S.Y. Kung/贡三元 教授

Princeton University/美国普林斯顿大学

课程题目/Course Topic: The Golden Age of AI – Devices, Data, and Deep Learning/人工智能的黄金时代——设备、数据和深度学习

Abstract: The rapid advancement of AI is propelled by three ``D"-pillars: (1) Devices, enabled by resilient VLSI technology; (2) Data, driven by large-scale LLMs and big data; and (3) Deep Learning, powered by transformative neural architectures like transformers (NN3.0).  This talk begins by tracing the evolution of neural networks—from MLP (NN1.0) and CNN (NN2.0) to today’s transformer-based models—highlighting how AI2.0 distills vast data into knowledge (D2K). Yet, while AI excels in specific tasks, achieving Artificial General Intelligence (AGI) demands a paradigm shift—integrating domain knowledge for short-term gains and ultimately enabling logical reasoning, scientific inference, and knowledge-to-knowledge (K2K) learning for long-term breakthroughs.  Our second theme is focused on  Regressive Component Analysis (RCA), a novel framework unifying subspace learning and supervision.  With over 25 billion connected devices,  our identity, habits, health, and other personal data may be inadvertently leaked or illegally hacked without.  As such, it is of paramount importance to address the challenging issue of online utility-privacy tradeoffs.  To this end, we demonstrate how RCA theoretically enables a novel privacy paradigm named ``compressive privacy” as a potential safeguard for online data in this treacherous era of AI. Finally, we highlight the emergence of AI-Mathematics (AIM)—a discipline that will prove fundamental to advancing intelligent systems, and our unequivocal theme is -  in short - “Math is the Cornerstone of AI”.

课程摘要:人工智能的快速发展由三大“D”支柱推动：(1) 设备，由弹性 VLSI 技术赋能；(2) 数据，由大规模 LLM 和大数据驱动；(3) 深度学习，由 Transformer（NN3.0）等变革性神经架构驱动。本次演讲首先回顾了神经网络的演进——从 MLP（NN1.0）和 CNN（NN2.0）到如今基于 Transformer 的模型——重点介绍了 AI2.0 如何将海量数据提炼为知识（D2K）。然而，尽管人工智能在特定任务上表现出色，但实现通用人工智能 (AGI) 需要范式转变——整合领域知识以获得短期收益，并最终实现逻辑推理、科学推理和知识到知识 (K2K) 学习，以实现长期突破。我们的第二个主题聚焦于回归成分分析 (RCA)，这是一个统一子空间学习和监督学习的新型框架。随着超过 250 亿台联网设备，我们的身份、习惯、健康和其他个人数据可能会在无意中泄露或被非法窃取。因此，解决在线效用与隐私权衡这一棘手问题至关重要。为此，我们展示了 RCA 如何在理论上实现一种名为“压缩隐私”的新型隐私范式，作为人工智能时代在线数据的潜在保障。最后，我们重点介绍了人工智能数学（AIM）的兴起——这门学科将成为推进智能系统发展的基础，我们的主题明确——简而言之——“数学是人工智能的基石”。

个人简介：普林斯顿大学电子工程专业教授，1977年获得斯坦福大学电子工程博士学位，曾任美国南加州大学电子工程系统专业任职教授；他是斯坦福大学，代尔夫特大学，早稻田大学访问教授；中国科技大学荣誉教授，香港理工大学荣誉讲座教授。他的研究领域包括，机器学习，计算机视觉, 超大规模集成电路阵列处理，系统建模与识别，人工神经网络，无线传输，传感器阵列处理，多媒体信号处理，生物信息数据挖掘与识别。贡三元教授曾发表400余篇（部）论文及专著。包括：“大规模集成电路和现代信号处理”（英文版及俄文版）；“大规模集成电路处理器”（英文版、俄文版及中文版）；“数字神经网络”（英文版）；“主成分神经网络”（英文版）；“生物信息认证：一种现代方法”（英文版）。贡三元教授从1988年开始为Fellow of IEEE，IEEE Signal Processing Society委员会委员(1989-1991)。是IEEE Signal Processing Society一些技术委员会的发起人，包括大规模集成电路信号处理技术委员会，神经网络信号处理技术委员会，多媒体信号处理委员会。贡三元教授为IEEE Transactions on Signal Processing副主编， Journal of VLSI Signal Processing Systems主编。鉴于贡三元教授的学术贡献，其获得IEEE Signal Processing Society技术进步奖，杰出讲座学者；最佳论文，IEEE千禧年勋章等。

CV： S.Y. Kung is a professor of electronic engineering at Princeton University. He received his PhD in electronic engineering from Stanford University in 1977. He has served as a professor of electronic engineering systems at the University of Southern California. He is a visiting professor at Stanford University, Delft University, and Waseda University; Honorary Professor at the University of Science and Technology of China, and Honorary Professor at the Hong Kong Polytechnic University. His research areas include machine learning, computer vision, ultra-large-scale integrated circuit array processing, system modelling and recognition, artificial neural networks, wireless transmission, sensor array processing, multimedia signal processing, and biological information data mining and recognition. Professor S.Y. Kung has published more than 400 papers and monographs. Including: "large-scale integrated circuits and modern signal processing" (English and Russian versions); "large-scale integrated circuit processors" (English, Russian and Chinese versions); "digital neural networks" (English version); "primary component neural networks" (English version); "biological information authentication: a modern method" (English version). Professor S.Y. Kung has been a member of the IEEE Fellow Committee (1989-1991). He is the initiator of some technical committees of the IEEE Signal Processing Society, including the Technical Committee on Large-scale Integrated Circuit Signal Processing, the Technical Committee on Neural Network Signal Processing, and the Multimedia Signal Processing Committee. Professor Gong Sanyuan is the deputy editor-in-chief of IEEE Transactions on Signal Processing and the editor-in-chief of Journal of VLSI Signal Processing Systems. Professor S.Y. Kung's academic contributions have earned him the IEEE Signal Processing Society Technology Progress Award, the title of Outstanding Lecturer, the Best Paper award, and the IEEE Millennium Medal, among other honours.

陈志远/Zhiyuan Chen 教授

诺丁汉大学马来西亚分校/ University of Nottingham Malaysia

课程主题 / Course Topic: 现代模式识别技术与方法/ Modern Technologies and Methods in Pattern Recognition

课程摘要：本短期课程（总时长8小时，含6小时理论授课与2小时实验操作）系统而精要地介绍了现代模式识别技术方法体系，重点涵盖监督式与非监督式学习两大技术路径。课程将从线性回归与分类的基础理论切入，深入解析非监督学习算法的核心原理及工程实践应用，特别设置实验环节帮助学员通过实操巩固理论认知。适合希望系统提升模式识别专业能力的学生与从业者，实现理论与应用能力的协同培养。

Abstract: This short course provides a concise yet comprehensive introduction to modern pattern recognition technologies and methods, with a focus on both supervised and unsupervised learning techniques. Participants will explore foundational concepts in linear regression and classification and gain insights into unsupervised learning algorithms and their practical applications. The course also includes a hands-on lab session to reinforce theoretical concepts through practical experience. Designed for students and professionals seeking to strengthen their understanding of pattern recognition, this course offers a balanced blend of theory and application over a total of 8 hours (6 hours of lectures and 2 hours of lab work).

个人简介：陈志远教授现任诺丁汉大学马来西亚分校计算机科学学院院长兼博士生导师，自2022年7月起获任英国高等教育学会资深会士（Senior Fellow），同时为英国计算机学会（BCS）专业会员。曾担任马来西亚加速技术实验室（MIMOS）、电信研发中心（TM R&D）、未来作物研究中心及Tentacle科技公司等政府机构、科研院所与企业的首席顾问。她于2007年与2011年先后获得诺丁汉大学计算机科学哲学硕士与博士学位，加入现职前曾任职英国"地平线"数字经济研究院研究员。其研究领域涵盖计算机科学、机器学习、数据挖掘、用户建模与人工智能，相关成果已发表多篇高水平论文，并长期担任计算机科学与工程领域国际会议的咨询委员会主席、主旨报告人及分会主席。2012年获中国教育部"海外高层次人才"认定。陈教授迄今指导逾百名硕士生，培养6名博士毕业生，现有10名在读博士生。主持马来西亚科技创新部资助的4项电子科学研究计划、高等教育部1项基础研究基金（FRGS）项目，以及20余项企业合作课题，发表60余篇高质量学术论文，其中最高被引论文引用次数超过300次。

CV: Professor Chen Zhiyuan is currently the dean and doctoral supervisor of the School of Computer Science at the University of Nottingham, Malaysia. He has been appointed as Senior Fellow of the British Higher Education Society since July 2022 and is also a professional member of the British Computer Society (BCS). He has served as chief consultant to government agencies, research institutes and enterprises such as Malaysia Acceleration Technology Laboratory (MIMOS), Telecom R&D Center (TM R&D), Future Crop Research Center and Tentacle Technology Corporation. She received her Master of Philosophy and PhD in Computer Science from the University of Nottingham in 2007 and 2011. Before joining her current position, she worked as a researcher at the "Horizon" Digital Economy Research Institute in the UK. Its research fields cover computer science, machine learning, data mining, user modelling and artificial intelligence. It has published many high-level papers on related achievements. She has served as the chairman of the Advisory Committee, Keynote Speaker and Chapter Chairman of the International Conference on Computer Science and Engineering for a long time. In 2012, it was recognised by the Ministry of Education of China as an "Overseas High-level Talent". Professor Chen has so far coached more than 100 master's students, trained 6 doctoral graduates, and currently has 10 doctoral students studying. He presided over four electronic science research projects funded by the Ministry of Science and Technology Innovation in Malaysia, one basic research fund (FRGS) project of the Ministry of Higher Education, and more than 20 corporate cooperation projects, and published more than 60 high-quality academic papers, among which the highest number of cited papers has been cited more than 300 times.

Hugo Gamboa 教授

葡萄牙新里斯本大学 / Universidade Nova de Lisboa

课程主题 / Course Topic: 生物信号解析/ Making sense from biosignals

课程摘要：从生物信号的采集、处理、特征提取到解析的完整视角，我们将简要介绍最常见的人体生物信号来源—包括心脏、大脑、双手、肌肉、肺部、声音等部位产生的信号。通过具体案例，我们将展示如何从生物信号中创造知识与应用价值。针对多元生物信号，还将演示如何从行为表达的生物信号中提取身份特征、位置信息、活动状态及性格特质。整个演讲将重点展示由葡萄牙新里斯本大学与PLUX公司、弗劳恩霍夫研究所合作开展的研究案例。

Abstract: From the perspective of collection, processing, feature extraction, and making sense of biosignals, we will briefly cover the most commonly used biosignals extracted from our heart, brain, hands, muscles, lungs, and voice, among others. We will give examples and show opportunities for creating knowledge and applications from biosignals. Also, with multivariate biosignals examples, we will showcase the extraction of identity, location, activity and personality from the behaviour expressed in biosignals. Throughout the presentation, we will showcase research examples conducted at Nova University of Lisbon in collaboration with PLUX and Fraunhofer.

个人简介：Hugo Gamboa现任葡萄牙新里斯本大学科技学院物理系正教授，并担任LIBPHYS研究中心主任，南京信息工程大学人工智能学院（未来技术学院）兼职教授。他毕业于里斯本大学高等技术学院，获电气与计算机工程博士学位。作为弗劳恩霍夫葡萄牙分会资深科学家，他主导里斯本办公室智能系统研究组的工作。Gamboa教授是PLUX无线医疗传感器公司的创始人和董事长，这家科技创新型企业专注于微电子技术、生物信号处理及软件开发领域。他在LIBPHYS研究中心领导的研究团队在医疗仪器研发、生物信号处理及机器学习在生物信号中的应用方面具有深厚造诣。学术成果方面，甘博亚教授已发表：60余篇期刊论文、15篇专著章节、10本精选论文集（最佳论文选编）以及110篇会议论文。现为IEEE高级会员。

CV： Hugo Gamboa is a Full Professor at the Physics Department of the Nova School of Science and Technology of the Universidade Nova de Lisboa and Director of LIBPHYS. He is an Adjunct Professor at the School of Artificial Intelligence, Nanjing University of Information Science and Technology. He got his PhD in Electrical and Computer Engineering from the Instituto Superior Técnico, University of Lisbon. As a Senior Scientist at Fraunhofer Portugal, coordinates the Lisbon Office research group, which focuses on Intelligent Systems. He is the founder and President of PLUX, a technology-based innovative startup in the field of wireless medical sensors, focused on microelectronics, biosignal processing and software development. He leads a research team on LIBPHYS with expertise on medical instrumentation, biosignal processing and machine learning applied to biosignals. Published more than 60 Journal Papers; 15 Book Chapters; 10 books (selected best papers); 110 Conference Papers. He is an IEEE Senior member.

Ana Rita Londral 教授

葡萄牙新里斯本大学/NOVA Univeristy of Lisbon

课程主题 / Course Topic: 开发改善患者疗效与医疗服务的技术/ Developing technology that improves patients' outcomes and healthcare services

摘要：本课程将重点阐述开发能提升患者疗效与医疗服务质量的关键技术需求—这一需求在创新程度高、人口老龄化显著且医疗体系可持续性面临挑战的社会中显得尤为迫切。演讲首先将以"价值医疗联合实验室"（Value-based Healthcare）理念作为研究课题的立论基础，继而展示系列研究项目，包括：（1）应用可穿戴技术与便携式医疗设备提升临床护理水平；（2）通过医疗数据智能分析优化服务供给效率。

Abstract: This course will focus on the need to develop technology that improves patient outcomes and healthcare services, especially in societies with high innovation, ageing populations, and challenges to healthcare sustainability. The topic of value-based healthcare will be introduced as the motivation for the research topics presented. Research projects will be presented in which wearable technologies and portable medical devices are used to enhance patient care, and healthcare data is leveraged to deliver more efficient services.  

个人简介：Ana Rita Londral教授毕业于里斯本大学，获电气与计算机工程学士及生物医学科学博士学位（神经科学方向），现任新里斯本大学助理教授、健康价值协同实验室（Value for Health CoLAB）主任，并担任综合健康研究中心资深研究员。她在医疗工程、数字健康和人工智能医疗创新领域拥有逾20年研发经验，专注于开发临床决策支持系统所需的数字工具与预测分析技术，在智能患者监护、疾病进展建模及数字健康技术与临床流程融合方面取得重要突破。作为核心成员参与20余项国内外研发项目（含多项欧盟资助计划），其研究成果已成功转化为实际医疗应用，并系统评估了技术的可扩展性与临床价值。

CV: Ana Londral studied Electrical and Computer Engineering and holds a PhD in Biomedical Sciences with a specialization in Neurosciences from the University of Lisbon. She is an Assistant Professor at Nova University of Lisbon and directs a collaborative laboratory, Value for Health CoLAB. She is also an integrated researcher at the Comprehensive Health Research Centre. She brings over 20 years of experience in medical engineering, digital health, and AI-driven healthcare innovation. Her work focuses on developing and implementing digital tools and predictive analytics for clinical decision support, with significant contributions to AI-powered patient monitoring, disease progression modelling, and the integration of digital health technologies into clinical workflows. She has joined more than 20 national and international R&D projects, including several EU-funded initiatives, translating technology research into practical healthcare applications and assessing their scalability and impact.

Giovanni Saggio 教授

意大利罗马第二大学 / University of Rome Tor Vergata

课程主题 / Course Topic: 数字孪生：挑战、机遇与局限 / Digital Twins: Challenges, Opportunities, Limits

摘要：当前先进技术已能够实现长期、持续地获取海量数据。这使得我们得以战略性地运用这些必要且关键的数据要素，对从简单到高度复杂的系统进行全面表征。通过对特定对象（包括物品、组件、系统等）进行长期数据采集与特征提取，我们能够构建该对象的虚拟副本，即数字孪生体（Digital Twin，DT）。当数字孪生技术与机器学习、人工智能等智能算法相结合时，便催生了智能数字孪生（Intelligent DT，IDT）。目前，数字孪生与智能数字孪生已成功应用于电子、机械、化学等领域，但相关技术远未发挥其全部潜力。

Abstract: Current advanced technologies have made it possible to acquire massive amounts of data over a long period of time. This allows us to strategically use these necessary and critical data elements to fully characterize systems from simple to highly complex. By collecting and extracting features from a specific object (including items, components, systems, etc.) over a long period of time, we can build a virtual copy of the object, namely a digital twin (DT). When digital twin technology is combined with intelligent algorithms such as machine learning and artificial intelligence, intelligent digital twins (IDT) are born. At present, digital twins and intelligent digital twins have been successfully applied in electronics, machinery, chemistry and other fields, but the relevant technologies are far from realizing their full potential.

个人简介：Giovanni Saggio，电子工程学士（1991年），微电子与通信学博士（1996年），现任意大利罗马第二大学副教授。他连续四年（2020-2021、2021-2022、2022-2023、2023-2024）入选斯坦福大学全球前2%顶尖科学家榜单，并被系统与信息控制通信技术研究院（INSTICC）授予"杰出研究员"称号。2020至2022年间，他担任欧盟委员会"预防性医疗"领域国际数字健康老龄化转型专家组（IDIH）协调员，该国际合作项目涵盖日本、韩国、美国、中国和加拿大。萨乔教授著有电子学领域多部专著及九部教科书，是CRC出版社的特邀作者，发表学术论文200余篇，担任多家期刊的专题编辑/客座编辑/学术编辑及编委会成员，持有十二项专利，并创立了三家衍生企业：Captiks有限公司、Seeti有限公司和Voicewise有限公司。

CV: Giovanni Saggio graduated in Electronic Engineering (1991), PhD in Microelectronics and Telecommunication (1996), currently Associate Professor at the University of Vergata, Rome, Italy. He is in the TOP 2% most influential Scientists (Stanford University ranking, 2020-21, 2021-22, 2022-23, 2023-24) and appointed Distinguished Researcher (Institute for Systems and Technologies of Information, Control and Communication, INSTICC). He was the coordinator of the International “Expert Group” IDIH (International Collaboration Digital Transformation Healthy Ageing), for the European Commission, involving Japan, South Korea, USA, China, Canada) on “Preventive Care” (2020-2022). He is author of several monographs and nine books on Electronics, is featured author for CRC Press, author/coauthor of 200+ publications, Topic/Guest/Academic Editor and Section Board/Editorial Board Member for different Journals, granted twelve patents and founded three Spinoffs: Captiks Srl, Seeti Srl, Voicewise Srl.

周挥宇/Huiyu Zhou 教授

英国莱斯特大学/University of Leicester

课程主题1 / Course Topic 1: 计算机视觉与大语言模型入门/ Introduction to Computer Vision and Large Language Model

摘要：计算机视觉旨在开发具备人类视觉系统能力的计算机系统，主要解决数字图像的获取与解析问题。该领域深度整合了数字图像处理、人工智能、计算机图形学和心理学等多学科知识。本课程将探讨当前实际计算机视觉系统中应用的基础原理与技术，以及新系统的研发方法，重点涵盖图像处理与大语言模型的基本原理和技术理解，并培养计算机视觉软件设计与实现的实践能力。

Abstract: Computer vision is the development of a computer-based system with the capability of the human vision system. It is mainly concerned with the problem of capturing and making sense of digital images. The study draws heavily on many subjects, including digital image processing, artificial intelligence, computer graphics and psychology. This course will explore some of the basic principles and techniques from these areas currently used in real-world computer vision systems and the research and development of new systems. The objectives are to develop an understanding of the basic principles and techniques of image processing and large language models and to develop skills in the design and implementation of computer vision software.

课程主题2 / Course Topic 2: 如何向IEEE会刊投稿 / Writing papers for IEEE

摘要：科睿唯安的期刊引文报告 (JCR) 每年都会评估学术研究出版物的影响力和影响力。统计数据显示，IEEE 期刊在引用排名方面继续位居各自领域的前列。向 IEEE Transactions和 Journals 提交稿件表明该研究工作具有一定的优点。在这次演讲中，周教授首先介绍一下自己的研究团队和背景。其次，周教授将解释为什么在 IEEE Transactions 和 Journals 上发表论文。接下来周教授会介绍论文被拒的主要原因，并进一步分析论文质量。最后周教授会用一些例子来讨论一下质量要求.

Abstract: Every year, Clarivate's Journal Citation Reports (JCR) evaluates the impact and influence of academic research publications. Statistical data show that IEEE journals continue to rank among the top in their respective fields in terms of citations. Submitting a manuscript to IEEE Transactions and Journals indicates that the research work has a certain level of merit. In this presentation, Professor Zhou will first introduce his research team and background. Next, he will explain why publishing in IEEE Transactions and Journals is advantageous. Professor Zhou will then discuss the main reasons for paper rejections and further analyze paper quality. Finally, he will use several examples to illustrate the key quality requirements.

个人简介：周挥宇教授先后获得中国华中科技大学无线电技术专业工学学士学位、英国邓迪大学生物医学工程理学硕士学位，以及英国爱丁堡赫瑞瓦特大学计算机视觉哲学博士学位。现任英国莱斯特大学计算与数理科学学院正教授，迄今已在相关领域发表同行评审论文600余篇。周教授担任《电气与电子工程新进展》主编，同时出任《IEEE人机系统汇刊》《IEEE生物医学与健康信息学杂志》《模式识别》《PeerJ计算机科学》及《IEEE Access》副主编，并担任机器人顶级会议ICRA、人工智能顶会IJCAI和英国机器视觉会议BMVC的区域主席。其研究项目获得英国工程与物理科学研究理事会(EPSRC)、医学研究理事会(MRC)、欧盟委员会、英国皇家学会、利华休姆信托基金、帕芬信托基金、英国阿尔茨海默症研究中心、北爱尔兰投资局及多家企业的持续资助。个人主页: https://le.ac.uk/people/huiyu-zhou

CV： Prof. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China and a Master of Science degree in Biomedical Engineering from the University of Dundee in the United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou is currently a full Professor at the School of Computing and Mathematical Sciences, University of Leicester, United Kingdom. He has published over 600 peer-reviewed papers in the field. Dr. Zhou serves as the Editor-in-Chief of Recent Advances in Electrical & Electronic Engineering and Associate Editor of "IEEE Transactions on Human-Machine Systems", “IEEE Journal of Biomedical and Health Informatics”, “Pattern Recognition”, “PeerJ Computer Science” and “IEEE Access”, and Area Chair of ICRA, IJCAI and BMVC. His research work has been or is being supported by UK EPSRC, MRC, EU, Royal Society, Leverhulme Trust, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry. Homepage: https://le.ac.uk/people/huiyu-zhou