Prof. Liansheng Wang
Xiamen University, China
Bio: Wang Liansheng is currently a professor at the School of Information at Xiamen University, a dual-appointed professor at the Department of Gastroenterology at the School of Medicine, a doctoral supervisor, the deputy director of the Digital Fujian Health and Medical Big Data Research Institute, and the deputy leader of the AI group of the Radiology Branch of the Fujian Medical Association, Vice Chairman of MICS. He has been engaged in medical image processing research for a long time. He has hosted and participated in a number of scientific research projects, including the National Natural Science Foundation of China Instrument Project, the Ministry of Science and Technology’s Scientific and Technological Innovation 2030, the National Key R&D Project, the National Natural Science Foundation of China and Youth Projects, etc. His publications include Nature Machine Intelligence , Nature Communications, Cell Patterns, TMI, MedIA, CVPR/AAAI and other related research papers, and won the Tencent Rhinoceros Research Award, CSPE Young Investigator, the second prize of the Fujian Province Science and Technology Progress Award, and the 2023 Xiamen University Field Award The first prize of the Zhaowu Interdisciplinary Award, and led the team to win the championship in international medical imaging competitions 11 times.
Speech Title: AI for pathology
Abstract: Artificial intelligence (AI) has emerged as a powerful tool in pathology, revolutionizing disease diagnosis and patient care. This talk explores the transformative potential of AI in pathology and its applications across various aspects of the field. From image analysis to predictive modeling, AI algorithms are augmenting pathologists' abilities, improving diagnostic accuracy, and enhancing patient outcomes. This talk highlights key areas where AI is making significant contributions in pathology, including image analysis for automated detection and classification of tissue abnormalities, predictive modeling for risk stratification and treatment planning, digital pathology for remote access and collaboration, and quality assurance processes. The talk emphasizes the potential of AI to revolutionize pathology practice, reduce inter-observer variability, and promote personalized medicine. By harnessing the power of AI, pathology stands poised to enter a new era of precision diagnostics and improved patient care.
Prof. Chengchao Liang
Chongqing University of Posts and Telecommunications, China
Bio: Prof. Chengchao Liang received a Ph.D. in Electrical and Computer Engineering from Carleton University, Canada in 2017 awarded the Senate Medal. He is currently a Professor with the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, China. Prior to this, He was a cross-appointed Postdoctoral Fellow in the Department of Systems and Computer Engineering, Carleton University as well as Huawei Ottawa R & D Centre from 2017 to 2019. He is in the Editorial Boards of EURASIP Journal on Wireless Communications and Networking, Transactions on Emerging Telecommunications Technologies, and Computer Communications. He has served as the reviewer and the TPC member for many IEEE journals and conferences. His research interests include wireless communications, satellite networks, Internet protocols and optimization theory.
Speech Title: When Satellite Meets 6G Networks: From Integration to Intelligent Optimization
Abstract:The integration of 'space-air-ground' and 'intelligence' are prominent features of Non-Terrestrial Networks (NTN) in B5G/6G. This report aims to outline the integrated approach of space-air-ground integrated information networks in various dimensions such as system, protocol, network architecture, services, and terminals. We will first discuss the evolution and basic framework of the integrated space-air-ground network, which aims to build an integrated information network in the sky and on the ground, ensuring seamless, all-weather coverage in global three-dimensional space, thereby enabling users to access on demand at any time and place. Next, this report will explore how to deeply integrate space-based and air-based networks with terrestrial mobile communication networks and integrated computing power through technologies like artificial intelligence, thereby enhancing the speed, service quality (QoS), and stability of the integrated network, offering users a continuous and reliable communication experience. Finally, the report will preliminarily explore LLM integration and control in the future integrated networks.
Assoc. Prof. Nianyin Zeng
Xiamen University, China
Bio: Dr. Nianyin Zeng is currently an Associate Professor with the Department of Instrumental and Electrical Engineering of Xiamen University. He received the B.Eng. degree in electrical engineering and automation in 2008 and the Ph.D. degree in electrical engineering in 2013, both from Fuzhou University. From October 2012 to March 2013, he was a RA in the Department of Electrical and Electronic Engineering, the University of Hong Kong. From September 2017 to August 2018, he was an ISEF Fellow founded by the Korea Foundation for Advance Studies and also a Visiting Professor at the Korea Advanced Institute of Science and Technology (KAIST).
His current research interests include intelligent data analysis, machine learning and computer vision, computational intelligent, system modeling and applications. He has published more than 80 research papers in referred journal and conferences as well as 13 patents. By Google Scholar, his publications have been cited by more than 7,000 times with H-Index 34.
Dr. Zeng is currently serving or has served as an Associate Editor for Neurocomputing Frontiers in Medical Technology, and Evolutionary Intelligence, an Editorial Board member for Computers in Biology and Medicine, Biomedical Engineering Online, Mathematical Problems in Engineering, Journal of Electronics & Information Technology, and Journal of Image and Graphics, and also Guest Editors for Frontiers in Neuroscience and Frontiers in Medical Technology. He has also served as program committee members for a number of premier international conferences related to intelligent data analysis, including EAI ICMTEL, ACAIT, ICCSE, etc.
Dr. Zeng has received a number of prestigious awards, including High-Level Talent of Fujian Province, Key-Talent of Xiamen City, Nanqiang Young-Top-Talent of Xiamen University, three Provincial Natural Science Awards, Invention and Entrepreneurship Achievement Award by the China Invention Association, and Distinguished Reviewers of Computers in Biology and Medicine, Neurocomputing, and Chinese Journal of Scientific Instrument.
Speech Title: Evolutionary transfer optimization methods for solving the dynamic multi-objective optimization problems
Abstract: Developing evolutionary transfer optimization(ETO)-based methods has become a novel trend in solving the dynamic multi-objective optimization problems, which dedicate to fully using the history searching experiences so as to accelerate the convergence in new environment. However, those ETO-based methods always suffer the challenge of negative transfer, and to overcome this issue, a hierarchical response system and a multi-strategy adaptive selection mechanism have been proposed, which alleviate the negative transfer from the aspects of "when to transfer" and "how to transfer", respectively. In particular, the former quantifies the environmental changes to make response, and the latter introduces the kernel mean matching method to match the data distribution so as to promote effective knowledge transfer. This report will give in-depth discussions on the motivation and effectiveness of the proposed methodologies, which inspires more thoughts on how to overcome the bottleneck of negative transfer.
Assoc. Prof. Ata Jahangir Moshayedi
Jiangxi University of Science and Technology, China
Bio: Dr Ata Jahangir Moshayedi, Associate professor at Jiangxi University of Science and Technology, China, PhD. In Electronic Science from Savitribai Phule Pune University, India. IEEE member, Instrument Society of India as a Life Member, Lifetime Member of Speed Society of India, member of the editorial team of various conferences and journals like; International Journal of Robotics and Control, JSME, Bulletin of Electrical Engineering and Informatics, International Journal of Physics and Robotics Applied Electronics, etc., 80 papers published in national journals and conferences, 3 books published, Owns 2 patent, 9 copyright. His research interest includes: Robotics and Automation/ Sensor modelling /Bio-inspired robot, Mobile Robot Olfaction/Plume Tracking, Embedded Systems / Machin vision-based Systems/Virtual reality, Machine vision/Artificial Intelligence.
Speech Title: Visionary Integration: Enhancing AGVs with Vision Systems and Machine Perception
Abstract: Service robots represent a transformative application of robotics that profoundly impacts human life, spanning domains from healthcare to industry. These robots serve as lifesavers and support systems, alleviating humans from strenuous tasks and repetitive work that might compromise accuracy in job execution. According to ISO 8373:2012, service robots encompass two main types: personal service robots, designed for use outside manufacturing, and professional service robots, catering to non-commercial and commercial purposes. These robots operate on a spectrum from semi-autonomous to fully autonomous, gradually gaining acceptance as invaluable human assistants across diverse applications and professions. Industries are increasingly integrating service robots into their production lines, marking a pivotal shift within the context of the industrial revolutions. The first revolution brought mechanization, followed by the second revolution powered by electricity. Industry 4.0, however, intertwines digital and internet technologies, propelling further innovation and evolution in the realm of technology. Within this discourse, the focus narrows to AGV (Automated Guided Vehicles) and MIR (Mobile Industrial Robots) as exemplary service robots. The discussion delves into the modeling steps and simulation processes involved in their creation. Additionally, it scrutinizes the performance of designed AGVs employing various algorithms. This analysis aims to serve as a guide for researchers, offering insights and practical implementations for diverse control systems within modeled systems.
Inviting & Updating*