlike closed and controllable task environments, autonomous driving must continuously adapt to dynamic, open-ended, and tightly coupled traffic systems, placing higher demands on generalization capabilities, interaction understanding, and autonomous decision-making levels. This report focuses on vehicle autonomous cognition and decision intelligence, exploring an end-to-end autonomous driving system architecture that integrates multi-modal cognition, causal reasoning, and generative learning, centered around three core capabilities: knowledge acquisition, associative prediction, and continuous evolution. Additionally, the report presents the latest technological advancements in multi-source perception fusion, autonomous decision control, and robust trajectory generation, providing conceptual support and methodological foundations for developing highly intelligent autonomous driving systems capable of operating in complex environments.

汪小帆教授，上海应用技术大学校长，长江学者，国家杰青

Prof. Xiaofan Wang, President of Shanghai Institute of Technology, China

汪小帆：1996年于东南大学获得博士学位。2002年开始在上海交通大学担任教授，曾任上海交通大学电子信息与电气工程学院副院长和致远学院常务副院长、上海大学副校长，现为上海应用技术大学校长。曾获国家杰出青年科学基金（2002），入选教育部长江学者特聘教授（2008）。曾获国家自然科学二等奖（2015）。现任国际自动控制联合会（IFAC）信息物理制造系统协调委员会副主席、中国系统工程学会副理事长等学术职务。主要研究领域为复杂动态网络分析与控制。

题目：具有执行器饱和的多智能体系统协同控制

摘要：多智能体系统协同控制理论为无人系统集群提供了理论基础，实际应用中往往存在的执行器饱和约束对集群的协同性能构成了挑战。本报告系统性地阐述我们多年来在具有执行器饱和约束的多智能体协同控制领域取得的研究成果。报告从三个关键维度构建分析框架与控制机制：对称与非对称饱和约束避免、半全局与全局饱和协同达成、同质与异质饱和处理。这些研究旨在为克服执行器饱和带来的限制、实现高性能无人集群协同提供理论和方法指导。

Xiaofan Wang received the Ph.D. degree from Southeast University, China in 1996. He has been a Professor with Shanghai Jiao Tong University (SJTU) since 2002 and a Distinguished Professor of SJTU since 2008. He was the vice-president of Shanghai University, and is now the president of Shanghai Institute of Technology, China. He received the 2002 National Science Foundation for Distinguished Young Scholars of P. R. China, the 2008 Distinguished Professor of the Chang Jiang Scholars Program, Ministry of Education, and the 2015 Second Class Prize of the State Natural Science Award, China. He is currently vice-chair of IFAC Coordinating Committee CC5, and vice-chair of Systems Engineering Society of China. His current research interests include analysis and control of complex dynamical networks.

Title: Cooperative Control of Multi-Agent Systems with Actuator Saturation

Abstract: The advancement of cooperative control theory for multi-agent systems has established a theoretical foundation for unmanned system swarms. However, inherent physical constraints frequently induce actuator saturation in practical implementations, presenting a barrier to transitioning cooperative control from theory to practice. This report systematically presents our years of research achievements in the field of multi-agent cooperative control subject to actuator saturation constraints. The presentation will construct analytical frameworks and control mechanisms from three critical dimensions: mitigation of symmetric and asymmetric saturation constraints, achievement of semi-global and global saturated coordination, and treatment of homogeneous and heterogeneous saturation. The research outcomes provide theoretical and methodological guidance for overcoming limitations induced by actuator saturation and enabling high-performance cooperative control of unmanned system swarms.

陈谋教授，南京航空航天大学，国家杰青

Prof. Mou Chen, Nanjing University of Aeronautics and Astronautics, China

陈谋，博士，教授、博士生导师，IET Fellow，中国自动化学会会士，享受国务院政府津贴。2018年国家自然科学基金杰出青年基金获得者、2019年国家“百千万”人才工程入选者。先后在南京航空航天大学获学士与博士学位，并先后在英国拉夫堡大学、新加坡国立大学和澳大利亚阿德莱德大学做访问或博士后研究。目前担任SCI收录英文期刊《IEEE Trans. Cybernetic》、《IEEE Trans. Circuits and Systems II: Express Briefs》等编委、担任《中国科学.信息科学》、《自动化学报》、《控制理论与应用》、《航空学报》等编委等。同时担任教育部高等学校教学指导委员会兵器类委员、中国人工智能学会智能空天专业委员会副主任委员、中国指挥与控制学会群集智能与协同控制专业委员会副主任委员、自动化学会信息物理系统控制与决策专业委员会副主任等。先后获国家自然科学二等奖1项(排名第二)、江苏省科学技术奖一等奖1项(排名第一)、江苏省青年杰出贡献奖、国防科技进步二等奖2项(排名第一)，申请授权发明专利40余项。出版中英文专著3部，发表学术论文200余篇。

题目：复杂低空环境下飞行器集群安全控制关键技

摘要：低空经济是新质生产力的代表性产业，而低空飞行器是低空复杂环境下飞行的主要载体，也是低空经济的支柱产业。随着低空经济的快速发展，亟需发展低空复杂环境下的集群飞行控制，但飞行器集群低空空域安全高效运行面临众多挑战。本报告主要介绍低空飞行器集群控制与安全研究意义及必要性，低空集群飞行已有成果和低空控制未来展望等三个方面。

Prof. Mou Chen, an IET Fellow and a CAA Fellow, serves as the Dean of the College of Automation Engineering at Nanjing University of Aeronautics and Astronautics. He was the recipient of the National Science Fund for Distinguished Young Scholars in 2018, was selected into the National “Hundred-Thousand-Ten Thousand Talents Project” in 2019, and was included in the “New Century Excellent Talents Support Program” of the Ministry of Education in 2011. Currently, he serves as an editorial board member of several SCI-indexed English journals, such as IEEE Trans. Cybernetic、IEEE/ASME Trans. Mechatronics、IEEE Trans. CS II: Express Briefs, etc., and also serves as an editorial board member of Chinese journals including Science China: Information Sciences, Acta Aeronautica et Astronautica Sinica, Acta Automatica Sinica, Control Theory & Applications, etc. He has successively won the Second Prize of the National Natural Science Award (ranked second), the First Prize of the Jiangsu Provincial Science and Technology Award (ranked first), the First Jiangsu Provincial Outstanding Contribution Award for Young Scientists and Technologists, the First Prize of the Natural Science Award of the Ministry of Education (ranked second), and 2 Second Prizes of the National Defense Science and Technology Progress Award (ranked first). He has applied for and been authorized more than 30 invention patents. He has published 3 monographs in Chinese and English and has published more than 200 academic papers.

Title: Key Technologies for Safe Control of Aircraft Swarms in Complex Low-Altitude Environments

Abstract: The low-altitude economy is a representative industry of new productive forces, and low-altitude aircraft, as the main carriers for flight in complex low-altitude environments, are also the pillar industry of the low-altitude economy. With the rapid development of the low-altitude economy, there is an urgent need to develop swarm flight control in complex low- ltitude environments. However, the safe and efficient operation of aircraft swarms in low-altitude airspace faces numerous challenges. This report mainly covers three aspects: the research significance and necessity of low-altitude aircraft swarm control and safety, the existing achievements in low-altitude swarm flight, and the future prospects of low-altitude control.

刘新旺教授，国防科技大学，国家杰青

Prof. Xinwang Liu, National Universiy of Defense Technology, China

刘新旺，国防科技大学计算机学院教授、博导。主要研究方向为机器学习、数据挖掘等。国家杰青(2023)、国家优青(2019)获得者。国家自然科学基金委重点项目、科技创新2030重大项目负责人，基金委创新群体A类核心成员。共发表CCF A类期刊/会议论文180余篇，ESI高被引论文17篇，ESI热点论文2篇，谷歌学术引用共2万余次，连续3年入选全球2%顶尖科学奖榜单 (2022-2024)。担任IEEE T-NNLS, IEEE T-CYB等国际顶刊AE, 以及ICML, NeurIPS等国际顶会领域主席。研究成果获中国人工智能学会吴文俊自然科学一等奖(2024)、北京市科技进步一等奖(2024)、湖南省自然科学一等奖 (2014, 2021)、中国图象图形学会自然科学二等奖 (2024)等。

题目：聚类分析的理论方法及应用

摘要：针对聚类分析中数据特征的多样性、非完整性及弱可学习性等挑战，本次报告将介绍课题组在多视图聚类的融合机理、非完整多视图聚类、深度聚类等方面取得了系列性创新成果：建立了如何充分利用不同类型特征来改进聚类性能的新理论、提出了面向聚类填充来应对非完整多视图聚类的新方法、探索了从原始数据中学习特征来最优地服务于聚类的新应用。拟开展持续聚类分析以应对数据的动态变化。

Xinwang Liu is a professor and doctoral supervisor at the School of Computer Science at the National University of Defense Technology. His research focuses on machine learning and data mining. He has been awarded the Natural Science Foundation of China for Excellent Youth in 2019, the Natural Science Foundation of China for Distinguished Young Scholar in 2023. He is the principal investigator of a key project of the National Natural Science Foundation of China and a major project of the Science and Technology Innovation 2030 Program, and a core member of the NSFC's Category A Innovation Cluster. He has published over 180 CCF Category A journal/conference papers, 17 ESI Highly Cited Papers, two ESI Hot Papers, and over 20,000 Google Scholar citations. He has been selected for the top 2% of scientific awards for three consecutive years (2022-2024). He serves as an AE for top international journals such as IEEE T-NNLS and IEEE T-CYB, and as an area chair for top international conferences such as ICML and NeurIPS. His research has been recognized with the Wu Wenjun First Prize in Natural Sciences of the China Association for Artificial Intelligence (2024), the First Prize in Science and Technology Progress of Beijing (2024), the First Prize in Natural Sciences of Hunan Province (2014 and 2021), and the Second Prize in Natural Sciences of the Chinese Society of Image and Graphics (2024).

Title: Theoretical Methods and Applications of Cluster Analysis

Abstract: To address the challenges of data feature diversity, incompleteness, and weak learnability in cluster analysis, this report will introduce a series of innovative achievements made by our research team in multi-view clustering fusion mechanisms, incomplete multi-view clustering, and deep clustering. These include establishing a new theory for fully leveraging different types of features to improve clustering performance, proposing a new cluster-filling-oriented approach to address incomplete multi-view clustering, and exploring new applications for learning features from raw data to optimally serve clustering. We plan to conduct continuous cluster analysis to address dynamic data changes.