Physical AI @ ST Engineering x DSSG

Details
Happy New Year to everyone =). We would like to kickstart this year with a series of talks by AI Scientists from ST Engineering on Physical AI. Starting with an architecture that connects the three pillars of Physical AI (Agentic, Spatial & Robotics), we will explore why successful Agentic AI systems require moving beyond standalone tools to integrate business processes, infrastructure, and ROI-driven strategy for lasting organisational impact. We will also explore the frontiers of Spatial Intelligence and Robotics Intelligence, dissecting the algorithms that allow diverse, heterogeneous robots to coordinate and compete in complex, high-stakes environments.

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​Agenda
6:30 PM – 7:00 PM Registration & Networking
​7:00 PM – 7:15 PM Introduction to Physical AI @ ST Engineering
​7:15 PM – 7:35 PM Agentic AI - Why AI Tools Alone Don’t Create Successful AI Systems by Michal
​7:35 PM – 7:55 PM Spatial AI by Ruofei
​7:55 PM – 8:15 PM Robotics AI by William
​8:15 PM – 8:30 PM Q&A and Closing

Sypnosis

Agentic AI - Why AI Tools Alone Don’t Create Successful AI Systems
Today, powerful AI tools are everywhere, yet most organisations still struggle to translate them into real business impact. Why is that? In this talk, Michal shares perspectives from consulting business units, evaluating AI startups, and designing end-to-end AI systems. He explains why many widely adopted tools, despite being technically impressive, fail to meaningfully transform organisations—and what needs to change for AI to deliver lasting value. The discussion moves beyond tools into the broader system: business processes, ROI and outcome measurement, software engineering discipline, self-hosting and infrastructure considerations, and model selection strategy. Attendees will leave with practical tips on what it actually takes to move from AI adoption to AI impact.

Spatial AI
Dive into the frontier of Spatial Intelligence where generative agent-based simulations meet the reasoning power of LLMs and VLMs. We will discuss the architecture of scalable data factories designed to fuel robust robotics training and bridge the gap between virtual reasoning and physical execution. Join us to explore how these converged technologies are accelerating the development of advanced humanoid applications and embodied AI.

Robotics AI
Uncover the complex dynamics of Swarm Robotics Intelligence as we examine how heterogeneous multi-robot systems master both collaboration and adversarial competition. We will dissect the algorithmic foundations that enable diverse agents to coordinate effectively or outmanoeuvre opponents in unstructured, high-stakes environments. This session offers deep insights into the future of decentralised autonomy and the strategic deployment of mixed-modality robot swarms.

Speakers
Kai Xin Thia is VP at ST Engineering Group Technology Office (GTO), heading the AI.DA Strategic Technology Centre (STC). AI.DA STC focuses on the research translation of AI, including Physical (Robotics Intelligence, Spatial Intelligence & Systemic Intelligence with Agents), Quantum, Urban Computing & AI Solutions. Kai Xin works at the intersection of data and product innovation, with over a decade of experience driving innovation across finance (London Stock Exchange, DBS), media (Tech in Asia), eCommerce (Lazada-Alibaba), and healthcare (Khoo Teck Puat Hospital). Kai Xin holds an MSc in Computer Science from Georgia Tech.

Michal Polanowski is the Head of Generative AI at AI.DA STC. He builds production-ready AI systems and advises leadership and internal business units on strategic AI adoption. He focuses on transforming cutting-edge research into AI-first workflows that solve real operational problems and deliver tangible, verifiable ROI. With nearly two decades of experience across big data, data mining, data science, deep learning, and now Generative AI, Michal has seen both the hype and the reality of enterprise AI. His approach is deeply pragmatic, grounded in hands-on experience, focused on what works, and uncompromising about measurable outcomes. Michal holds a PhD in Consumer Economics / Market Research from The University of Georgia.

Ruofei Ouyang is the Head of Applied AI at AI.DA STC, specialising in spatial intelligence & simulation AI. He holds a PhD in Computer Science from NUS. His academic expertise lies in Gaussian processes, decentralised data fusion, and high-dimensional Bayesian optimisation, particularly for multi-agent systems. He has established a strong research track record, with publications in top-tier venues in artificial intelligence and robotics, including AAAI, AAMAS, and the Autonomous Robots journal. His professional profile combines deep theoretical knowledge in probabilistic modelling with practical experience in large-scale data science applications.

William Teo is the Robotics AI Research Lead at AI.DA STC NEAR Lab, where he directs research translation for multi-agent robotics systems and embodied AI applications. He is pursuing an industrial PhD in Robotics at NUS MARMoT Lab, specialising in multi-robot cooperation and efficient edge AI. His diverse academic background includes a Master of Science in Computer Science from Georgia Tech and a Master of Engineering in Supply Chain Management from MIT. Currently, he focuses on bridging academia and industry by collaborating with top local & overseas universities to advance the real-world deployment of robotics swarm intelligence.