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PS Lee reposted thisPS Lee reposted thisCongratulations to Assistant Professor James Utama Surjadi on receiving the 2026 Materials Research Society (MRS) Postdoctoral Award, one of the Society's Spring 2026 awards. The MRS Postdoctoral Award recognises postdoctoral scholars who show exceptional promise through research excellence, leadership, advocacy, outreach or teaching. James received the award for his work in advancing the mechanical properties of polymer and medium-entropy alloy metamaterials through defect engineering and architecture design. Read more about the award and his work: https://lnkd.in/gekFCw9n https://lnkd.in/gPpp4-Mn
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PS Lee shared thisThe AI Data Centre Is Becoming One Machine For decades, data centres were engineered as separate systems: IT, power, cooling and controls. AI is making that model obsolete. The real bottleneck has shifted At 100–200+ kW per rack, the challenge is no longer simply whether we can remove enough heat from the chip. The harder question is whether the entire infrastructure can deliver power, move heat, survive faults and be commissioned fast enough for expensive silicon to become productive. A cold plate can perform perfectly while the data centre underperforms. A CDU can meet specification while poor hydraulics, sensor error or control interactions erode reliability. The unit of optimisation is no longer the component. It is the whole machine. Cooling is now part of compute Liquid cooling is not simply replacing fans with pipes. What matters is how much heat is captured into liquid, at what coolant temperature, with what pressure drop, pumping penalty and residual air load. Those choices determine plant size, brownfield viability and how much of a constrained electrical allocation can actually reach the GPUs. A megawatt saved in cooling is not merely an energy saving. In a power-constrained facility, it can become another megawatt of compute. PUE cannot tell the whole story Two facilities can report similar PUE and yet deliver very different AI productivity. One may suffer low utilisation, excessive water use, thermal throttling or commissioning delays. The other may convert the same grid capacity into more useful computation. The better question is: “How much useful intelligence do we obtain per unit of electricity, water, carbon, land, capital, grid capacity and time?” That is infrastructure productivity. Time to first token begins in the plant room A GPU waiting for pipe flushing produces zero tokens. A rack awaiting commissioning produces zero tokens. A cluster derated by its thermal system produces fewer tokens. Deployment velocity is therefore an AI productivity metric. Standardised interfaces, trustworthy metrology, repeatable commissioning, coolant quality and system-level validation determine how quickly capital becomes useful compute. The next frontier is integration The AI data centre is becoming a digital factory. Cooling becomes part of the computer. Power becomes part of thermal design. The digital twin becomes part of operations. The workload scheduler becomes part of infrastructure control. Resilience becomes a property of the system, not a box. The central challenge is no longer to cool the chip. It is to engineer the entire chain, from silicon to grid to first token, as one machine. If we keep optimising individual subsystems while the real failures occur at the interfaces, are we still engineering the right thing? #AI #AIInfrastructure #DataCentres #LiquidCooling #Engineering #DigitalInfrastructure #Sustainability #EnergyEfficiency #DigitalTwin
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PS Lee shared thisGrowing NUS Mechanical Engineering for the Next Decade The discipline is no longer defined only by machines, structures, fluids, heat and manufacturing as separate domains. Increasingly, the most consequential problems sit at their intersections: intelligent machines interacting with the physical world; AI systems constrained by energy and thermal limits; semiconductor systems shaped by mechanics and packaging; manufacturable new materials; and space systems demanding exceptional integration. At NUS Mechanical Engineering, we are planning to grow the department by around 30% over the coming years. But this is not simply about becoming bigger. It is about building critical mass where Mechanical Engineering can shape what comes next. Our current thinking centres on five interconnected capability clusters: • Intelligent Machines, Robotics & Physical AI — embodied intelligence, autonomous systems, sensing, actuation and control. • Advanced Manufacturing, Materials & Semiconductor Systems — AI-enabled materials discovery, advanced manufacturing, semiconductor packaging, reliability and manufacturability. • Sustainable Energy, Thermal Systems & AI Infrastructure — high-performance thermal management, liquid and two-phase cooling, next-generation ACMV, energy systems, industrial decarbonisation and infrastructure for AI at scale. • Mechanics, Fluids & Multiphysics Engineering — structures, flows, reliability and complex physical systems, strengthened by computation, scientific machine learning and advanced experimentation. • Space, Aerospace & Extreme-Environment Engineering — integrated systems where mass, energy, reliability and environmental constraints become especially demanding. The people matter more than the labels. I hope we can attract researchers who are deep in fundamentals but unafraid to cross boundaries; who combine theory, computation and experiments; who see AI not as a substitute for engineering science, but as a powerful engineering tool; and who want their work to matter beyond publications. We need experimentalists who can build things that have never existed before, and computational researchers who can reveal behaviour experiments alone cannot reach. Increasingly, we need people who can do both, or collaborate seamlessly across that divide. Tomorrow’s technologies will still depend on enduring questions: How does heat move? When does a structure fail? How does a fluid behave? How do we manufacture reliably? How do we control a dynamic system? The context has changed, from AI factories and semiconductor systems to autonomous machines and space, but the foundations remain. Growing NUS ME is about using those foundations to engineer what comes next. I hope some of the world’s most ambitious engineering talents will come build that future with us. #NUS #NUSMechanicalEngineering #MechanicalEngineering #PhysicalAI #AdvancedManufacturing #Semiconductors #Energy #AIInfrastructure
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PS Lee shared thisENGINEER IN THE LOOP: WHEN AI MEETS PHYSICAL REALITY As AI becomes embedded in critical infrastructure, one question matters more: When the data disagree, who decides what is true? More data does not always mean more truth Imagine a high-density AI data centre during an abnormal event. Rack temperature is rising. Flow, temperatures and pump power all look plausible. The CDU reports no fault. Yet the energy balance does not close. Every sensor can look “reasonable” while the system as a whole tells an impossible story. This is where engineering AI must go beyond anomaly detection. Measurement. Model. Physics. Engineer. I increasingly see engineering AI as a four-layer evidence system: Measurement tells us what instruments observed. Models tell us what the system should be doing. Physics tells us what the system can possibly be doing. Engineering judgement decides what to do. Sensors drift. Digital twins become stale. AI can extrapolate beyond validated conditions. Engineers make mistakes too. The goal is not one “source of truth”, but independent evidence challenging itself. The data centre is becoming one machine In AI infrastructure, compute, power, cooling, controls and operations are tightly coupled. Workload alters power. Power becomes heat. Heat changes coolant demand. Cooling changes pumping and heat rejection. Thermal conditions feed back into silicon performance. Local optimisation can create global failure. Engineering AI must reason at system level, not component level. Engineer in the loop cannot mean approval button “Human-in-the-loop” is meaningless if the engineer sees only a recommendation and an Approve button. Meaningful oversight requires knowing: What does the AI believe? What supports or contradicts it? How certain is it? What happens if it is wrong? Autonomy should vary with consequence, uncertainty and reversibility. Low-risk, reversible actions can be automated. As uncertainty and consequence rise, authority should shift toward the engineer. The best AI may sometimes say: “I don’t know.” For critical infrastructure, confidence without evidence is dangerous. A strong engineering AI should be able to say: “I cannot distinguish between two plausible causes. Here is the safest reversible action, and the additional measurement that would resolve the uncertainty.” That is engineering intelligence. The engineer of the AI era will move from reading sensors to interrogating evidence, and from knowing the answer to knowing when it should not be trusted. The goal is not Human versus AI. AI watches everything. The digital twin predicts what should happen. Measurements show what appears to be happening. Physics constrains what can happen. The engineer decides what to trust when they disagree. That is what “Engineer in the Loop” should mean. #Engineering #ArtificialIntelligence #AIInfrastructure #DataCentres #DigitalTwin #CriticalInfrastructure #HumanInTheLoop #STDCT
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PS Lee shared thisSingapore’s Infrastructure Challenge Is Increasingly a Talent Challenge Singapore has spent decades building world-class infrastructure. A harder question now is: Are we building the engineering profession needed to sustain and reinvent it? More than a manpower issue The challenge is not simply whether enough young people choose civil, mechanical or electrical engineering. It is whether talented engineers see a compelling long-term future: meaningful technical work, mentorship, progression and remuneration that reflects the value they create. If advancement means moving progressively away from engineering, we weaken the technical depth that complex infrastructure depends on. Infrastructure is becoming more, not less, technical The next generation will involve smart grids, AI data centres, energy storage, digital twins, climate adaptation and low-carbon industry. A modern airport, hospital, semiconductor fab or data centre is an integrated system of structures, power, thermal systems, fluids, controls and operations. We should stop thinking of M&E as merely supporting infrastructure. M&E is infrastructure. Technical mastery must have a future A Principal Engineer, Chief Engineer or Technical Fellow should be able to achieve status, remuneration and influence comparable with senior management. Young engineers need to see that becoming exceptionally good at engineering is itself a viable destination. Projects should produce engineers, not just assets Every infrastructure programme should deliver two outcomes: the asset itself, and stronger engineering capability. That means giving young engineers ownership of design decisions, commissioning, troubleshooting and failure investigation, not only coordination. It also means procurement that supports technical talent, mentoring and innovation rather than competition mainly on fees. Engineering judgement takes time Equipment can be imported. Consultants can be engaged. Technology can be licensed. But knowing when a design is questionable, why a system is unstable, how a plant may fail, or whether an innovation will survive real operating conditions comes from years of designing, commissioning, operating and improving. Once that depth is lost, it is difficult to rebuild quickly. The real question Singapore has always treated infrastructure as a strategic national asset. We should treat infrastructure engineering capability with the same seriousness. The question is not only: How do we persuade more students to study engineering? It is also: How do we make infrastructure engineering a profession that talented people want to enter, remain in and master over decades? Getting this right strengthens Singapore’s ability to design, operate and reinvent the infrastructure on which its competitiveness, resilience and quality of life depend. #Engineering #Infrastructure #MechanicalEngineering #ElectricalEngineering #CivilEngineering #Singapore
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PS Lee posted thisThe Infrastructure Era: AI Needs More Than Compute SMS Tan Kiat How’s opening address at Tech Week Singapore reframes how we should think about AI infrastructure. For the past few years, “AI infrastructure” has largely meant GPUs, data centres, networks, cloud and energy. Those foundations remain essential. But compute alone does not create adoption, trust or economic value. The next challenge is not simply building infrastructure for AI. It is building the infrastructure around AI. Four layers of the AI economy The speech describes four interdependent layers: 1. Physical — compute, data centres, connectivity and energy. 2. Deployment — data, integration, identity, cybersecurity and workflows. 3. Trust — evaluation, assurance, provenance, auditability and accountability. 4. Interoperability — allowing models, clouds, platforms and AI agents to work together. A GPU becomes economically productive only when intelligence can move securely from the model into a real workflow and produce a useful outcome. For resource-constrained Singapore, that distinction matters. The objective cannot simply be more MW of data-centre capacity. Every MW, litre of water, square metre of land and dollar of infrastructure must enable more economic and societal value. From model assurance to system assurance An important transition is from asking whether an AI model performs well to asking whether the entire AI-enabled system works safely and effectively. Real-world outcomes depend on more than the model: Human + AI + data + workflow + organisation + infrastructure. There is a direct parallel in AI data centres. Optimising a GPU, cold plate, CDU, UPS or cooling plant independently does not necessarily optimise the facility. Compute, power, cooling, controls and workload increasingly have to be treated as one cyber-physical system. The same systems principle applies above the data centre. Trust becomes infrastructure When AI can access databases, call tools, move information, transact or interact with other agents, identity, authorisation, traceability and intervention become part of the operating architecture. Trust has to be engineered into the system, not added after deployment. Build for change Models, hardware, workloads and applications will change. The durable strategy is modularity, interoperability, observability and adaptability. The Infrastructure Era is not simply an era of bigger data centres. It is an era of integrated systems—where physical infrastructure provides capacity, deployment infrastructure converts it into capability, trust allows it to scale, and interoperability allows value to move across organisations and borders. The question is shifting from “How much AI infrastructure can we build?” to “How effectively can we turn infrastructure into trusted, useful intelligence?” #AI #AIInfrastructure #DataCenters #AgenticAI #DigitalTrust #SystemAssurance #SustainableDataCenters #Singapore https://lnkd.in/dQPQTr2u
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PS Lee shared thisThe US$6 Trillion Question: Can AI Create Enough Value to Pay for the Infrastructure? Bain & Company has put a striking number on the AI infrastructure challenge: by 2031, the AI market may need to approach US$6 trillion in annual revenue to sustain projected infrastructure spending of as much as US$1.5 trillion per year. Existing consumer and enterprise AI could contribute US$1.2–1.8 trillion. That leaves roughly US$4.2 trillion of new value still to be created. This is not simply an “AI bubble” question The US$6 trillion figure is not a forecast of what AI will earn. It follows from Bain’s assumption that infrastructure capex of US$1.5 trillion can be sustained if it represents roughly 25% of industry revenue. That distinction matters. The real question is whether society can convert rapidly expanding compute capacity into sufficient economic output before infrastructure outruns applications. Productivity alone will not close the gap Much of today’s AI discussion focuses on copilots, automation and reducing labour hours. Valuable—but Bain argues these gains are nowhere near enough. The larger opportunity must come from new sources of value: autonomous systems, robotics and physical AI, scientific discovery, new materials, drug development, energy innovation and products that do not yet exist. Bain estimates physical AI alone could represent a US$900 billion opportunity and “autonomous everything” another US$400 billion. This suggests the next phase of AI may be less about generating more text—and much more about connecting intelligence to the physical economy. And this changes the data-centre conversation Bain projects US$5–6.5 trillion of data-centre investment by 2030, adding around 150 GW of compute capacity. That expansion collides directly with constrained grids, transformers, water, land, semiconductors and skilled manpower. So the important metric cannot simply be how many megawatts we build. Nor is PUE alone sufficient. We increasingly need to ask: How much useful economic and societal value can each constrained MW, litre of water, dollar of capital and tonne of carbon produce? That is why Singapore’s principle of “maximum value from compute”, rather than maximum compute, is particularly important. From compute scarcity to value discipline The first phase of the AI race was about securing GPUs and building capacity. The next phase must be about utilisation, application innovation and value creation. The winners may not be those with the most compute, but those that translate compute most efficiently into useful intelligence—and useful intelligence into economic and societal outcomes. The AI infrastructure race is therefore becoming a value-creation race. And that may ultimately be the more consequential competition. #AI #AIInfrastructure #DataCenters #PhysicalAI #Robotics #DigitalInfrastructure #EnergyEfficiency #SustainableDataCenters #Compute #Innovation #Singapore #SustainabilityAI faces US$6 trillion test to justify data centres: reportAI faces US$6 trillion test to justify data centres: report
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PS Lee shared thisFrom Data Centres to Autonomous Infrastructure Very pleased to see Yokogawa Engineering Asia join the Sustainable Tropical Data Centre Testbed Phase 2.0 (STDCT 2.0) as we scale the platform towards a multi-megawatt pilot facility for next-generation AI infrastructure. The significance goes well beyond adding another technology partner. AI data centres are becoming industrial systems As rack densities rise and liquid cooling, high-power electrical architectures, energy storage and dynamic AI workloads converge, the data centre is evolving into a highly coupled cyber-physical system. Optimising individual components is no longer enough. Compute, power, cooling, water, controls and operations increasingly have to be orchestrated as one integrated machine. Yokogawa brings an important capability. Its experience in industrial measurement, automation, energy management and autonomous operations brings approaches long used in complex process industries to AI infrastructure. From monitoring → optimisation → autonomy The next frontier is not simply collecting more sensor data. It is turning operational data into progressively higher levels of intelligence: Measure → Understand → Predict → Optimise → Act A sufficiently instrumented AI data centre should be capable of continuously balancing thermal conditions, electrical demand, cooling capacity, water consumption, equipment health and workload requirements. Digital twins, advanced control, AI-based optimisation and predictive maintenance can move operations from a largely reactive model towards self-optimising and increasingly autonomous infrastructure. Why a live testbed matters Many promising technologies perform well individually. The harder question is whether they continue to perform when integrated into a real operating environment. STDCT 2.0 provides a platform to test precisely this. In Singapore’s hot and humid climate, we can validate not only equipment efficiency, but also system interactions, resilience, control strategies and operating envelopes under realistic AI workloads. Critically, the collaboration is not limited to deploying commercial products. We will also explore research problems where today’s solutions are not yet sufficient. The bigger opportunity As AI infrastructure grows towards hundreds of kilowatts per rack and eventually megawatt-class computing systems, operational intelligence may become as important as hardware efficiency. The sustainable AI data centre of the future will therefore not just be better cooled or better powered. It will be measured, integrated, adaptive, predictive and increasingly autonomous. That is an exciting direction for STDCT 2.0—and an important reason why industrial automation expertise matters. #STDCT #AIInfrastructure #SustainableDataCenters #LiquidCooling #IndustrialAutomation #IndustrialAI #AutonomousOperations #DigitalTwin #EnergyEfficiency #Sustainability #NUS #Yokogawa #Singapore https://lnkd.in/esM_-EwWYokogawa to Assist the Scale Up of Tropical Climate AI Data Center Testbed into a Multi-megawatt Pilot FacilityYokogawa to Assist the Scale Up of Tropical Climate AI Data Center Testbed into a Multi-megawatt Pilot Facility
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PS Lee shared this⚛️ Singapore is building something more important than a nuclear power plant: the capability to decide, rigorously, whether it should ever build one. The restructuring of government teams, together with expanded hiring, research and training, marks a significant step in Singapore’s nuclear journey. But the signal should not be over-read: Singapore has not decided to deploy nuclear energy. Capability before commitment The 2027 IAEA Integrated Nuclear Infrastructure Review Phase 1 will assess Singapore across 19 areas, including safety, radioactive waste, emergency planning, legal and regulatory frameworks and institutional readiness. That distinction matters. Phase 1 asks whether a country has the expertise, institutions and processes to make an informed decision, not whether it has decided to proceed. Singapore is therefore creating option value before making a technology commitment. Nuclear readiness is a systems capability A credible nuclear programme cannot be built around reactor technology alone. It requires independent regulation, safety culture, safeguards and security, emergency preparedness, waste strategy, financing capability, project governance, skilled operators and public confidence. That is why the organisational changes and manpower pipeline may matter as much as the reactor studies themselves. SNRSI’s planned expansion, the proposed NUS master’s programme in nuclear engineering and a broader pipeline of scientists and engineers can strengthen Singapore’s ability to assess technologies and advice independently. Valuable even without a reactor These capabilities retain value even if Singapore ultimately decides not to deploy nuclear power. Several Southeast Asian countries are examining nuclear options. Singapore therefore benefits from deeper expertise in reactor safety, severe-accident analysis, atmospheric dispersion, radiochemistry, radiation protection and emergency preparedness irrespective of its own deployment decision. In that sense, nuclear capability is also regional resilience capability. The real question is broader than SMRs Much discussion focuses on small modular reactors and other advanced designs. But the eventual decision should not be reduced to “Is the reactor safe enough?” The harder question is whether a nuclear pathway can satisfy Singapore’s requirements simultaneously for safety, reliability, affordability, land use, waste management, system flexibility, security and long-term decarbonisation, and how it compares with other low-carbon pathways. The deeper signal is not that Singapore is moving toward nuclear. It is that Singapore is investing in the capability to make the decision from a position of knowledge rather than dependence. For infrastructure that may operate for decades, institutional capability is not overhead. It is part of the technology. #NuclearEnergy #EnergyTransition #Singapore #EnergySecurity #NuclearSafety #Engineering #EnergyPolicySingapore reshapes government teams, steps up hiring as it builds nuclear energy capabilitiesSingapore reshapes government teams, steps up hiring as it builds nuclear energy capabilities
PS Lee replied to a comment
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Wani Azmi I would not ask the colo to report a single universal “compute per MW” number. The more workable model is layered: the colo reports what it controls, facility energy, water, usable IT capacity, thermal/electrical headroom and infrastructure efficiency, while the tenant reports workload-specific compute productivity within its own trust boundary.
The shared opportunity is at the interface: standardised telemetry and agreed KPIs can show whether the infrastructure is enabling or constraining the compute, without requiring the colo to know the tenant’s proprietary workloads. In other words, distributed ownership does not prevent optimisation; it just means measurement and accountability must also be distributed.
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PS Lee liked thisPS Lee liked thisCongratulations to Assistant Professor James Utama Surjadi on receiving the 2026 Materials Research Society (MRS) Postdoctoral Award, one of the Society's Spring 2026 awards. The MRS Postdoctoral Award recognises postdoctoral scholars who show exceptional promise through research excellence, leadership, advocacy, outreach or teaching. James received the award for his work in advancing the mechanical properties of polymer and medium-entropy alloy metamaterials through defect engineering and architecture design. Read more about the award and his work: https://lnkd.in/gekFCw9n https://lnkd.in/gPpp4-Mn
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PS Lee liked thisHonored to be able to partner such distinguished organisations in Korea Atomic Energy Research Institute | KAERI and Thailand Institute of Nuclear Technology (Public Organization). KAERI is a key player in the Korean nuclear ecosystem and has deep experience in the design and development of many SMR designs. We also have a strong bond with TINT under the leadership of Dr.Thawatchai Onjun as members of the ASEAN-NPSR. Now that Thailand is assessing SMRs as part of its draft Power Development Plan 2026, it would be an excellent opportunity to share our insights.PS Lee liked thisAt the sidelines of the 70th International Atomic Energy Agency (IAEA) General Conference in Vienna, SNRSI signed Memoranda of Understanding (MOUs) with the Korea Atomic Energy Research Institute | KAERI and the Thailand Institute of Nuclear Technology (Public Organization), strengthening our international and regional partnerships in nuclear research and capability building. The MOUs will expand opportunities for collaboration in areas including advanced nuclear technologies, nuclear safety, and safety assessments. Through joint research, technical exchanges, workshops, training and researcher exchanges, we will continue to learn from one another and build expertise across our institutes. We look forward to deepening our partnerships with KAERI and TINT, exchanging knowledge and strengthening the technical capabilities needed to support the assessment of nuclear energy technologies in Singapore and the region. Photo: MSE (KAERI MoU) #SNRSI
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PS Lee liked thisPS Lee liked thisA meaningful visit to NUS, Singapore 🇸🇬 It was a pleasure meeting Professor PS Lee , Head of the Department of Mechanical Engineering at the National University of Singapore (NUS) and Executive Director of the Energy Studies Institute. I also had the opportunity to visit the Sustainable Tropical Data Centre Testbed (STDCT) and see firsthand the work being done around sustainable and energy-efficient data-centre technologies. Our discussion touched on AI-ready data centers, high-density computing, advanced cooling, energy efficiency and sustainability — areas that are becoming increasingly interconnected as rack power densities continue to rise. At TierX Data Center Solutions,we see tremendous potential in bringing industry, academia and applied research together to develop the next generation of data-center infrastructure. Looking forward to exploring deeper collaboration with the NUS ecosystem. From India to Singapore — building connections for the next generation of AI infrastructure. #TierX #NUS #DataCenters #AIInfrastructure #LiquidCooling #SustainableDataCenters #EnergyEfficiency #Singapore #Innovation
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PS Lee liked thisPS Lee liked this👏 Join us in congratulating Prof Jason Kai Wei LEE on his promotion to Professor, effective 1 October 2026. As Director of the Heat Resilience & Performance Centre | NUS Medicine and Co-Lead of the @Human Potential Translational Research Programme at NUS Medicine, Prof Lee is internationally recognised for his pioneering work in environmental physiology, occupational health, and heat resilience. His research has driven impactful initiatives, including Project HeatSafe, and shaped policies and practices that protect workers, military personnel, and communities in increasingly challenging climates. 🏃 Through his leadership, innovation, and commitment to translating science into real-world solutions, Prof Lee has strengthened Singapore's and Southeast Asia's capabilities in heat resilience while influencing health, workplace safety, and human performance practices worldwide. Read more via the link in comments below 👇🏼 #NUSMedicine #AcademicPromotion #HeatResilience #HumanPerformance #ClimateResilience
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PS Lee liked thisPS Lee liked thisMessage from Executive Director Peter Govindasamy: “What does it take to bring nearly 200 countries, each with different national circumstances, energy needs, and development priorities, to agree on a common climate agenda? Having participated in the UNFCCC negotiations process since COP14 in Poznań in 2008, witnessed the successful conclusion of the landmark Paris Agreement in 2015, and served as Senior Adviser to the UAE Presidency that delivered the UAE Consensus at COP28 in 2023, I have seen firsthand that successful climate diplomacy is as much about building trust, understanding, and compromise as it is about negotiating technical provisions and legal texts. It was therefore a privilege to represent ESI as its Executive Director and contribute to the Ministry of Foreign Affairs (MFA) Singapore's capacity-building programme for senior officials from across different regions on the UNFCCC negotiations process, as well as the evolving international frameworks governing mitigation, adaptation, climate finance, technology development and transfer, and implementation support. As the world seeks to reconcile the energy trilemma of energy security, economic development, and sustainability, strengthening the capabilities of negotiators and policymakers remains essential to advancing the objectives of the UNFCCC and the Paris Agreement, while supporting an orderly, equitable, and inclusive energy transition.”
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PS Lee liked thisPS Lee liked this𝐂𝐋𝐎𝐔𝐃, 𝐑𝐄𝐆𝐔𝐋𝐀𝐓𝐈𝐎𝐍 & 𝐃𝐄𝐋𝐈𝐕𝐄𝐑𝐘 From Digital Demand to Digital Infrastructure Infrastructure exists to serve digital demand. And that demand is evolving rapidly. Cloud computing, AI, high-performance computing and emerging digital services are creating new requirements for infrastructure capacity. This is where Neo Cloud Providers become an important part of the ecosystem. But demand alone cannot build infrastructure. Infrastructure development also requires: Regulatory certainty. Clear rules. Effective governance. Predictable frameworks. And strong legal foundations. At the same time, infrastructure ultimately needs to be delivered. From design to engineering. From construction to commissioning. From systems integration to operations. This is why Law Firms & Regulatory Experts and Construction Companies & System Integrators are also essential pillars of the KIPDI ecosystem. The ecosystem therefore connects the entire chain: 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐃𝐞𝐦𝐚𝐧𝐝 → 𝐂𝐚𝐩𝐢𝐭𝐚𝐥 → 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐢𝐨𝐧 → 𝐄𝐧𝐞𝐫𝐠𝐲 → 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 → 𝐂𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 → 𝐃𝐚𝐭𝐚 𝐂𝐞𝐧𝐭𝐞𝐫𝐬 → 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 The objective is not simply to connect companies. 𝐈𝐭 𝐢𝐬 𝐭𝐨 𝐜𝐨𝐧𝐧𝐞𝐜𝐭 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬. And when capabilities are connected, Indonesia can move from fragmented development toward a more coordinated digital infrastructure ecosystem. #IDPRO #KIPDI #CloudComputing #DataCenter #DigitalInfrastructure #Regulation #Construction #AIInfrastructure
Experience & Education
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Publications List
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See publicationClick link below (See publication) for my Google Scholar Profile
Patents
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Microchannel heat sink
Issued US US20070025082 A1
A microchannel heat sink as well as method includes one or more microchannels through which a working fluid flows to remove heat from a heat-generating component, such as a microelectronic chip, and one or more recesses disposed in a surface communicated to the one or more of the microchannel to enhance heat transfer rate of the microchannel heat sink. The recesses can be located in a surface of a cover that closes off the microchannels. The one or more recesses can be located at one or more…
A microchannel heat sink as well as method includes one or more microchannels through which a working fluid flows to remove heat from a heat-generating component, such as a microelectronic chip, and one or more recesses disposed in a surface communicated to the one or more of the microchannel to enhance heat transfer rate of the microchannel heat sink. The recesses can be located in a surface of a cover that closes off the microchannels. The one or more recesses can be located at one or more local hot spot regions to enhance heat transfer rates at the local regions as well as overall heat removal rate of the heat sink.
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English
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Chinese (Simplified)
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Energy Institute
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ASME
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The Institution of Engineers Singapore
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