Manchester Metropolitan University, UK | L.Han@mmu.ac.uk
Bio: Prof. Han is currently a full Professor of Computer Science at the Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University. Prof. Han is Academic Director for Centre for Digital Data Research, Faculty Lead for AI, Digital and Cyber Physical Systems and Deputy Director of ManMet Crime and Well-Being Big Data Centre. Prof. Han's research areas mainly lie in the development of novel big data analytics/Machine Learning/AI, and development of novel intelligent architectures that facilitates big data analytics (e.g., parallel and distributed computing, Cloud/Service-oriented computing/data intensive computing) as well as applications in different domains (e.g. Precision Agriculture, Health, Smart Cities, Cyber Security, Energy, etc.) As a Principal Investigator (PI) or Co-PI, Prof. Han has a proven track record of successfully leading multi-million-pound projects on both national and international scales (supported by diverse funding sources: UKRI, NIHR, GCRF/Newton, EU, Industry, and Charity) and has extensive research and practical experiences in developing intelligent data driven AI solutions for various application domains (e.g. Health, Food, Smart Cities, Energy, Cyber Security) using various large datasets (e.g. images, numerical values, sensors, geo-spatial data, web pages/texts). Prof. Han has served as an associate editor/a guest editor for a number of reputable international journals and a chair (or Co-Chair) for organisation of a number of international conferences/workshops in the field. She has been invited to give a number of keynotes and talks on different occasions (including international conferences, national and international institutions/organisations). Prof. Han is a member of EPSRC Peer Review College, an independent expert of European Commission for proposal evaluation/mid-term project review, and serves on assessment and panels for UKRI (EPSRC, BBSRC, Innovate UK, MRC etc.) and the British Council.
Speech Title: Meeting Societal Challenges: Scalable Big Data-driven, AI-enabled Approaches
Abstract: This talk will present our latest advances in scalable AI and big data learning, spanning fundamental methodological development and real-world application. Through case studies across domains such as health, it will demonstrate how our scalable AI solutions can support better decision-making, enable innovation and deliver meaningful public benefit.
Qilu University of Technology, China
Bio: Hongjiao Guan is currently an Associate Professor at Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences). She received her Ph.D. degree in Computer Science and Technology from Harbin Institute of Technology. Her research focuses on machine learning and natural language processing, with current primary interests covering medical information processing, medical large language models, and affective computing. She serves as a committee member of YSSNLP and Affective Computing under the Chinese Information Processing Society (CIPS) of China. She has led and participated six projects, including the Youth Program of the National Natural Science Foundation of China and Youth Project of the Shandong Provincial Natural Science Foundation. She has published 15 SCI-indexed and CCF papers as the first or corresponding author, and held over 10 authorized invention patents as the first inventor.
Shenzhen University, China
Bio: Youneng Bao is an Assistant Professor at the College of Electronics and Information Engineering, Shenzhen University. He received his Ph.D. in Information and Communication Engineering from Harbin Institute of Technology and was a postdoctoral researcher at City University of Hong Kong. His research focuses on intelligent media compression and efficient computing, including learned image and video compression, lightweight neural models, robust coding, and deployment-friendly optimization for ultra-high-definition video, live streaming, VR/AR, and immersive media. He has published papers in leading journals and conferences, including ICCV, AAAI, IEEE T-CSVT, Signal Processing.
Kamla Nehru Institute of Technology (KNIT), India | n_badal@hotmail.com
Bio: Dr. Neelendra Badal is a Professor in the Department of Computer Science & Engineering at Kamla Nehru Institute of Technology, (KNIT), at Sultanpur (U.P.), INDIA of Dr. A.P.J. Abdul Kalam Technical University (AKTU), UP Lucknow INDIA (Formerly Uttar Pradesh Technical University, (UPTU), Lucknow (On-Leave). And presently Director of Rajkiya Engineering College, Bijnor. He received B.E. (1997) from Bundelkhand Institute of Technology (BIET), Jhansi (U.P.), INDIA, in Computer Science & Engineering, M.E. (2001) in Communication, Control and Networking from Madhav Institute of Technology and Science (MITS), Gwalior (M.P.), INDIA and PhD (2009) in Computer Science & Engineering from Motilal Nehru National Institute of Technology (MNNIT), Allahabad (U.P.), INDIA. He is Chartered Engineering (CE) from Institution of Engineers (IE), India. He is a Senior Member of IEEE, Member of ACM and Life Member of IE, IETE, ISTE, CSI, India, IoTSocietyofIndia. He has published more than 100 papers in International/National Journals, conferences and seminars. His research interests are Distributed System, Parallel Processing, GIS, Data Warehouse & Data mining, Software engineering, Networking, IoT and Data Analytics.
Speech Title: Future of Visual Intelligence in the Era of Generative AI
Abstract: Visual Intelligence has emerged as one of the most advanced transformative domains of Artificial Intelligence, enabling machines to perceive, interpret, and interact with the visual world in increasingly sophisticated manners. The recent rise of Generative AI has accelerated this transformation by introducing powerful models capable of creating, understanding, and reasoning about visual content with unprecedented accuracy and creativity. In the era of Generative AI, the evolving landscape of Visual Intelligence highlighting how foundation models, vision-language architectures, and multimodal learning are redefining traditional computer vision paradigms. The discussion will focus on the transition from task-specific systems to generalized visual intelligence capable of image generation, scene understanding, visual question answering, content synthesis, and autonomous decision-making. The objective of talk will examine emerging applications across healthcare, smart cities, autonomous systems, education, agriculture, and industrial automation. This demonstrating how generative visual models are creating new opportunities for innovation and societal impact. It will also address critical challenges related to explainability, scalability, biasness, privacy, security, computational sustainability, and ethical deployment of AI-driven visual systems. The aim of the talk is to provide the insights into how Generative AI is shaping the next generation of intelligent visual systems and driving the future of digital transformation to the researchers, academicians, industry professionals, and policymakers by presenting recent advances, future research directions, and global technological trends.