Xiamen University, China

Nianyin Zeng was born in Fujian Province, China, in 1986. 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 as an ISEF Fellow founded by the Korea Foundation for Advance Studies and also a Visiting Professor at the Korea Advance Institute of Science and Technology.

Currently, he is a Professor with the Department of Instrumental & Electrical Engineering of Xiamen University. His current research interests include intelligent data analysis, computational intelligent, time-series modeling and applications. He is the author or co-author of several technical papers and also a very active reviewer for many international journals and conferences.

Prof. Zeng is currently serving as Associate Editors for Neurocomputing, Evolutionary Intelligence, and Frontiers in Medical Technology, and also Editorial Board members for Computers in Biology and Medicine, Biomedical Engineering Online, and Mathematical Problems in Engineering.

Speech Title: Research on Intelligent Visual Defect Detection Methods for Aero-engine Core Components under Complex Conditions

Abstract: Aero-engines, as highly complex precision systems, can suffer catastrophic consequences from minute defects in critical components like turbine and compressor blades. Traditional inspection methods often rely on manual expertise, suffering from low efficiency and high rates of missed/false detection. In contrast, AI-driven inspection offers non-contact operation, high precision, and automation potential, presenting a key solution to these bottlenecks. This report analyzes core scientific challenges and solutions for intelligent defect detection in aero-engines under complex and dynamic scenarios: (1) Addressing Data Limitations: Analyzing methods to process and enhance low-quality defect images captured in extreme environments, mitigating noise and overcoming insufficient labeled samples; (2) Overcoming Algorithmic Bottlenecks: Solving how to extract discriminative defect-related features from high-dimensional visual data to distinguish subtle, complex, and obscured defect features from complex background; (3) Resolving Deployment Constraints: Exploring lightweight, high-precision, low-latency defect detection under limited computational resources to achieve efficient performance trade-offs for in-line aero-engine quality inspection. Furthermore, the report introduces emerging technologies, discussing their industry-academia-research collaboration for intelligent aviation equipment maintenance.

Sun Yat-sen University, China

Dr. Han HUANG is a professor and doctoral supervisor in the School of Software Engineering, Sun Yat-sen University. He is currently serving as an associate editor of IEEE Transactions on Emerging Topics in Computational Intelligence and Complex & Intelligent Systems (IF: 6.5), and Deputy Director at the Key Laboratory of Big Data and Intelligent Robotics under the Ministry of Education, Director of the Software Engineering Committee of the Guangdong Computer Society. He is a Distinguished Member of CCF and a Senior Member of IEEE. Prof. Huang has received the National Excellent Teacher Award commemorating the 20th anniversary of the establishment of National Exemplary Software Schools. He has led over 20 national and provincial-level projects, including the National Natural Science Foundation Major Research Program, Key R&D Projects of the Ministry of Science and Technology, General Projects of the National Natural Science Foundation, and the Guangdong Outstanding Youth Fund. He has published three Chinese academic books and two textbooks with National First-Class Presses, as well as two English books with internationally renowned publishers. As the first author or corresponding author, He has also published more than 80 papers in IEEE TPARMI, EEE TCYB, IEEE TSE, IEEE TEVC, IEEE TIP, IEEE TFS, and Science China, including ESI highly cited papers. Prof. Huang has 63 invention patents granted in China and eight invention patents granted in the United States as the first inventor. He has received the China Patent Excellence Award, the Second Prize of Guangdong Provincial Science and Technology Progress Award (Rank 1). As the primary contributor, he led the formulation of the national group standard "White-Box Testing Standard Without Source Code." His research team possesses internationally recognized third-party testing qualifications, including CMA, CNAS, and ISTQB. Prof. Huang has long been dedicated to research on intelligent algorithm theory, applications, and industrial ecosystems. He has publicly released six software systems and has completed nearly 100 algorithm application cases. His implemented "AI Empowering All Industries" initiative has already achieved over 300 cases, serving more than 600 organizations and over 200,000 individual users.

Speech Title: Micro-cost Computing：An Artificial Intelligence Approach for Low-cost and High-efficiency Applications

Abstract: This report starts from the essence of artificial intelligence, elaborates on the core of the three key elements of artificial intelligence, and based on the latest trends in artificial intelligence, takes the improvement of algorithm efficiency and algorithm design as the entry points to introduce micro-computing, a low-cost and high-efficiency artificial intelligence method.

micro-cost computing includes micro-scale searching and micro-cost learning, which target the two fundamental problems in artificial intelligence: optimization and classification, respectively. micro-scale searching is an interpretable stochastic heuristic optimization algorithm. The report introduces the application of this method to various complex mixed-integer programming problems, such as constrained multi-objective optimization, filter parameter tuning, computer vision, and large-scale two-layer spanning tree path planning. It also highlights its achievements in software engineering, national strategic communication applications, and energy systems. The report focuses on micro-cost learning, a machine learning approach characterized by low-cost training, low-cost training data, and small-scale networks. The applications of micro-cost learning in computer vision and large language models are presented, together with its practical agent-based applications in medical, business, and educational scenarios.

Guangzhou University,China

Hongliang Dai is currently a Professor at the Department of Statistics, Guangzhou University, a Distinguished Professor of "Guangzhou Scholar", a Doctoral Supervisor, a Postdoctoral Supervisor, and the Program Director of Data Science and Big Data Technology. He obtained his master's degree in Applied Mathematics from Wuhan University in 2003 and his doctoral degree in Applied Mathematics from Sun Yat-sen University in June 2013. In 2015, he was awarded the title of Professor in Artificial Intelligence. 

He joined the School of Mathematics and Computational Science at Guangdong University of Finance and Economics in July 2003, and transferred to the Department of Statistics, School of Economics and Statistics at Guangzhou University in April 2017. He is an "Outstanding Middle-aged and Young Key Teacher" of the second batch at Guangdong University of Finance and Economics, and a university-level cultivation candidate of the sixth batch of the "Qianbai Shi Project" in Guangdong Province.

He is a Member of IEEE, ACM, and CCF; a Standing Director of the Branch of Statistics History and Culture under the Chinese Association for Applied Statistics; a Director of the Branch of Data Science and Artificial Intelligence under the Chinese Association for Applied Statistics; a Director of the Branch of Educational Statistics and Management under the Chinese Association for Applied Statistics; a Standing Director of the Guangdong Society for Computational Mathematics; and a Member of the Big Data Professional Committee of the Guangdong Computer Society.

Speech Title: 

Generative Intelligence for Industrial Big Data: From Adaptive Clustering to Rectified Flow-Based Fault Diagnosis

Abstract: 

In the era of Industry 4.0, industrial big data is characterized by high dimensionality, severe class imbalance, and complex spatiotemporal dependencies, posing significant challenges to traditional analytical services. This keynote presents a comprehensive framework of "Generative Intelligence" designed to robustly serve industrial decision-making. First, we introduce an Adaptive Multiple Kernel Clustering with Low-Rank Representation (AMKC-LRR), which automatically learns kernel correlations without predefined metrics, significantly enhancing clustering performance on diverse high-dimensional data. Second, addressing the critical scarcity of fault samples, we propose two novel generative paradigms: a Deep Long-Range Spatiotemporal Dependency SMOTE (DLRSD-SMOTE) for high-fidelity sample synthesis, and a pioneering Spatiotemporal-Conditional Information Fusion Rectified Flow (SCIF-CFGRF) model. The latter marks the first application of Rectified Flow in bearing fault diagnosis, achieving inference speeds 15x faster than diffusion models while maintaining superior fidelity. Validated on real-world industrial datasets, this integrated approach demonstrates how adaptive learning and efficient generative models can transform raw industrial data into reliable intelligent services, offering a new roadmap for robust data-driven maintenance systems.

Assoc. Prof. Dr. Azhar Imran Mudassir

Beijing University of Technology, China

Azhar Imran Mudassir received his PhD in Software Engineering from Beijing University of Technology, China, and his Master’s degree in Computer Science from the University of Sargodha, Pakistan. He is currently an Associate Professor in Computer Science, with research and teaching focused on machine learning and deep learning–based image analysis.

Dr. Mudasir has over 13 years of national and international academic experience. His research interests include medical image analysis, machine learning, deep learning, explainable AI, healthcare informatics, and social media analytics. He has published more than 100 research articles in well-reputed international journals and conferences and actively serves as an editorial board member and reviewer for several SCI- and Scopus-indexed journals, including IEEE Access and MDPI journals. He is a regular member of IEEE and has contributed to numerous international conferences as a keynote speaker, invited speaker, session chair, and technical committee member.

Speech Title: Computational Intelligence in Healthcare: Navigating hope vs hype in China 

Abstract: Computational Intelligence (CI) has emerged as a transformative force in healthcare, promising unprecedented advances in diagnosis, treatment, and personalized medicine. Techniques such as machine learning, deep learning, natural language processing, and evolutionary algorithms are redefining how clinicians interpret medical data and make decisions. However, alongside the optimism lies considerable hype exaggerated claims, ethical concerns, data biases, and limited clinical validation that often hinder real-world impact. This speech, Computational Intelligence in Healthcare: Hope vs. Hype, explores the fine balance between technological promise and practical limitations. It highlights success stories in predictive diagnostics, drug discovery, and medical imaging while critically addressing challenges related to data quality, model interpretability, regulatory compliance, and patient trust. The discussion aims to separate genuine innovation from inflated expectations, urging researchers and policymakers to adopt a responsible, evidence-driven approach to integrating CI into healthcare systems. Ultimately, the talk emphasizes that the true hope of computational intelligence lies not in replacing clinicians but in empowering them through transparent, ethical, and human-centered AI.