| Professor Anthony (Tony) CohnFREng, FLSW, CEng, CITP, FAAAI, FEurAI, FAISB, FAAIA, FAIIA, FIET, FBCS University of Leeds, UK Bio: Anthony (Tony) Cohn is Professor of Automated Reasoning in the School of Computer Science, University of Leeds. His current research interests range from theoretical work on spatial calculi (receiving a KR test-of-time classic paper award in 2020) and spatial ontologies, to cognitive vision, modelling spatial information in the hippocampus, and Decision Support Systems, particularly for the built environment, as well as robotics. He is Foundation Models lead at the Alan Turing Institute where he is conducting research on evaluating the capabilities of large language models, in particular with respect to commonsense reasoning, and is also a co-investigator on a project combining LLMs and probabilistic answer set programming. He is Editor-in-Chief of Spatial Cognition and Computation and was previously Editor-in-chief of the AI journal. He has previously been President of IJCAI, EurAI, KR inc, and AISB. He is the recipient of the 2021 Herbert A Simon Cognitive Systems Prize, and is also (uniquely) the recipient of Distinguished Service Awards from the three main international AI societies: IJCAI, AAAI and EurAI, as well as from KR Inc. He is a Fellow of the Royal Academy of Engineering, the Learned Society of Wales, the AI societies AAAI, AISB, EurAI and AAIA, as well as the CORE Academy (International Core Academy of Sciences and Humanities) and the International AI Industry Alliance. Title: Can Large Language Models Reason about Spatial Information? Abstract: Spatial reasoning is a core component of an agent’s ability to operate in or reason about the physical world. LLMs are widely promoted as having abilities to reason about a wide variety of domains, including commonsense. In this talk I will discuss the ability of state-of-the-art but disembodied LLMs to perform commonsense reasoning, particularly with regard to spatial information. Across a wide range of LLMs, although they show abilities rather better than chance, they still struggle with many questions and tasks, for example when reasoning about directions, or topological relations. |
 | Professor Andrew LimSouthwest Jiaotong University, China Bio: Dr. Andrew Lim is an internationally acclaimed researcher and leader in intelligent systems and supply chain innovation—a field central to global stability and growth. He has held professorships at premier universities across Asia, and his honors include the Returning Singaporean Scientists Award and recognition as a National High-Level Talent of China. He is ranked among the world’s top 2% most impactful scientists. Currently, as a professor at Southwest Jiaotong University, he directs two prominent research initiatives: the Sustainable Intelligent Transportation National Lab and the Intelligent-Safety Digital Transportation & Traffic Lab (a Sichuan-Chongqing Joint Key Laboratory). His influential work, featured in leading academic journals, has been translated into advanced solutions for Fortune 500 firms—driving billions of dollars in savings and earning global innovation accolades. Serving as the founding director of the ASEAN Applied Research Centre and a principal consultant to global industry leaders—whose combined annual revenues exceed $1 trillion—Dr. Lim bridges academic research with large-scale industry implementation. His career continues to shape how intelligent systems are engineered, deployed, and scaled to strengthen the world’s most critical supply chains. Title: From Theory to Transformation: AI, Optimization & Simulation in Modern Supply Chains Abstract: In today's interconnected global economy, supply chains are the vital arteries of commerce, resilience, and competitive advantage. This presentation explores how the strategic convergence of cutting-edge technologies—from AI-driven analytics and advanced simulation to cross-disciplinary systems optimization—can address and overcome persistent and emerging supply chain challenges once deemed insurmountable. Drawing on real-world deployments in the semiconductor industry, we will demonstrate how these innovations are not merely theoretical concepts but practical tools, successfully implemented in high-stakes environments to achieve scalable efficiency gains and build the robust, adaptive supply networks of tomorrow. |
 | Professor Yu ZhengIEEE Fellow JD.COM, China Bio: Dr. Yu Zheng is the Vice President and Chief Data Scientist of JD.COM, and the president of JD Intelligent Cities Research. Before Joining JD.COM, he was a senior research manager at Microsoft Research. He is also a chair professor at Shanghai Jiao Tong University and an adjunct professor at Hong Kong University of Science and Technology. Zheng had published over 200 quality papers at prestigious conferences and journals and received over 6,4000 citations (H-index 114). He founded the research field of urban computing, which had been widely followed by world-class scientists. His monograph published by MIT Press becomes the first text book of this field. He was the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology (2015-2021) and had served as the program co-chair of ICDE 2014 and CIKM 2017. He was a keynote speaker of AAAI 2019, KDD 2019 Plenary Keynote Panel and IJCAI 2019 Industrial Days. He received SIGKDD Test-of-Time Award twice (in 2023 and 2024) and SIGSPATIAL 10-Year-Impact Award four times (in 2019, 2020, 2022, and 2024). He was named one of the Top Innovators under 35 by MIT Technology Review (TR35), an ACM Distinguished Scientist (2016) and an IEEE Fellow (2020), for his contributions to spatio-temporal data mining and urban computing. After joining JD.COM, he has served over 70 cities with his technology, generating a revenue over 1 billion USD. Title: Urban Computing: Enabling Spatio-temporal Intelligences in Cities Abstract: Urban computing creates a data-centric computing framework, which connects urban sensing, urban data management, urban data analytics and providing services into a recurrent process to unlock the power of urban big data (particularly spatial and spatio-temporal data), for an unobtrusive and continuous improvement of people’s lives, city operation systems, and the environment. This talk will present unique properties of spatio-temporal data and the framework that can enable spatio-temporal intelligences. In each layer of urban computing, we will discuss its key research challenges, such as capturing spatio-temporal properties in AI models and cross-domain multimodal data fusion in the physical world, and introduce fundamental methodologies to tackle these challenges. Real-world deployments of urban computing will be also presented at the end of this talk. |
 | Professor Min LiuHunan University, China Bio: Prof. Min Liu is a second-level professor at Hunan University and serves as the Party Secretary of the School of Artificial Intelligence and Robotics. He is a recipient of National Science Fund for Distinguished Young Scholar founded by National Natural Science Foundation of China (NSFC), Young Changjiang Scholar by the Ministry of Education, and the chief scientist of National Key Research and Development Program. He is also a core member of an NSFC innovative research group. He received the bachelor’s degree from Peking university and the Ph.D. degree from the university of California, Riverside, USA. He currently is the vice president of Hunan Association of Automation, the director of China Machinery Industry Federation Key Laboratory of Advanced Manufacturing Visual Inspection and Control Technology, and the deputy director of the Youth Working Committee of China Society of Image and Graphics. He has led two National Key Research and Development Programs, one NFSC key project, and has received five national- or ministerial/provincial-level scientific and technological awards. Title: Preliminary Studies on Embodied Surgery Robots Abstract: Technology breakthroughs and intelligent upgrading of high-end medical equipment like surgery robots constitute a major national strategic mission oriented toward frontiers of global science and technology, major national demands, and protection of people’s life and health, which also provides decisive support for breaking the technological monopoly of Euro and American high-end digital medical equipment. Concurrent surgery robots lack effective multimodal collaborative perception systems for surgical targets and rely heavily on highly skilled manual operation by surgeons, which severely restricts their large-scale deployment and application in emergency responses to major national incidents in the fields of defense security, pandemics, and disasters. Embodied intelligence enables surgery robots to understand surgical environments, adapt to complex scenarios, and make intelligent decisions in a manner similar to human surgeons, by establishing a closed-loop interaction mechanism of perception-cognition-action, which provides critical pathway toward a leap in autonomous capability. To address these challenging issues on surgery robots, this lecture thoroughly introduces the fundamental principles and key methodologies of robot multimodal perception before, during and after the surgical process, and subsequently presents some preliminary progress achieved by our team in embodied intelligence-driven surgery robot autonomous operation, providing important technological supports for the reduction of medical accidents in our country. |
