Project supervisor: Dr Jovan Tan (jovan@nus.edu.sg)

Industry partner/collaborator: NUS Sustainability Strategy Unit (SSU)

HVAC systems are the largest energy consumers in commercial buildings, often relying on fixed climate setpoints that ignore rapidly changing external weather conditions. In tropical Singapore, this reactive approach causes significant energy waste and poor thermal comfort.

In partnership with the NUS Sustainability Strategy Unit (SSU), this project challenges a multidisciplinary team to transform campus energy management. The goal is to design, develop, and prototype an Intelligent Weather-Responsive Building Management System to turn static buildings into dynamic, energy-efficient environments.

The team will adopt a holistic, systems-thinking approach, modelling the building's environmental responses to ensure climate adjustments optimise occupant comfort and indoor air quality. They will also develop the software infrastructure to process real-time predictive weather data and create hardware interfaces for automated setpoint adjustments. Additionally, students will evaluate the system's commercial viability and energy-saving potential to develop a scalable deployment plan for sustainable urban infrastructure.

The project aims to produce a fully functional, integrated control prototype that autonomously optimises building climate settings based on predictive weather data, along with an assessment of its energy savings and cost benefits.

Project supervisor: Dr Jovan Tan (jovan@nus.edu.sg)

Industry partner/collaborator: AnomalyBio

Anomaly Bio, a Singapore-based novel ingredient company, aims to develop an AI-powered market intelligence platform that assesses the commercial potential of positioning a new bioactive molecule, pentadecanoic acid (C15:0), a fermentation-derived odd-chain fatty acid linked to longevity, within the skincare lines of major consumer brands (e.g., Estée Lauder, L’Oréal, OneSkin).

The project integrates agentic AI workflows, synthetic persona interviews, and a limited number of real consumer interviews to generate a confidence score for product-market fit during the ideation phase, prior to manufacturing any physical product.

Students will design the synthetic interview methodology, develop product mock-ups and positioning artefacts, conduct validation interviews with actual consumers, and evaluate the extent to which a hybrid approach (e.g., 2 real + 20 synthetic interviews) can mirror the insight quality of traditional larger studies.

This work sits at the crossroads of computational design, AI/LLM engineering, consumer psychology, and industrial design, resulting in a functional prototype tool with direct commercial relevance for Anomaly Bio’s go-to-market strategy.

Project supervisors: Mr Royston Shieh (shiehtw@nus.edu.sg), Mr Keith Tan (keithtcy@nus.edu.sg)

Industry partner/collaborator: CapitaLand Ascott Trust

Global hospitality operators depend on accurate, timely, and verifiable sustainability data from a large number of properties worldwide. These data underpin ESG reporting, corporate disclosures, and strategic sustainability decision-making. However, sustainability data collection today faces several challenges:

• The process is highly manual and time-consuming
• Data submissions are often incomplete, inconsistent, or late
• Regional differences introduce language barriers, varied local capabilities, and uneven understanding of sustainability requirements

While digital tools such as online forms, dashboards, and basic automation are already in use, sustainability teams at headquarters still spend significant effort on data chasing, validation, and reconciliation, limiting their capacity to focus on analysis and impact.

This capstone project invites students to design and prototype an AI-enabled toolkit or system that helps our industry partner to automate the extraction, validation, and transformation of sustainability data from known or input data sources. The system should intelligently map data into standardised formats suitable for dashboards, analytics, and sustainability reports, improving efficiency, accuracy, and scalability across global operations.

Project supervisor: Dr Tang Kok Zuea (kz.tang@nus.edu.sg)

Industry partner/collaborator: TE Connectivity

Problem statement:

• AI-based inspection systems require large datasets of defect images.
• Rare defects lack sufficient data for effective training and detection accuracy suffers for uncommon but critical defects.

Proposed approach:

• Develop generative models optimised for manufacturing defect generation with control mechanisms ensuring physical plausibility of different defect types.
• Implement systems to generate variations in orientations, lighting, and severity.
• Design validation methodologies for models trained on synthetic data.

Potential innovations:

• Generative AI with physical constraint mechanisms for realistic defect generation.
• Domain-specific data augmentation techniques.

Expected/target benefits:

• Improved detection of rare but critical defects.
• Balanced training datasets for AI inspection systems resulting in higher overall inspection accuracy.

Notes:

• Students who take up this project may have the opportunity to be engaged as interns during the project.
• Students who take up this project may be required to go for a site visit of TE Connectivity's manufacturing plant in Dongguan, China to better understand the problem and/or test their solution.
• Students who take up this project are expected to enter their project for the TE AI Cup in 2026.

Project supervisor: A/Prof Mark De Lessio (mdeles@nus.edu.sg)

There is tremendous opportunity in the area of creating front end apps to interface with some of the Large Language Models that enable individuals and/or businesses to interact with them in a manner that adds value to both.  I would be very interested in exploring such opportunities with students that may have ideas that fit into this category.  The Automated Assessment Creation Tool project is based in the concept of how to fairly assess student comprehension based on the content delivery of the learning environment. The premise is the development of a device/app that can be used to record/scan delivered content and then interface with large LLM’s to automatically create assessments to measure the understanding of the content that has been delivered. There are a number of options to possibly achieve this through variations of AI ranging from doing key word assessment on the digitized content and then automatically determine the best assessment questions selected from a pool of pre-determined questions to actually developing the ability to automate the creation of relevant assessment material/questions based on the digitized content that has been captured. A significant goal is to ensure that the assessment is most closely based on the content that has actually been delivered in the actual classroom during the term.

Project supervisor: Mr Graham Zhu (graham.zhu@nus.edu.sg)

Industry partner/collaborator: Chertsey Engineering Pte Ltd

This project aims to develop an integrated software system to streamline the collection, management, analysis, and reporting of daily operational data from five water treatment plants.

The system will replace manual data handling processes with a centralised, automated platform that improves data accuracy, operational visibility, and reporting efficiency, while enabling data-driven decision-making.

Project supervisor: Mr Royston Shieh (shiehtw@nus.edu.sg)

Complex machines such as those used in lithography perform within very specific controlled conditions. Turbulent flow in cooling channels can result in performance degradation and disrupt the nanometer accuracy required in the industry. One of such contributing factors is the presence of bubbles in the cooling circuit. During the maintenance of such complex machines, the coolant must be drained and refilled in the shortest time possible to optimize the uptime of the machines.

The goal of this project is to introduce technical problem-solving skill by thinking beyond the object boundaries like a system engineer. In this holistic approach, the students will explore the basics of fluid dynamics followed by developing a process and design solution which will most effectively fill a cooling circuit with a standard detachable flange. The team shall develop a filling process, and make design improvements from the standard flange with given boundary conditions. The goal is to prevent and remove any trapped bubbles in the circuit. They are to build a simple test set-up with a monitoring system capable of identifying air bubbles, and validate their solution (filling process) and flange designs using the test set up.

Project scope:

• Explore the basics of fluid dynamics and their impact in cooling systems.
• Explore a variety of solutions and narrow down the options with help from design of experiment.
• Develop a process which minimizes bubbles creation during a filling process.
• Generate an FMEA for flange designs and test set up.
• Improve the design of a given standard flange to minimize/prevent trapping air bubbles.
• Build a simple test set-up capable of measuring bubbles and maybe vibrations.
• Test and validate their solution using the set-up to achieve a bubble free circuit within the shortest fill time.

Project supervisors: Mr Soh Eng Keng (ek.soh@nus.edu.sg), Prof Veerasekaran S/O P Arumugam (veera.s@nus.edu.sg)

Food systems are facing unprecedented pressures and transformation globally:

• Climate volatility: increased floods, droughts, heatwaves and marine heat events that threaten yields and fish stocks.
• Geopolitical and supply-chain risks: trade disruptions, export restrictions, and geopolitical tensions that threaten security of import-dependent nations like Singapore.
• Urbanisation and land constraints: rapid urban growth that reduces arable land and necessitates new models of urban food production.
• Ageing population, changing diets and rising incomes: shifts towards protein-rich and diversified diets and increases pressure on food systems, while raising sustainability, health and safety considerations.
• Sustainability, energy and decarbonisation imperatives: growing demand for food systems that are resource-efficient, energy efficient, low-carbon, circular, and environmentally sustainable.

The overarching research domains are classified into 3 areas:

• Agritech Division: smart, urban and circular agriculture
• Aquatech Division: sustainable blue food systems
• Foodtech Division: novel foods, processing and nutrition

The Aquatech Division will develop climate-resilient, high-productivity “blue food” through advanced genetics approaches, health and nutrition, systems engineering, and digital tools. Key research themes and deliverables will include:

• Seed supply and genetic improvement: advanced genetics programmes and improved foundations of reproductive biology and early-life development.
• Nutrition and health: aquatic animal health and advance nutrition strategies - diagnostics, vaccination strategies, and circular feed innovation.
• Systems design and real-time monitoring: next-generation production systems to improve resource efficiency and circularity.
• Data integration, predictive modelling, and digital twins: digital twin models to enable scenario testing and real-time operational optimisation.

Project supervisors: Dr Yen Shih Cheng (shihcheng@nus.edu.sg), Mr Graham Zhu (graham.zhu@nus.edu.sg)

Industry partner/collaborator: Bang & Olufsen

With the advent of spatial audio and 3D audio effects, smart speakers now have the ability to use advanced algorithms and audio processing techniques to place sounds in different parts of a room, creating immersive audio landscapes. In addition, by using precise positioning technologies like Ultra-Wideband (UWB) to localise the listener, these immersive experiences can potentially be much more directed and even follow a listener as they move around a room.

In this project, we will work with Bang & Olufsen (who are renowned for creating iconic audio experiences) to explore these and other technologies (e.g. on-device LLM for voice control (https://gektor650.medium.com/audio-transcription-with-openai-whisper-on-raspberry-pi-5-3054c5f75b95), parametric acoustic arrays (https://youtu.be/Fbphhg9ArXw), innovative mechanical designs to reduce discomfort while using headphones, actuating audio transparent materials in the Beosound Shape (https://www.bang-olufsen.com/en/sg/speakers/beosound-shape) to provide visual representations of music (https://youtu.be/VTEOu1p_0OU), etc.) to design and test new listening experiences.

Project supervisor: Mr Graham Zhu (graham.zhu@nus.edu.sg)

This project aims to design and develop an advanced trajectory tracking device specifically for air pistol training and performance analysis. The system will provide real-time, high-precision tracking of muzzle movement and shot execution, enabling athletes and coaches to analyze shooting mechanics, improve accuracy, and accelerate skill development.

Existing systems such as SCATT MX-W2 and Mantis X2 demonstrate the value of sensor-based and optical tracking technologies in shooting training. However, there is an opportunity to develop a more specialised, compact, and cost-effective solution tailored to air pistol disciplines, with enhanced usability and analytics.

Project supervisors: Mr Kenneth Neo (kenneth@nus.edu.sg), Mr Lim Hong Wee (hongwee@nus.edu.sg)

With the growing trend of carbon fibre composite products, innovative solutions are high in demand to reduce manufacturing time, cost and waste. The mould for composite products are the basics of composite tooling and can be very expensive. Students will learn and investigate the composite manufacturing process and trends. With sufficient knowledge, students derive innovative solutions for composite manufacturing to reduce manufacturing costs and reduce waste.

Project will go through proper engineering work like materials selection, CAD design, optimisation studies, manufacturing techniques and testing.

Project supervisors: A/Prof Lim Li Hong Idris (lhi.lim@nus.edu.sg), Dr Kate Sangwon Lee (katelee@nus.edu.sg)

Industry partner/collaborator: Asian Women’s Welfare Association in Singapore (AWWA)

AWWA’s special education schools provide students with autism, aged 7 to 18, with nurturing and holistic educational experiences that build their independence and maximise their potential in tandem with their capabilities and aspirations. Children diagnosed with Autism Spectrum Disorder (ASD) often encounter significant challenges in developing the life skills necessary for independent living. Yet these competencies are critical for their long-term independence and quality of life.

Despite growing awareness of autism in Singapore and globally, there remains a shortage of accessible, scalable, and engaging tools to support life skills training for children with ASD. Key challenges include:

• Inconsistent training environments: Real-world environments such as MRT stations are unpredictable and can overwhelm children who depend on routine and structure.
• Lack of technology-driven solutions: While educational games exist, few are designed specifically for autistic children with a focus on real-world daily activities.

This project aims to design and develop a platform that is engaging, structured, repeatable, and accessible — one that mirrors real-life scenarios in a safe environment that children can navigate at their own pace.

The scope of the project includes the following:

• Study the needs of students with autism and select appropriate engagement tools.
• Design and develop a platform that simulates real-world daily life scenarios, with an initial focus on navigating the MRT system.
• Apply evidence-based principles of autism-friendly design, including predictable interactions, visual cues, low sensory overload, and positive reinforcement mechanics.
• Create a platform that is intuitive and usable by autistic children aged 9 to 16 without requiring constant adult supervision.
• Provide caregivers and therapists with a dashboard to monitor skill acquisition over time.
• Evaluate the user experience, stability and repeatability.

Project supervisors: Mr Royston Shieh (shiehtw@nus.edu.sg), A/Prof Loh Wai Lam (mpelohwl@nus.edu.sg)

Predicting when flow in a horizontal pipeline transitions from stable stratified flow to potentially damaging slug flow is a critical design challenge in the oil and gas industry. Classical models such as Taitel-Dukler (1976) are widely used but were derived under simplifying assumptions that reduce accuracy at elevated operating pressures. There is a practical need for a fast, data-driven prediction tool that can generalize across a wide range of operating conditions and pipe geometries without requiring expensive experiments or simulations for every new case.

This project challenges the student to design, train, and validate a machine learning classification tool that predicts the stratified-to-slug flow transition boundary in horizontal gas-liquid pipe flow. The student will first conduct a structured literature review of flow regime transition physics and existing prediction methods, then compile a labelled training dataset from published experimental flow maps using dimensionless input features, liquid and gas Reynolds numbers, liquid Froude number, Eötvös number, and dimensionless operating pressure.

A baseline classifier (random forest or support vector machine) will be designed and tested in Semester 1, followed by refinement using a neural network architecture and systematic evaluation against held-out data and classical model predictions in Semester 2. The final deliverable is a validated, documented prediction tool with quantified accuracy and clearly identified operating limits.

Project supervisor: A/Prof Mark De Lessio (mdeles@nus.edu.sg)

With rotary telephones tossed on the trash heap of a by gone era the concept of dialling has been replace by most users interfacing via a key pad with their mobile telephones. However, many people struggle with interfacing with their mobile phone in an efficient and quick manner because of the small size of the key pad used. This project proposes researching the availability of a technical solution that can be used to address this issue. For example, would it be possible to incorporate holographic technology or a similar technology to expand the key to enable an easier interface with the key being selected as it is approached by the finger/thumb doing the selection. Perhaps there is an alternative technology that could be incorporated to achieve the same result.

Project supervisors: Mr Soh Eng Keng (ek.soh@nus.edu.sg), Prof Veerasekaran S/O P Arumugam (veera.s@nus.edu.sg)

Food systems are facing unprecedented pressures and transformation globally:

• Climate volatility: increased floods, droughts, heatwaves and marine heat events that threaten yields and fish stocks.
• Geopolitical and supply-chain risks: trade disruptions, export restrictions, and geopolitical tensions that threaten security of import-dependent nations like Singapore.
• Urbanisation and land constraints: rapid urban growth that reduces arable land and necessitates new models of urban food production.
• Ageing population, changing diets and rising incomes: shifts towards protein-rich and diversified diets and increases pressure on food systems, while raising sustainability, health and safety considerations.
• Sustainability, energy and decarbonisation imperatives: growing demand for food systems that are resource-efficient, energy efficient, low-carbon, circular, and environmentally sustainable.

The overarching research domains are classified into 3 areas:

• Agritech Division: smart, urban and circular agriculture
• Aquatech Division: sustainable blue food systems
• Foodtech Division: novel foods, processing and nutrition

The Agritech Division aims to develop the next-generation crop systems tailored for dense, resource-efficient urban environments and tropical conditions. Key research themes and delierables will include:

• Next-generation crop varieties for controlled environment agriculture and urban farms: production yield, nutrition, shelf life, flavour, visual appeal, databases and protocols
• Integrated precision agricultural systems: AI, sensor networks, automation
• Substrates, microbiomes and resources: growth media to improve nutrient uptake, disease resistance and stress tolerance and improve productivity and nutritional quality
• Waste valorisation and circular integration: upcycle residues into fertilisers, bioenergy, insect feed to enable regenerative, circular agri–aqua–food systems.

Project supervisor: Mr Royston Shieh (shiehtw@nus.edu.sg)

Industry partner/collaborator: CapitaLand Ascott Trust

At the corporate headquarters (HQ) level, teams regularly collaborate with technology vendors to pilot new energy saving solutions and equipment across properties. These pilots are intended to validate whether the technologies perform as claimed and whether the resulting energy savings justify broader roll out across the portfolio.

However, reliably quantifying energy savings during pilot periods remains a major challenge. Energy consumption is influenced by fluctuating factors such as weather conditions and occupancy levels, and many properties lack comprehensive sensor infrastructure or historical baseline data for comparison. As a result, reported savings from vendors may not align clearly with observed changes in utility bills, making it difficult for HQ teams to make confident, evidence based scaling decisions. There is therefore a strong need for a practical, cost effective approach to energy savings quantification that works with limited data while remaining robust and transparent.

This project aims to design and prototype a cost effective, data driven methodology or decision support tool that enables HQ teams to quantify and validate energy savings from technology pilots under real world operating conditions. The solution should achieve the following:

• Enable fair and repeatable comparison of energy performance across pilot and non pilot periods
• Account for variability in influencing factors (eg climate, occupancy, and usage) without excessive manual effort
• Reduce dependence on expensive sensing infrastructure by identifying suitable proxies and alternative data sources
• Reconcile differences between vendor claimed savings and actual utility bill outcomes to support confident scale up decisions

Project supervisors: Mr Soh Eng Keng (ek.soh@nus.edu.sg), Prof Veerasekaran S/O P Arumugam (veera.s@nus.edu.sg)

Food systems are facing unprecedented pressures and transformation globally:

• Climate volatility: increased floods, droughts, heatwaves and marine heat events that threaten yields and fish stocks.
• Geopolitical and supply-chain risks: trade disruptions, export restrictions, and geopolitical tensions that threaten security of import-dependent nations like Singapore.
• Urbanisation and land constraints: rapid urban growth that reduces arable land and necessitates new models of urban food production.
• Ageing population, changing diets and rising incomes: shifts towards protein-rich and diversified diets and increases pressure on food systems, while raising sustainability, health and safety considerations.
• Sustainability, energy and decarbonisation imperatives: growing demand for food systems that are resource-efficient, energy efficient, low-carbon, circular, and environmentally sustainable.

The overarching research domains are classified into 3 areas:

• Agritech Division: smart, urban and circular agriculture
• Aquatech Division: sustainable blue food systems
• Foodtech Division: novel foods, processing and nutrition

The Foodtech Division will develop Novel foods, alternative proteins, and advanced processing technologies that are safe, nutritious, climate-friendly and consumer-acceptable with pathways to manufacturing and scale-up. Key research themes and deliverables will include:

• Novel foods and alternative proteins: advance cultivated meat, plant-based proteins and microalgae, tailored to Asia.
• Food safety, shelf life and quality: preservation and decontamination techniques, packaging innovations, extended shelf life, reduced plastic use, improved environmental impact.
• Functional foods and nutrition: targeted health benefits and stratified nutrition.
• Translational food innovation: pilot-scale processing, manufacturing and product innovation facilities, process validation and market testing.

Project supervisor: A/Prof Mark De Lessio (mdeles@nus.edu.sg)

There is tremendous opportunity in the area of creating front end apps to interface with some of the Large Language Models that enable individuals and/or businesses to interact with them in a manner that adds value to both.  I would be very interested in exploring such opportunities with students that may have ideas that fit into this category.  The “Smart Advisor Tool” consists of  a front end tool that assist individuals to assess what their personal interest are and how that might correlate to the best degree programs that would enable them to realize these interests as working adults.  This would essentially leverage AI to categorize their interests, identify relevant degree programs, identify the types of jobs available to individuals with similar interests, identify the best universities that offer such programs and eventually even perhaps the organizations that have such jobs available.  There would be many ways to monetize such a tool including working with Universities to advertise their programs and/or partnering with job placement agencies or even with organizations themselves to push potential candidates to them that have already been vetted and matched with positions they currently are seeking to fill.

Project supervisor: Mr Soh Eng Keng (ek.soh@nus.edu.sg)

Industry partner/collaborator: National Parks Board (NParks)

As Singapore becomes increasingly urbanised, the rate of human-wildlife conflicts is expected to increase disproportionately. The NParks Wildlife Management team aims to seek ways to improve operational efficacy of wildlife management through technological solutions that can reduce the occurrence of human-wildlife conflict, while simultaneously reducing man-hours involved in routine manual operational work, such as deterring of wildlife and site surveillance.

Project objective

Students should design an innovative and efficacious method to reduce human-wildlife conflict through automation and technology. Our target species of interest include: crows, monkeys, otters, pigeons, bats.

Project scope

Students are encouraged to explore a variety of robotics-based ideas and pitch creative ideas. Some ideas for students’ consideration include:

• Targeted tracking deterrent systems that can deploy ranged deterrents on animals (while being safe for humans) (e.g. guided lasers, aerosols, sprays, gel beads)
• Animatronic/robotic animals for guarding against various wildlife (e.g. robotic dogs/snakes for monkey deterrence.)
• Focused sonic masking of noisy bird roosts to reduce noise disamenities

Students should bear in mind that while these solutions aim to deter wildlife from human spaces, the welfare of the animals and people needs to be considered as well. Ideally, these solutions can achieve deterrence whilst minimising harm done to animals and potential disamenities to the public, hence the need for solutions to be targeted and detection to be accurate.