Prof. Jian Ma,

City University of Hong Kong, China

Research Areas:Decision and decision support systems; Business intelligence; Research information systems; Research Social Networks; Big Data Research Analytics

Introduction (Click to know more): Jian Ma is a professor in the Department of Information Systems at City University of Hong Kong. He also serves as the director of the Generative Artificial Intelligence for Business Laboratory and the Research Information Management Laboratory. His main research areas include collaborative innovation networks, generative artificial intelligence, and big data research analytics. Professor Ma has published over 200 papers in international refereed journals, with more than 5,300 citations in SCI and an H-index of 33, placing him among the top 2% of highly cited scholars globally.

His research results are widely applied in various institutions, including the National Natural Science Foundation of China, the Ministry of Education, the Guangdong Provincial Department of Science and Technology, and the Hong Kong Innovation and Technology Commission, as well as in several universities, research institutions, and technology enterprises.

Professor Ma is also the founder of IRIS Software (www.IRISaaS.com) and ScholarMate (www.ScholarMate.com), and he established InnoCity Limited (www.InnoCity.com), where he serves as chairman. The company has received angel investment from HK Tech300 and funding from the Hong Kong SAR Government's RAISe+ initiative, with a vision to connect researchers and innovators to share and co-create knowledge, enhancing research success and innovation efficiency.

Speech Title: Human AI collaboration for Knowledge and Business Value Co-creation

Abstract:We present the STIGPT (Science, Technology, and Innovation Generative Pretrained Transformer), designed to enhance human-AI collaboration in co-creating knowledge and business value via the ScholarMate platform. This professional research social network uses generative AI technologies for intelligent writing, checking, and reviewing, effectively connecting university researchers with industry professionals to promote smarter research and collaborative innovation. ScholarMate facilitates comprehensive knowledge creation by seamlessly integrating academic insights with business needs. Through real-world examples and interactive discussions, participants will discover how AI-augmented collaboration improves research efficiency, quality, and commercial impact, ultimately driving data-driven innovation across sectors. Join us to explore the future of smart collaborative innovation in today’s dynamic landscape.

Prof. Jianxi Luo,

City University of Hong Kong, China

Research Areas: Data-Driven Innovation; AI for Design; Intelligent System Design; Design Science; Artificial Intelligence; Vehicles & Mobility Innovation; Technology Entrepreneurship; Technology Policy

Introduction(Click to know more): Jianxi Luo is a Professor of Systems Engineering at City University of Hong Kong. He holds a PhD in Engineering Systems and an MS in Technology Policy from MIT, as well as an MS in Automotive Engineering and a BE in Thermal Engineering from Tsinghua University. Professor Luo is ranked No.1 in Hong Kong and 22nd globally in the field of Design Practice and Management in Stanford‘s list of world's top 2% scientists. Luo's work has been instrumental in establishing the Data-Driven Innovation (DDI) paradigm, developing its foundational theories, methods, and tools. In recognition of his contributions to DDI, he was conferred as a G20 Professor in 2022. Previously, he was Director of the Data-Driven Innovation Lab at Singapore University of Technology and Design (SUTD) from 2012 to 2023, Principal Investigator at the SUTD-MIT International Design Centre from 2013 to 2020, and Director of the SUTD Technology Entrepreneurship Programme from 2019 to 2021. He also served as an adjunct faculty member at Schwarzman College, Tsinghua University from 2022 to 2024, a full-time faculty member at New York University from 2011 to 2012, a visiting scholar at Cambridge University in 2005, and Columbia University from 2011 to 2012. Luo has collaborated with organizations such as World Economic Forum, Environmental Defense Fund, Asian Development Bank, and Toyota USA.

Prof. Weijia Jia,

Beijing Normal University (BNU-Zhuhai), China

Research Areas: Edge computing, artificial intelligence algorithms, sensing of physical objects in the cyber space, construction of human-machine-object integrated knowledge graph and big data processing

Introduction(Click to know more): Professor Weijia Jia is currently the Director of Institute of Artificial Intelligence and Future Networking, and the Director of Super Intelligent Computer Center, Beijing Normal University (BNU, Zhuhai); also a Chair Professor at BNBU, Zhuhai, Guangdong, China. He has served as the VP for Research at BNBU(UIC) in 6/2020-7/2024. Prior joining BNU, he served as the Deputy Director of State Kay Laboratory of Internet of Things for Smart City at the University of Macau and Zhiyuan Chair Professor at Shanghai Jiaotong University, PR China. His contributions have been recoganized for the research of edge AI, optimal network routing and deployment; vertex cover; anycast and multicast protocols; sensors networking; knowledge relation extractions; NLP and intelligent edge computing. He has over 700 publications in the prestige international journals/conferences and research books and book chapters. He has received the best product awards from the International Science & Tech. Expo (Shenzhen) in 2011/2012 and the 1st Prize of Scientific Research Awards from the Ministry of Education of China in 2017 (list 2), and top 2% World Scientists in Stanford-list (2020-2024) and many provincial science and tech awards. He has served as area editor for various prestige international journals, chair and PC member/keynote speaker for many top international conferences. He is the Fellow of IEEE and the Distinguished Member of CCF.

Speech Title: Edge AI for Intelligent Business 

Abstract:Large Language Models (LLMs) are widely used across various domains, but deploying them in cloud data centers often leads to significant response delays and high costs, undermining Quality of Service (QoS) at the network edge. Although caching LLM request results at the edge using vector databases can greatly reduce response times and costs for similar requests, this approach has been overlooked in prior research. To address this, we propose a novel non-invasive RAG approach called Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework that caches LLM request results at the edge using vector databases, thereby reducing response times for subsequent similar requests. Unlike methods that modify LLMs directly, VELO leaves the LLM’s internal structure intact and is applicable to various LLMs. Building on VELO, we formulate the QoS optimization problem as a Markov Decision Process and design an algorithm based on Multi-Agent Reinforcement Learning. Our algorithm employs a diffusion-based policy network to extract the LLM request features, determining whether to request the LLM in the cloud or retrieve results from the edge’s vector database. Implemented in a real edge system, our experimental results demonstrate that VELO significantly enhances user satisfaction by simultaneously reducing delays and resource consumption for edge users of LLMs. We further explore the non-invasive RAG approach to the intelligent business of real estate by introducing a system RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate market. 

Su Jipu,

Product Architect of Tongyi Large Model

Alibaba Group

Introduction: Research Fellow,specializing in Brain-Computer Interfaces, Affective Computing, and the Productization of GenAI. He Holds a Ph.D. in Communication Engineering from Southeast University (No.1 globally in the 2024 Soft Science Rankings). Has held positions at internationally renowned companies such as MiTAC, Huawei, and Alibaba. Currently engaged in model product incubation and commercialization, supporting Alibaba Cloud's nationwide SME and new ecosystem model invocation services. Possesses extensive industry experience in smart cities, education and training, intelligent manufacturing, terminal hardware, cross-border expansion, and industry-academia collaboration. Products designed under their leadership have generated cumulative revenues exceeding 1 billion RMB. Recognized as a Shanghai Chief Technician under the Thousand Talents Plan and a High-Skilled Leading Talent in Zhejiang Province. Over 10 policy recommendations in the field of AI large models have been adopted by the Shanghai Municipal Committee of the Chinese People's Political Consultative Conference (CPPCC) and the National CPPCC.

Speech Title: Generative AI: Technological Trends, Path to Productization, and Business Value

Abstract:Large language models are profoundly reshaping industries, making their productization and commercialization a key focus for businesses. Drawing from an industry-frontline perspective, this presentation will offer a deep dive into the latest GenAI technological advancements for 2025 and its future landscape, exploring the core challenges and opportunities in product implementation. The session aims to provide strategic and architectural guidance for decision-makers and developers, clarifying the roadmap for large language model adoption, enabling them to identify opportunities while navigating pitfalls, and ultimately gain a competitive edge in the AI era.