Keynote Speakers

Yaochu Jin, Westlake University, China

IEEE Fellow, Member of Academia Europaea

Yaochu Jin (Fellow, IEEE) received the B.Sc., M.Sc., and Ph.D. degrees in automatic control from Zhejiang University, in 1988, 1991 and 1996, respectively, and the Dr.Ing, degree in neuroinformatics from Ruhr University Bochum, Germany in 2001. He joined the Westlake University in October 2023 as a Chair Professor of AI, leading the Trustworthy and General Al Laboratory. He was an Alexander von Humboldt Professor for Al with the Faculty of Technology, Bielefeld University, Germany from 2021 to 2023, and a Distinguished Chair, Professor in Computational Intelligence with the Department of Computer Science, University of Surrey, UK. He was a Finland Distinguished Professor with the University of JyvAskyla, Finland, Changjiang Distinguished Visiting Professor with Northeastern University, China, and Distinguished Visiting Scholar, University of Technology Sydney, Australia. His main research interests include multi-objective and data-driven evolutionary optimization, evolutionary multi-objective learning, trustworthy AI, and evolutionary developmental AI. Professor Jin is presently the President of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He is the recipient of the 2018, 2021 and 2023 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by the Clarivate as a Highly Cited Researcher from 2019 to 2023 consecutively. He is a Member of Academia Europaea and Fellow of IEEE.

(Onsite Talk) Speech Title: Privacy-preserving machine learning in healthcare

Abstract: Machine learning has attracted increased attention in healthcare, including medical imaging analysis, disease management, and resource allocation, among many others. However, most data in healthcare are sensitive, making it challenging to train high-quality machine learning models without privacy leakage. This talk provides a brief overview of privacy-preserving computing techniques, discusses the main challenges, and presents some recent advances. One example of federated medical image synthesis based on deep learning is given. Finally, we conclude the talk with an outline of open challenges.

KWONG Tak Wu Sam, Lingnan University Hong Kong, China

Fellow of Hong Kong AES, US NAI and IEEE

Professor KWONG Sam Tak Wu is the Chair Professor of Computational Intelligence, and concurrently as Associate Vice-President (Strategic Research) of Lingnan University. Professor Kwong is a distinguished scholar in evolutionary computation, artificial intelligence (AI) solutions, and image/video processing, with a strong record of scientific innovations and real-world impacts. Professor Kwong was listed as one of the top 2% of the world’s most cited scientists, according to the Stanford University report. He was listed as one of the most highly cited scientists by Clarivate in 2022 and 2023. He has also been actively engaged in knowledge transfer between academia and industry. He was elevated to IEEE Fellow in 2014 for his contributions to optimization techniques in cybernetics and video coding. He was the President of the IEEE Systems, Man, and Cybernetics Society (SMCS) in 2021-23. Professor Kwong has a prolific publication record with over 350 journal articles, and 160 conference papers with an h-index of 82 based on Google Scholar. He is currently the associate editor of many leading IEEE transaction journals. He is a fellow of the US National Academy of Innovators. and the Hong Kong Academy of Engineering and Sciences.

Speech Title: Creating a Better Future: Harnessing AI for Social and Environmental Responsibility
Abstract: In this talk, I will explore the potential of artificial intelligence (AI) to address some of the most pressing social and environmental challenges facing our world today. With its ability to analyze vast amounts of data, identify patterns, and make predictions, AI has the potential to revolutionize fields such as healthcare, education, and climate science.
However, as AI becomes more powerful and ubiquitous, it is also raising important ethical and social questions. How can we ensure that AI is used for the greater good, rather than contributing to inequality and injustice? How can we ensure that the benefits of AI are shared fairly across society, rather than concentrated among a small group of wealthy individuals and corporations?
In this talk, the speaker will delve into various questions related to AI applications and their positive impact on society and the environment. The talk will draw on examples of specific AI applications that are already making a difference. For instance, the underwater instance segmentation, which is the process of detecting and segmenting objects in underwater images. This technology has the potential to improve underwater exploration, marine conservation, and disaster response efforts.
Another example is image reconstruction based on compressive sensing. This technique allows for the reconstruction of high-quality images from a limited amount of data, which can be particularly useful in applications such as medical imaging or remote sensing. The third topic is the low night image enhancement, which is a technology that enhances images taken in low-light conditions. This can improve the accuracy and effectiveness of applications such as surveillance, transportation safety, and security.
By exploring these and other examples of AI applications, the talk aims to demonstrate the potential of AI to make a positive impact on society and the environment, and to inspire further innovation in
Ultimately, this talk will aim to inspire and empower attendees to think critically about the role of AI in shaping our future, and to explore ways in which they can harness this powerful technology to create a more just, equitable, and sustainable world.

Prof. Jun Li, China University of Geosciences, Wuhan, China

IEEE Fellow

Prof. Jun Li (Fellow, IEEE) received the B.S. degree in geographic information systems from Hunan Normal University, Changsha, China, in 2004, the M.E. degree in remote sensing from Peking University, Beijing, China, in 2007, and the Ph.D. degree in electrical engineering from the Instituto de Telecomunicações, Instituto Superior Técnico (IST), Universidade Técnica de Lisboa, Lisbon, Portugal, in 2011. Currently, she is a Full Professor with Geoscience Uniersity of China (Wuhan), Wuhan, China. She is serving as the Editor in Chief for the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2021-).

Prof. Jinchang Ren, Robert Gordon University, U.K

Jinchang Ren received his B. Eng. degree in computer software, M.Eng. in image processing, and D. Eng. in computer vision, all from Northwestern Polytechnical University, Xi’an, China. He was also awarded a Ph.D. degree in Electronic Imaging and Media Communication from the University of Bradford, Bradford, U.K.
Currently he is a full Professor of Computing Sciences, Transparent Ocean Lead, and Director of the Hyperspectral Imaging Lab at the National Subsea Centre (NSC), Robert Gordon University, Aberdeen, U.K.
Dr. Ren is a Senior Member of IEEE. His research interests focus mainly on hyperspectral imaging, image processing, computer vision, big data analytics and machine learning. He has published 380+ peer reviewed journal/conferences articles, and acts as an Associate Editor for several international journals including IEEE TGRS and J. of the Franklin Institute et al. He has also chaired and co-chaired a number of conferences and workshops. His students have received many awards, including the Best PhD thesis from IET Image and Vision Section and various conferences/workshops.

(Onsite Talk) Speech Title: Advances in Underwater Optical and Sonar Image Enhancement and Quality Assessment

Abstract: In underwater environments, imaging devices with optic sensors face numerous challenges including water turbidity, light attenuation, scattering, and the presence of particles, which collectively degrade image quality, reduce contrast, and distort colours. To mitigate these issues, SOund and NAvigation Ranging (SONAR) sensors are often employed to capture information using sound pulses reflected from the scene. This imaging technique serves as a valuable complement in scenarios with poor lighting conditions. However, sonar images also exhibit limitations such as lower resolution, susceptibility to environmental factors like salinity and temperature, vulnerability to underwater currents and noises from marine life, and the presence of shadow zones, complicating the differentiation between original objects and their associated shadows.
Despite these limitations, the fusion of these two modalities can yield highly informative results. Consequently, researchers have concentrated on developing quality assessment techniques to ensure the acquisition of high-quality data, supplemented by enhancement methods to further refine data quality. Enhancements encompass haze removal, contrast and resolution enhancement, and enhancement of colour distribution in optical images. Additionally, for sonar images, emphasis is placed on noise reduction and shadow removal to improve overall clarity and interpretability.
In this talk, we will discuss the devised quality assessment techniques for both image types utilizing machine learning algorithms trained to correlate images, mapped within a predetermined feature space, with corresponding quality scores. Our experimental findings have demonstrated the effectiveness of these methods, exhibiting strong correlation with human evaluations. Furthermore, to facilitate enhancement, we have introduced innovative deep neural network architectures enriched with attention-driven inception modules and autoencoders. Through experimentation, we have showcased the efficacy of our developed networks in enhancing image quality, mitigating noise, and demonstrating robust generalization capabilities across publicly available datasets. Finally, applications of the developed techniques to tackle real challenges in offshore energy, subsea operations, military and environmental sectors are demonstrated.