Speakers

Yiguang Liu

Sichuan University

Prof. Yiguang Liu, Changjiang Distinguished Professor at Sichuan University, serves as Chief Scientist for Aerospace Information Detection & Intelligent Perception Platform, and sole expert at “National High-end Aviation Equipment Innovation Center”. He holds academic committee positions in multiple provincial defense key laboratories and national engineering centers. Currently serving as Executive Council Member of China Society of Image & Graphics, he co-chaired CCIG2022 and has delivered 20+ keynote speeches at major forums including CAE's Tri-Service Aviation Summit.

With academic credentials spanning Nanjing University of Science & Technology (B.S. 1995), Peking University (M.S. 1998), Sichuan University (Ph.D. 2004), and postdoctoral training at National University of Singapore (2008), he conducted visiting research at Imperial College London (2011) and Michigan State University (2013).

His research integrates modern mathematics with next-gen detection technologies for aerospace applications, particularly in dynamic complex environments. Developed systems have been implemented in long-endurance UAV platforms. He has led over 10 national projects including NSFC key programs and major special projects, publishing 100+ SCI papers in IEEE Trans./CVPR/ICCV, etc. Holder of 26 patents and 18 software copyrights. Awards include the First Prize of Sichuan Science and Technology Progress Award (1/10) and Second Prize of Sichuan Natural Science Award (1/5), plus two IEEE ICME paper awards.

Title: Detection Perception and Adversarial Intelligence----Reflections on Airborne Unmanned Systems

Abstract: Enabling "stable-accurate-rapid" signal detection and intelligent sensing via optoelectronic/infrared/SAR sensors is fundamental for adversarial unmanned systems. This presentation reviews the state-of-the-art of global adversarial airborne unmanned systems, identifies limitations of big-data deep learning, and analyzes the essence of adversarial intelligence. Based on these insights, proposes requirements for future intelligent systems tailored to adversarial scenarios. Finally, preliminary research progress from our team is presented.

Kay Chen Tan

Hong Kong Polytechnic University

Kay Chen Tan is a Chair Professor (Computational Intelligence) of the Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University. He has co-authored eight books and published over 300 peer-reviewed journal articles. Prof. Tan was the Vice-President (Publications) of the IEEE Computational Intelligence Society from 2021-2024. He was the Editor-in-Chief of IEEE Transactions on Evolutionary Computation from 2015-2020 and IEEE Computational Intelligence Magazine from 2010-2013. Prof. Tan is an IEEE Fellow and an Honorary Professor at the University of Nottingham in the UK. He is also the Chief Co-Editor of the Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications.

Title: Towards Automated Learning and Optimization in the Era of Large Models

Abstract: LLMs are emerging as catalysts for automated learning and optimization. This talk first examines the applications of LLMs in automated learning, covering topics such as algorithm selection, neural architecture search, and the merging of heterogeneous models. It then discusses the role of LLMs in addressing complex optimization problems, ranging from leveraging LLMs and their multimodal capabilities for automated optimization, to enabling the automated design of optimization algorithms, and further advancing more adaptive pretraining approaches. This talk highlights the potential of LLMs in empowering next-generation intelligent systems.

Hamido FUJITA

Malaysia-Japan International Institute of Technology (MJIIT)

Universiti Teknologi Malaysia (UTM)

Hamido FUJITA is Professor at Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia (UTM), and head of i-kouza (SEiR-Software Engineering and industrial Revolution) Kuala Lumpur Malaysia. He is also Distinguished Professor of Iwate Prefectural University, Iwate Japan. He is also Research Professor at the Faculty of Science, University of Hradec Kralove, Hradec Kralove 50003, Czech Republic, and Research Professor at University of Granada (Spain), and HUTECH University Vietnam; Expert Excellence Professor at Shanghai University of Medicine & Health Sciences, and Foreign Expert at Hunan University, China. He is currently the Executive Chairman of i-SOMET Incorporated Association, Japan. He is Highly Cited Researcher in Cross-Field for the year 2019 and 2020, 2021, 2022, 2023, 2024 in Computer Science field, respectively from Clarivate Analytics. He is Editor-in-Chief of Applied Intelligence (Springer), Editor-in-Chief of Healthcare Management (Tayler&Francis), and Editor-in-Chief of Knowledge-Based Systems (Elsevier) (2010-2020) and Emeritus Editor of Knowledge-Based Systems, also he is Area-Editor of Array journal (Elsevier).

Title: Views on Artificial Intelligence and Machine Learning perspectives

Abstract: The hot topics in training in Machine Learning is a crucial aspect that affects the credibility of the system in terms of performance and is employed for robust applications such as in healthcare systems. Machines or algorithms, in wide challengeable applications in security or vision or health care early predictions, learn from data. Nevertheless, in most cases, the extensive and unbalanced data and noise make it unreliable in prediction accuracy. Supervised machine learning is and was one of the aspects of providing artificial intelligence-based solutions. However, this is and was limited due to the difficulty of labeling big data and many crucial problems in weak relations and noise in data. Semi-supervised learning, for example, Multiview learning, could assist in solving these problems. In many published research, there are still problems in providing machine learning models that are unbiased and efficient in terms of robustness and resilience in data-driven systems. Multiclass classification still has problems in terms of clear definition in class classification, bias, imbalance and weak relations, making machine learning for multiclass classification insecure for classification or regression analytics. This causes limitations in applying such technology in medical applications and diagnosis prediction. In this lecture, I will outline these problems in our one-class classification project. These are related to providing more robust accuracy prediction with some uncertainty that can help us have more accurate classification and prediction. We have applied such findings in health care for heart sickness and seizure early prediction.

We also have deep learning models, which also have challenges related to evidential deep learning and fairness relative to data. There are important issues in expanding research in evidential deep learning, in which uncertainty prediction of variational Auto encoders can provide decisions on evidential distribution, which in turn helps to provide a measure of uncertainty in decision.

We currently have a research project titled “Healthcare Risk Prediction on Data Streams Employing Signal Transformation Network (OCSTN)”, which is supported by grant from Japan Science Promotion Society (JSPS). In this project, we have employed one-class classification deep neural network for health care prediction. In this lecture I will outline of these perspectives and discuss challenging trends.

Zhigeng Pan

Nanjing University of Information Science and Technology

Prof. Zhigeng Pan became a full professor in Zhejiang University in 1996 because of his excellent academic performance. He has published more than 200 technical papers on important journals (such as PAMI, TVCG, IEEE Multimedia) and conferences (such as ACM Multimedia, IEEE VR, ICCV, et al ). He is a member of IEEE, ACM SIGGRAPH. His research interests include virtual reality, computer graphics and HCI. Currently, he is the Editor-in-Chief of international journal Metaverse. Main research fields include VR/AR, HCI，Virtual Human, and Metaverse. The applications are focusing in the fields of education, medical care, culture and sight-seeing. He has got two national level awards and six other awards.

Title: AI-enabled Metaverse and its Creative Applications

Abstract: In 2021，Metaverse became a hot topic around the world, and 2021 is recognized as the meta year of Metaverse. This talk will introduce the research results in the field of Metaterse enabled by intelligence techniques. Metaverse is an integrated techniques including block-chain，interaction, graphics/game，artificial intelligence，network/computing power，Internet of Things）. This talk discusses these techniques briefly, then introduce some typical applications （education metaverse，culture metaverse，military metaverse and medical metaverse）. Key techniques are explained, especially the engaged AI techniques. Finally the future research directions are discussed.

Weisheng Dong

Xidian University

Weisheng Dong is currently a professor and vice dean of the school of AI of Xidian University, and The Yangtze River Scholar of Ministry of Education of China. His main research interests include low-level vision, deep learning and vision understanding. He has published more than 170 papers and got more than 13000 Google citations. He has been an AE of IEEE T-IP and currently an AE of SIAM J. on Imaging Sciences.

Title: Low-quality Image Restoration and Recognition

Abstract: Recovering a high-quality image from its low-quality counterpart is an ill-posed inverse problem. To facilitate motion deblurring, we proposed a parametric blur kernel estimation method to further improve image deblurring performance. For light-weight image super-resolution tasks, a binary deep network was proposed. We also investigated generative image prior for image super-resolution. In this talk, I will also introduce some of our recent progresses in high-level tasks for low-quality images.

Jijun Zhao

South China Normal University

Professor Zhao Jijun is a Changjiang Chair professor at the School of Physics, South China Normal University, and a member of the Discipline Evaluation Group of the State Council Academic Degrees Committee of China. His main research areas include the artificial intelligence-enabled materials design and atomic-level manufacturing. He has published over 800 SCI papers, which have been cited more than 30,000 times. He was ranked 215th in the field of applied physics of on the list of Stanford/Elsevier Top 2% Scientists for career since 2019.

Title: Materials Simulation and Design based on Artificial Intelligence

Abstract: In recent years, artificial intelligence has developed rapidly and has made a significant impact on the field of computational materials science. In this report, several examples of our research team’s application of artificial intelligence (including genetic algorithms, machine learning, etc.) to the calculation of low-dimensional and bulk materials will be discussed. The topics include the search for the ground state structure of Ag clusters using a genetic algorithm combined with a graph attention network, the growth of MoS2/WS2 homojunctions and heterojunctions simulated by an exponential graph neural network potential, the screening of two-dimensional organic magnetic anisotropic materials by a transition metal interconnection neural network, and the design of high-entropy vacancy-ordered double perovskite photovoltaic materials by a graph neural network. Finally, the challenges, opportunities, and future development of this emerging field will be discussed.

Speakers

Email： xb@cqut.edu.cn Tel： 023-62561406

Add: 69 Hongguang Dadao, Banan District, Chongqing 400050

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