The One About Digital Twins
As cities grow smarter and AI systems scale rapidly, digital twins enable us to test, predict, and optimize real-world systems. From reducing data center energy consumption to creating more resilient urban environments, learn how these virtual replicas are transforming sustainability and urban planning.
More About the Sharings
Aarthi Kannalagan (Senior Consultant, Dell Technologies) will show how digital twins are revolutionising urban planning by creating dynamic virtual models that mirror real-world conditions. Through practical examples in urban mobility, energy management, and disaster response, discover how cities integrate IoT sensors, GIS, and building systems data to reduce emissions, optimise resources, and build climate resilience. (Technical Difficulty: 100)
Dr. Marcel Ignatius (Senior Research Fellow, NUS) will share insights from the CoolNUS-BEAM Project (Baselining–Evaluating–Action–Monitoring) under CoolNUS. The initiative combines AI-driven modeling, sensor networks, and digital twin technologies to capture and predict microclimate dynamics across the NUS Kent Ridge campus. By integrating data fusion, machine learning, and simulation frameworks, BEAM translates environmental data into real-time insights for heat mitigation and sustainable campus design. Dr. Ignatius will discuss how AI and digital twins can transform environmental sensing into actionable intelligence for climate-resilient urban systems. (Technical Difficulty: 100)
Generative AI’s rapid rise brings both innovation and environmental challenges, as large-scale AI data centers strain global energy resources. Dr. Wen Yonggang (Professor, NTU) will share insights on the twin transition: how digitalization and sustainability can reinforce each other through sustainability by AI and AI for sustainability. Learn about the energy footprint of large language models, renewable-powered data center strategies, and how AI-driven optimization and digital twins can help cut emissions and costs. Dr. Wen will also introduce “The AI Wears PRADA” framework, uniting Platforms, Researchers, Algorithms, Data, and Applications to build a more sustainable and efficient AI ecosystem. (Technical Level: 100)
More About the Speakers
Aarthi Kannalagan is a Senior Consultant based in Singapore, with extensive experience in smart city solutions and digital transformation. She is passionate about leveraging emerging technologies such as AI and digital twins to drive sustainable and resilient urban development. In addition to her consulting work, Aarthi serves as an adjunct lecturer, sharing her expertise with the next generation of professionals and contributing to thought leadership in the tech and innovation space.
Dr Marcel Ignatius is a Senior Research Fellow at the Department of Building, National University of Singapore, Marcel holds both a Doctoral and Master's degree in Building Science from the National University of Singapore's esteemed Department of Building, and a Bachelor's degree in Architecture from Tarumanagara University. His expertise includes conducting comprehensive research on urban micro-climates and their impact on outdoor thermal comfort and energy performance in urban settings. Marcel has actively participated in numerous research projects in collaboration with Singapore government agencies. These initiatives have primarily focused on critical areas such as urban heat island (UHI) mitigation, urban climatic mapping, and sustainable design methodologies.
Dr. Yonggang Wen is a Professor and President’s Chair at Nanyang Technological University (NTU), Singapore, where he serves as Associate Provost (Graduate Education) and Dean of the Graduate College. He earned his PhD from MIT and has published over 300 papers on cloud computing, green data centers, and AI-driven systems. His award-winning innovations, including Cognitive Digital Twin for Data Centers, have earned global recognition. Dr. Wen is Editor-in-Chief of IEEE Transactions on Multimedia, an IEEE Fellow, Singapore Academy of Engineering Fellow, and ACM Distinguished Member.