| Prof. Hongbo JiangHunan University, China (Member of Academia Europaea, IET/BCS/AAIA Fellow) BIO: Hongbo Jiang is currently a Distinguished Professor and Vice Dean of the College of Computer Science and Electronic Engineering (CSEE), Hunan University. He is also the director for the trusted systems and networking key laboratory of Hunan Province, the director of Hunan international technical cooperation base of high performance computing and applications. Prior to Hunan University, he was a tenured professor at Huazhong University of Science and Technology. He received his Ph.D. in Computer Science from Department of Electrical Engineering and Computer Science working with Prof. Shudong Jin, Case Western Reserve University. He ever interned at IBM T. J. Watson Research Center, as well as AT&T Labs Research. He was a Hong Kong Scholar Research Fellow at The Chinese University of Hong Kong, working with Prof. John C. S. Lui. Hongbo has been serving in the editorial board of IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, ACM Transactions on Sensor Networks, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology, IEEE Internet of Things Journal, IEEE Communications Magazine, ACM/Springer Wireless Networks, Wiley Security and Communication Networks, International Journal of Ad Hoc and Sensor Wireless Networks. He has also been guest editors for IEEE Transactions on Industrial Informatics, Springer Mobile Networks and Applications. He is an elected Member of Academia Europaea (MAE), Fellow of IET (The Institution of Engineering and Technology), Fellow of BCS (The British Computer Society), Fellow of AAIA (The Asia-Pacific Artificial Intelligence Association), Fellow of AIIA (The International Artificial Intelligence Industry Alliance), Senior Member of ACM, Senior Member of IEEE, Distinguished Member of CCF, and Full Member of IFIP TC6 WG6.2. Now his research focuses on computer networking, especially, wireless networks, and mobile computing. Here are some ongoing projects:
The Academic Genealogy: Hongbo Jiang (Case Western Reserve University, 2008) --> Shudong Jin (Boston University, 2003) --> Azer Bestavros (Harvard University, 1992) --> Thomas Edward Cheatham (Purdue, 1954), one of the "roots" of the academic genealogy of applied computer scientists. Speech Title: To be Updated Abstract: To be Updated |
| Prof. Pietro S. OlivetoSouthern University of Science and Technology, China BIO: Pietro S. Oliveto is a Professor of Computer Science at the Southern University of Science and Technology (SUSTech) Shenzhen, China. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. He has been EPSRC PhD+ Fellow (2009-2010) and EPSRC Postdoctoral Fellow (2010-2013) at the University of Birmingham, UK and Vice-Chancellor's Fellow (2013-2016) and EPSRC Early Career Fellow (2015-2020) at the University of Sheffield, UK. Before moving to SUSTech he was Chair in Algorithms at the Department of Computer Science, University of Sheffield, UK. His main research interest is the performance analysis, in particular the time complexity, of bio-inspired computation techniques including evolutionary algorithms, genetic programming, artificial immune systems, hyper-heuristics and algorithm configurators. He is currently building a Theory of Artificial Intelligence Lab at SUSTech. Speech Title: Computational Complexity Analysis of Sexual Evolution for the Design of Better General Purpose Algorithms for AI Abstract: Large classes of the general-purpose optimisation algorithms at the heart of modern artificial intelligence and machine learning technologies are inspired by models of Darwinian evolution. In this talk we show how the foundational computational complexity analysis of such algorithms leads to an understanding of their behaviour and performance. Such understanding in turn allows informed decisions on how to set their many parameters and how to improve the algorithms to allow for the obtainment of better solutions in shorter time. We provide two concrete examples of how such analyses can lead to counter intuitive insights into how to design sexual evolution inspired algorithms (using populations and recombination) and how to set their parameters such that they can considerably outperform their single trajectory and mutation only (asexual) counterparts at hillclimbing unimodal functions, and at escaping from local optima. We conclude the talk by presenting experimental results that confirm the superiority of the designed algorithms that was proven for benchmark functions with significant structures, for classical combinatorial optimisation problems with practical applications. |
| Prof. Azhar ImranBeijing University of Technology, China BIO: Dr. Azhar Imran is an Associate Professor at the Department of Computer Science, Beijing University of Technology, China. With over 13 years of academic experience, he specializes in Artificial Intelligence, Data Science, and Machine Learning, and has made significant contributions in areas such as Natural Language Processing (NLP), Generative Adversarial Networks (GANs), Computer Vision, and Cyber Intelligence. Dr. Imran has published over 85 research articles and has been invited to deliver keynote speeches at prestigious conferences, including ICBDDM-24, ICCTEC, CCET25, CVIT-24, ICDSP24, and CVIT-23. As a Senior Member of IEEE, Dr. Imran is also an active member of various academic and professional committees and serves on the editorial boards of multiple high-impact journals. Throughout his career, he has been recognized with several awards, including the Outstanding Graduate Award and Best Researcher Award from Beijing University of Technology, as well as the Embassy Honored Award from the Pakistan Embassy in Beijing. Dr. Imran’s research continues to bridge the gap between academic advancements and real-world AI applications, particularly in the fields of healthcare and cybersecurity. His work focuses on using AI-driven solutions to enhance systems such as fall detection for elderly care, medical image processing for diagnosis improvement, and cyber intelligence for better security frameworks. 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. |
| Prof.JongMyoung KimSehan University, Korean BIO: JongMyoung Kim is a Professor in the Department of AI Big Data at Sehan University, South Korea. His primary research interests include Artificial Intelligence, Big Data Analytics, Deep Learning applications, and AI education. He is currently focusing on developing automated ESG evaluation models using machine learning techniques and is leading research and curriculum development on Generative AI and digital content creation. Speech Title: Deep Learning Based ESG Evaluation Automation Model and Inter-Company Comparison Study Abstract: As the importance of Environmental, Social, and Governance (ESG) management grows, traditional manual evaluation methods face challenges regarding efficiency and accuracy when processing large-scale data. This presentation introduces a Deep Learning-based automation model for ESG evaluation. The proposed model aims to automatically extract key ESG indicators by analyzing unstructured big data publicly available from companies and facilitates objective comparisons between firms. By applying state-of-the-art Natural Language Processing (NLP) techniques and deep neural networks, this study enhances evaluation precision and explores the model's potential industrial applications and scalability within a big data environment. |
