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Why Microsoft chose Singapore as its First Hub for AI Research in Southeast Asia

Why Microsoft chose Singapore as its First Hub for AI Research in Southeast Asia

Managing Director of Microsoft Research Asia Dr Lidong Zhou shares why Singapore offers the ideal ecosystem for translating AI lab research into real-world solutions.

Man in a blue shirt and glasses seated in a modern office setting, with a staircase in the background.

The world’s leading technology companies are accelerating their artificial intelligence (AI) investments in Southeast Asia, and Singapore is fast emerging as the region’s strategic command centre. Recognised for its trusted governance, strong research institutions, and world-class digital infrastructure, Singapore offers companies a stable environment where advanced AI research can move rapidly from concept to deployment.

It is against this backdrop that Microsoft has chosen Singapore as the home of Microsoft Research Asia (MSRA)’s first AI research lab in Southeast Asia. Microsoft determined that Singapore uniquely combines the research depth, talent density, and cross-sector collaboration needed to scale AI’s societal impact across the region.

In this interview, Dr Lidong Zhou, Corporate Vice President of Microsoft, and Managing Director of Microsoft Research Asia, shares how Singapore enables Microsoft’s next phase of AI innovation, from foundational models and agentic AI to healthcare transformation and Southeast Asia-ready AI systems:
 

1. Why did Microsoft choose Singapore as the base for its first Southeast Asia AI research lab?

Microsoft takes a long-term approach in every region we enter, and Singapore is no exception. Over the past year and a half, we carefully evaluated the potential of establishing a research lab here. Singapore is widely recognised as a globally respected hub for sectors such as finance, healthcare, and advanced manufacturing—industries that are inherently well-suited for advanced AI applications due to their complex data, high standards of safety and reliability, and rich, diverse scenarios for testing new AI systems.

Building on decades of established partnerships between Microsoft and Singapore’s universities as well as industries, we saw first-hand how the country provides an ideal environment that blends world-class institutions, policy foresight, strong cross-sector collaboration, and cultural diversity to translate research into measurable industrial impact. As Microsoft Research (Microsoft’s research subsidiary) expand our presence and influence across the region, Singapore stands out as a natural strategic gateway connecting Asia and the world.

 

2. What makes Singapore an attractive hub for global companies looking to establish AI centres of excellence?

Singapore’s talent density, industrial diversity, strong government support, and visionary policies such as the National AI Strategy 2.0 make it a compelling base for innovation, creating a uniquely frictionless environment for companies to build, test, and scale AI with global reach.
 

“A key differentiator is Singapore’s “full-stack” AI talent pool. The workforce demonstrates deep research expertise, strong engineering capability, and multilingual, cross-cultural fluency. Singapore also produces highly adept practical implementers who can translate foundational models into reliable, resource-efficient solutions for demanding industries like healthcare, finance, and logistics. These strengths enable us to develop equitable AI solutions that work effectively across Southeast Asia’s diverse environments.”

Dr Lidong Zhou

Managing Director

Microsoft Research Asia


Government-supported initiatives such as the EDB’s Industrial Postgraduate Programme (IPP) help sustain this pipeline. A standout example is Dr Xinxing Xu, our first local researcher at MSRA Singapore, whose work in multimodal AI and foundation models illustrates how Singaporean talent bridges cutting-edge research with real-world impact.

We see Singapore as a global aggregation point for top AI talent, providing a regional training ground through joint PhD programmes and hands-on research opportunities, and a multiplier enabling AI developed here to scale responsibly across Southeast Asia.
 

3. What role does Singapore play in shaping global standards for trustworthy and responsible AI, and how will MSRA leverage this?

Singapore is uniquely positioned to be a pioneer in shaping global standards for trustworthy and responsible AI. Its strength lies in pairing an ambitious national AI strategy with a transparent, internationally respected regulatory framework. Tools such as the AI Verify Foundation’s Testing Framework and Toolkit have become a global reference point for operationalising responsible AI principles. As a global AI hub, Singapore provides the regulatory clarity needed to champion standards that are technologically sound yet globally adaptable.

This governance model complements MSRA’s core research pillar of Societal AI, an interdisciplinary area of study examining how AI intersects with social systems and public life. Our Singapore lab is deeply engaged in this work, collaborating with local researchers to align AI systems with Southeast Asian cultural values and ethical principles using our Value Compass framework, to advance a more accountable, transparent, and human-aligned AI trajectory.
 

4. How will the Singapore lab grow and contribute to Microsoft Research’s broader AI research priorities?

Microsoft Research bridges foundational research—spanning multimodal models, domain-specific foundation models, agentic reasoning, and safety techniques—with industry-grade applications deployed at scale. This enables tangible impact across sectors.

Locally, MSRA Singapore works closely with industry partners to explore AI transformation. For instance, we are advancing precision health with SingHealth by leveraging its high-resolution pathology datasets to develop AI capabilities that enable more personalised analysis and more accurate diagnostics. This would help clinicians deliver precise, tailored treatment plans for patients.

We are also collaborating with the National University of Singapore (NUS) on embodied and spatial intelligence by advancing benchmarks that integrate perception, language, and action for handling of complex manipulation tasks. These efforts represent some of the many real-world applications that we aim to operationalise from Singapore.
 

5. What are the unique challenges and opportunities in advancing AI innovation in a region as diverse as Southeast Asia?

Southeast Asia is entering a pivotal phase of digital and economic growth, driven by a young, digitally savvy population and increasing adoption of services from fintech to e-commerce. It is fast becoming one of the world’s most dynamic testbeds for applied AI; however, Southeast Asia’s vast diversity—linguistic, cultural, economic, and regulatory—creates complexity for developing scalable AI solutions. Models must exhibit high cultural and linguistic adaptability and work across varying data maturity, infrastructure levels, and rapidly evolving regulatory landscapes.

Conversely, the same diversity presents a unique opportunity to pioneer inclusive and resource-efficient AI. Our work in multimodal foundation models, human-aligned AI, and Societal AI helps bridge linguistic divides, respect cultural nuances, and promote equitable outcomes. The region’s unique datasets and growing digital adoption in areas such as healthcare, finance, logistics, and smart cities provides fertile ground for impactful AI solutions.
 

6. How is AI itself reshaping the future of research, in areas like foundation models, agentic AI, and multimodal intelligence?

Large-scale foundation models, underpinned by multimodal intelligence and agentic systems, have reduced the need to train models from scratch, enabling rapid prototyping and accelerated experimentation across fields such as industrial AI, healthcare, and spatial intelligence.

Intelligent AI agents can now reason, plan, and autonomously execute complex, multi-step research workflows previously infeasible for humans—from hypothesis generation to experimental design and data interpretation—dramatically shortening development cycles. For example, RD-Agent, MSRA’s in-house technology, acts as an intelligent R&D co-pilot by automating literature triage, running ablations, and synthesising findings in weeks instead of months, enabling researchers to focus on ideas rather than manual tasks.

AI is evolving from a passive tool into an active collaborator and powerful accelerator of discovery—particularly in R&D and complex scientific work.

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