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35 undergraduates have been named recipients of the SoC Student Awards 2025, the School’s annual recognition of student contributions beyond academics. 

Lee Kai Xuan, a Year 2 Computer Science student, received the Outstanding Award. Five students received Gold Awards, five received Silver, 16 received Bronze and eight were awarded Merit Certificates. The recipients come from every year of study and five undergraduate programmes: Computer Science, Computer Engineering, Business Analytics, Information Systems and Information Security.

The awards recognise contributions across four areas: leadership in SoC clubs and interest groups, achievement while representing the School or NUS in competition, voluntary service under the School's name, and participation in School events such as orientation and the Inter-Faculty Games.

Full List of SoC Student Awards 2025 Recipients 

Outstanding Award 

• Lee Kai Xuan (Year 2, Computer Science)

Gold Award 

• Leong Hoi Sheng, Daryl (Year 2, Business Analytics) 
• Leow Sze Theng, Jolyn (Year 3, Computer Science)
• Neo Ryan (Year 2, Computer Science)
• Agarwal Ritvi (Year 3, Computer Science)
• Darryl Yeng Wei Zhi (Year 2, Computer Science)

Silver Award 

• Chai Pin Zheng (Year 1, Computer Science)
• Chen Dong Jun (Year 2, Computer Science)
• Glenda Chong Rui Ting (Year 4, Computer Science)
• Alyssa Ong Yi Xian (Year 4, Computer Science)
• Ong Yu Xuan Zowie (Year 2, Computer Science)

Bronze Award 

• Chen Le (Year 3, Computer Science)
• Choong Jing Xiang (Year 2, Computer Science)
• Ravichandran Gokul (Year 2, Computer Science)
• Hoi Hao Yuan (Year 2, Business Analytics)
• Ho Cheng En Bryan (Year 4, Computer Science)
• Jha Satwik (Year 3, Computer Science)
• Kin Wei Qi, Vanessa (Year 4, Computer Science)
• Ryan Koh Jun Hao (Year 2, Computer Engineering)
• Lim Cheng Hean, Ryan Darius (Year 3, Business Analytics)
• Nigel Gabriel Morais (Year 2, Information Security)
• Chaudhari Moushami Sujeet (Year 2, Business Analytics)
• Hasan Ahmed Nasif (Year 2, Computer Science)
• Ong Si Hui (Year 3, Information Systems)
• Arnav Parag Salkade (Year 3, Computer Engineering) 
• Shananth Sivakumar (Year 1, Computer Science)
• Sun Jiaen (Year 3, Computer Science)

Merit Certificate

• Chew Jing Heng (Year 4, Computer Science)
• Hing Yen Xing, Joanne (Year 4, Computer Science)
• Lee Isaac (Year 4, Computer Science)
• Lin Myat (Year 3, Business Analytics)
• Tirodkar Om Milind (Year 2, Computer Engineering)
• Tan Ee Syuen (Year 2, Computer Science)
• Tan Le Yew (Year 3, Computer Science)
• Tay Shu Sui (Year 2, Business Analytics)

Two of our Computer Science PhD students, Weida Li and Zhuanghua Liu, have won the Best Paper - Runner-up Award at the Conference on Uncertainty in Artificial Intelligence (UAI) 2026 — ranking 2nd among 1,087 submissions to one of the top-ranked venues in AI research, working under the supervision of Associate Professor Bryan Kian Hsiang Low.

Their paper, "Nonlinear Axiomatic Attribution for Cooperative Games," tackles a quiet flaw in one of machine learning's most trusted tools: the Shapley value, widely used to rank how much each player, feature, or data point contributes to an outcome. The team found that its defining strength, linearity, is also its blind spot – it can miss real differences between contributors because too much variation falls into what the method simply can't see. Their fix borrows from the "least core," a longstanding alternative to Shapley values, and builds a new family of nonlinear attribution methods around it, each one solving an optimisation problem to stay as faithful as possible to the real data.

Attribution methods like this quietly shape how AI decisions get explained, how contributions get priced in data markets, and how audits trace which inputs actually mattered.

Congratulations to Weida, Zhuanghua, and Prof Low!

A research paper led by Song Yuexi, a Computer Science undergraduate at NUS Computing and member of the NUS Tennis Team, has received the Best Paper Award at the 3rd International Sports Analytics Conference and Exhibition (ISACE 2026).

The paper, TANS-Agent: Structured, Multi-Agent Tactical Reasoning for Tennis Match Analysis and Strategy Recommendation, introduces a multi-agent AI framework that uses a structured notation system to support tennis match analysis and tactical strategy recommendation. By grounding large language model (LLM) agents in explicit spatial and tactical representations, the framework enables more interpretable, consistent, and tactically grounded recommendations.

The research was conducted under the guidance of Professor Liang Zhenkai.

Congratulations to Song Yuexi, Professor Liang, and the team on this achievement.

Provost's Chair Professor Abhik Roychoudhury has been named a member of the inaugural class of the ACM SIGSOFT Software Engineering Academy, joining a global cohort recognised for lasting contributions to software engineering research, practice, and education.

The Academy was established by ACM SIGSOFT, the Association for Computing Machinery's Special Interest Group on Software Engineering, as a standing body of honour marking the organisation's 50th anniversary. It was previewed at the FSE 2026 conference in Montreal in July, before the inaugural class was formally announced. New members will be elected annually through nominations from the community.

Prof Roychoudhury has been with the Department of Computer Science at NUS Computing since 2001, where he leads the Trustworthy and Secure Software (TSS) research group. His work on semantic and symbolic program repair, including the SemFix approach recognised with an ICSE Most Influential Paper (Test-of-Time) Award, helped establish program repair as a field of its own. That line of research later grew into AutoCodeRover, an autonomous program-improvement agent that was acquired by Sonar and commercialised as the SonarQube Remediation Agent, where he served as Senior Scientific Advisor.

His group's work on fuzz testing and symbolic execution has similarly shaped how the field approaches software security, earning recognition including the IEEE New Directions Award. Prof Roychoudhury is the current Editor-in-Chief of ACM Transactions on Software Engineering and Methodology (TOSEM), sits on the editorial board of Communications of the ACM, and has chaired both ICSE and FSE, the field's two flagship conferences, as well as the FSE Steering Committee. He was named an ACM Fellow in 2024 and was the inaugural recipient of the NUS Outstanding Graduate Mentor Award, with former doctoral students now on faculty at institutions around the world.

The recognition arrives alongside a decade-long throughline in his research: from the original SemFix paper in 2013, through the ICSE Test-of-Time award for that work, to AutoCodeRover's acquisition by Sonar and its commercial launch this year as the SonarQube Remediation Agent.

For more information, visit https://www2.sigsoft.org/academy/inaugural-class.html.

Three papers co-authored by Professor Xiaokui Xiao have won top honours at leading international conferences this year, recognising work spanning AI system efficiency, differential privacy auditing, and graph algorithms.

“I am deeply grateful to our students, research staff, former students and collaborators, whose hard work and creativity made these results possible, and to NUS Computing for its strong support of our research,” said Prof Xiao. 

ACM SIGKDD Best Student Paper Award
KDD 2026, 9–13 August, Jeju, South Korea 

Yiqian Huang, a Year 3 PhD candidate in Prof Xiao’s group, won the award for SCOPE: Cost-Efficient Model Selection for Compound AI Systems under Quality Constraints, co-authored with Shiqi Zhang, a postdoctoral researcher in Xiao's group, Tianyuan Jin, a former PhD student of Xiao’s, who is now an Assistant Professor in the Thrust of Data Science and Analytics (DSA) at the Hong Kong University of Science and Technology (Guangzhou), and Prof Xiao.

The paper proposes a way to choose a cost-effective combination of large language models for a multi-step AI system, while ensuring that the system as a whole meets a required quality level. In experiments, SCOPE met that quality level at as little as one-sixth of the cost of the strongest competing approach.

IEEE Symposium on Security and Privacy Distinguished Paper Award
IEEE S&P 2026, 18–21 May, San Francisco, USA 

Auditing Apple's DifferentialPrivacy.framework: Implementation Bugs, Misconfigurations, and Practical Risks put a widely trusted privacy system to the test. Apple's differential privacy framework runs on more than 2.35 billion devices, quietly reassuring users that the data their phones send back can't be traced to them individually. The team, led by Rishav Chourasia and Ergute Bao, both former PhD students of Xiao’s now with BetterData and as a postdoctoral researcher at Mohamed bin Zayed University of Artificial Intelligence respectively, together with Uzair Javaid, and Prof Xiao, found that several mechanisms did not deliver their stated privacy guarantees.

In particular, some secure-aggregation protocols had local privacy protection switched off entirely, and out on the open internet, the team found analytics logs that could be decoded to reveal what websites someone had visited, or which emojis they had typed. The team disclosed the vulnerabilities to Apple, and was informed that the affected mechanisms had been deprecated.

ACM PODS Best Newcomer Award
PODS 2026, 1–3 June 2026 (part of SIGMOD/PODS), Bengaluru, India

Near-Optimality for Single-Source Personalized PageRank, by Xinpeng Jiang, Haoyu Liu, and Siqiang Luo, of the College of Computing and Data Science at Nanyang Technological University, together with Prof Xiao, won the Best Newcomer Award at the ACM Symposium on Principles of Database Systems. The paper largely closes a long-standing gap between the best known upper and lower bounds on how efficiently personalized PageRank, a graph-ranking method used in web search and recommendation systems, can be computed for a single source node.

A research group at NUS Computing has received a Distinguished Paper Award at CAV 2026, one of the flagship conferences on formal methods and verification, for their work on Velvet, a tool that automatically proves whether a piece of code does what it’s meant to do. CAV 2026 was held as part of FLoC 2026, the 9th Federated Logic Conference.

The paper, “Velvet: A Foundational Multi-Modal Verifier for Imperative Programs in Lean” was one of eight papers to receive the award, out of 81 accepted submissions and 311 papers submitted overall. The team is led by Associate Prof Ilya Sergey, together with Vladimir Gladshtein, a final-year PhD student and the paper's lead author, fellow NUS Computing PhD students Yueyang Feng, Dipesh Kafle, George Pîrlea, Qiyuan Zhao, and Vitaly Kurin.

Proving Code Does What It’s Supposed to Do 

Testing a program only reveals how it behaves on the inputs someone happened to try. Velvet takes a different route, known as formal verification. Developers write a precise statement, alongside their code, describing what that code is supposed to do, and Velvet builds a mathematical proof that the code satisfies it across every possible input. It works on ordinary imperative programs, the kind most real-world software is written in, with mutable variables, loops, and exceptions.

Verification tools already exist. Dafny is one; Verus, used at Amazon AWS, Google, and Microsoft, is another. Prof Sergey's team points to two recurring frustrations with these tools. Automation that fails offers little recourse beyond reworking the code and hoping for a different outcome. And the verifiers themselves are substantial pieces of software, which means trusting the tool becomes almost as important as trusting the code it's checking.

"For decades, every program verifier has been its own island, with its own language, its own logic, and its own bugs. Our bet is that verifiers should instead be ordinary libraries inside a single proof assistant, where they can share specifications, decades of formalised mathematics, and the rapidly improving AI automation growing around Lean,” said Assoc Prof Sergey. “Velvet shows this recipe can produce a tool that matches industrial verifiers while being trustworthy down to a tiny proof-checking kernel. The award is encouragement that the community sees promise in this direction. The credit goes to the people who built Velvet: Vladimir Gladshtein, a final-year PhD student who led the effort, Vitaly Kurin, who started on it as an intern and is now joining us as a PhD student, and the rest of the VERSE Lab team, who together pushed Lean well past what it was thought to be for.”

Building a Verifier Inside a Proof Assistant

Velvet is built as a library inside Lean, the proof assistant mathematicians use to formalise research mathematics and now a leading platform for AI-driven theorem proving. Most proofs are generated automatically. Where automation runs out, developers can open the proof, see exactly what remains unproven, and finish it themselves using Lean's tactics, its Mathlib library, or AI assistance. Lean's small trusted kernel checks every proof regardless of how it was produced, so confidence in the result doesn't depend on trusting Velvet's own code.

Since the CAV 2026 paper was published, Prof Sergey says Velvet is now ten times faster than the version reported at the conference, and that it now outperforms Dafny, whose automation is considered the state of the art in automated verification. Velvet extends a broader research programme in VERSE Lab building a family of domain-specific verifiers as Lean libraries on shared foundations. An earlier tool, Veil, verifies distributed protocols and was published at CAV 2025. The work was partially supported by a Singapore Ministry of Education Tier 3 grant and a Stellar Development Foundation Academic Research Grant.

Velvet is open source, available at https://github.com/verse-lab/velvet.

What's Next

The team has two directions in mind. One treats Velvet as a form of computer-checked pseudocode. Researchers have long used pseudocode to communicate algorithms concisely, but without any guarantee that the pseudocode itself is correct. A Velvet program can serve the same purpose while carrying a machine-checked proof, opening a path toward AI-assisted algorithm discovery, where an AI proposes candidate programs, Lean checks them, and only the correct ones survive. LeetProof, a first step in this direction, is due to appear at ASE'26.

The other is teaching. As students increasingly write code with AI assistance, Prof Sergey argues that the skill worth building is learning to state precisely what a program should do. They plan to bring Velvet into the classroom, giving students immediate, rigorous feedback on whether their programs meet their own specifications.

NUS Presidential Young Professor Yi-Jun Chang and his collaborators have won two awards at leading conferences in distributed computing this year: the Outstanding Paper Award at the 2026 ACM Symposium in Parallelism in Algorithms and Architectures (SPAA), given to the top three papers at the conference, and the Best Student Paper Award at the 2026 ACM Symposium on Principles of Distributed Computing (PODC), which recognises the best paper among those with substantial contributions from student authors.

The Outstanding Paper Award at SPAA 2026 recognises “Energy-Efficient Aggregation and Minimum-Degree Spanning Trees in Radio Networks”, joint work by Prof Chang and Guan Yang Ze, who started the project as an undergraduate doing his Final Year Project under Prof Chang. Their starting point was a problem anyone who has managed a network of battery-powered sensors will recognise: devices burn through power just by listening, so pooling readings from across a network can drain them fast. 

Prof Chang and Guan built a protocol where devices sleep for most of the process and wake only when needed, still ending up with a combined result, such as a total count or an aggregated reading, from across the entire network. The energy cost comes close to the lowest possible for any network shape, while maintaining efficient running time. The techniques may inform how future sensor networks for environmental monitoring or smart infrastructure are designed to run longer on a single charge.

“Efficient Counting and Simulation in Content-Oblivious Rings” won the Best Student Paper Award at ACM PODC 2026, joint work by Prof Chang, his PhD student Zhou Haoran, Jérémie Chalopin (Aix Marseille Université), and Giuseppe A. Di Luna (Sapienza Università di Roma). The paper studies the content-oblivious model, where devices communicate only through pulses that signal nothing more than that a message was sent, with no data and no sender identity attached. The model represents an extreme fault scenario: an adversary can corrupt the content of every message in the network, though genuine messages cannot be deleted and fake ones cannot be created. 

The authors found that this stripped-down setting costs far less than expected. Ordinary digital communication can be reconstructed from these content-free pulses at only constant overhead, well below earlier results where the cost grew with the size of the network. From there, they developed faster algorithms for basic tasks including counting devices and combining information distributed among them, even when the contents of every message may be completely corrupted.

“Both papers improve our understanding of the capabilities of distributed computation under severe communication constraints, showing that surprisingly efficient algorithms are still possible. I am also happy to see the contributions of Yang Ze and Haoran recognised by the research community through these awards,” Prof Chang said.

Associate Professor Liang Zhenkai from the Department of Computer Science has received the Test-of-Time Award at the 21st ACM Asia Conference on Computer and Communications Security (AsiaCCS 2026), recognising his 2011 paper, “Jump-oriented programming: a new class of code-reuse attack”.

The paper, co-authored with Tyler K. Bletsch, Xuxian Jiang, and Vincent W. Freeh, was first presented at AsiaCCS 2011 in Hong Kong. It introduced jump-oriented programming, a code- reuse attack technique that circumvents the stack-based defences designed to stop earlier return-oriented programming attacks, revealing a new class of vulnerability in how software could be exploited without injecting malicious code. 

The ACM AsiaCCS Test-of-Time Award honours papers that have demonstrated enduring significance and lasting contributions to the field of computer and communications security.

15 years on, the paper continues to be cited and built upon in the security research community studying code-reuse attacks and defences against them.

Congratulations to Associate Professor Liang on this recognition. 

Qu Wenjie, a PhD candidate in Computer Science, has been selected as a 2026 ML and Systems Rising Star by MLCommons, an honour recognising early-career researchers shaping the future of machine learning and systems research. 

Qu joins a cohort of 39 researchers from 26 institutions worldwide, chosen from a pool of more than 175 applicants. This programme connects participants with leaders across academia and industry, and this year’s group presented their work at the Rising Stars Workshop, hosted at AMD’s headquarters in Santa Clara from 30 - 31 July.

His research sits at the intersection of machine learning, security, and cryptography, focused on making large language models trustworthy and verifiable. Working under the supervision of NUS Presidential Young Professor Zhang Jiaheng and closely with Professor Dawn Song, he builds systems for auditing and proving the correctness of AI services, including zero-knowledge proofs for model inference and training, with work published in venues such as IEEE S&P, USENIX Security, ACM CCS, and NDSS.

"I am deeply honoured to receive this recognition, which reflects not only my past work but also the support of my advisor, mentors, collaborators, friends, and the NUS Computing community," said Qu. "My research focuses on building secure and trustworthy AI systems, particularly understanding and mitigating emerging security risks in large language models and autonomous agents. Going forward, I hope to develop principled security foundations that enable increasingly capable AI agents to operate safely and reliably in the real world.”

As AI agents take on more autonomous roles, from executing transactions to making decisions with real-world consequences, Qu's work on verifiable AI systems addresses a problem that will only grow more pressing: how to trust what these systems are doing.

NUS School of Computing (SoC) co-organised this year's edition of SINFRA, the annual Singapore-France workshop on Computer Science and AI, bringing together more than 80 researchers from across the two countries' AI, robotics, and human-computer interaction communities.

Held over three days at NUS and A*STAR, the workshop is run each year by the International Research Laboratory IPAL, a joint venture between CNRS, NUS, A*STAR and a growing roster of French universities. The NUS Co-Director of IPAL, Associate Professor Ooi Wei Tsang welcomed participants, opening a programme that spanned 26 scientific presentations and more than 20 posters.

Among them were five oral presentations from SoC’s researchers, spanning trustworthy computing and human-drone interaction.

Professor Abhik Roychoudhury presented on trustworthy automatic programming, and Professor Xiaokui Xiao on data protection through differential privacy, both areas where SoC has built a sustained research track record in making AI systems safer and more accountable. 

PhD candidates Peisen Xu and Yize Wei, alongside Peter Cleveland, a Research Fellow at the Augmented Human Lab, represented SoC’s growing body of work on human-drone interaction – spanning AR interfaces for drone piloting, assistive drones for blind and low-vision users, and the embodied experience of drone control. 

This year's SINFRA also marked a milestone for the wider IPAL partnership, formally welcoming the University of Grenoble Alpes back into the fold after an online address by its president, Yassine Lakhnech – the latest step in a collaboration between CNRS, A*STAR, NUS and French partner universities now in its 28th year.

12 NUS Computing Recipients Honoured at 2026 National Awards

12 NUS Computing staff and faculty members have received the 2026 National Awards, for their dedication and service to NUS and Singapore.

Established in 1962, the National Awards recognise individuals for their contributions and service to the nation.

Public Administration Medal (Bronze)

• Associate Professor Soo Yuen Jien, Department of Computer Science, and formerly Director, Centre for Development of Teaching & Learning
• Professor Lee Mong Li, Director, NUS Centre for Trusted Internet and Community

Efficiency Medal

• Cheng Chek Keong, Laboratory Technologist

Long Service Medal

• Senior Lecturer Chia Wai Kit, Henry, Department of Computer Science
• Senior Lecturer Low Kok Lim, Department of Computer Science
• Professor Abhik Roychoudhury, Department of Computer Science
• Associate Professor Chan Chee Yong, Department of Computer Science
• Ang Hwee Ying Agnes, Senior Associate Director, Graduate Studies
• Teo Pei Pei, Management Support Officer, Office Operations and Events
• Associate Professor Tan Swee Lin, Sharon, Department of Information Systems and Analytics
• Professor Chan Mun Choon, Department of Computer Science 
• Professor Tulika Mitra, Dean of School of Computing
• Associate Professor Soo Yuen Jien, Department of Computer Science, and formerly Director, Centre for Development of Teaching & Learning

These honours recognise the contributions of colleagues across our academic, research, technical, and administrative community. Their work over the years has supported our students, strengthened the School, and contributed to the wider NUS community.

Congratulations to all our recipients on this well-deserved recognition, and thank you for your years of service to NUS Computing and Singapore.

A paper by Bai Yunpeng, a PhD candidate at NUS Computing, has been published in Nature Human Behaviour, one of the leading journals in the behavioural and cognitive sciences.

“Hierarchical resource rationality explains human reading behaviour” addresses a long-standing gap in reading research. Existing theories have explained either how the eyes move during reading or how comprehension is built, but not the link between the two. Bai Yunpeng, working with Associate Professor Antti Oulasvirta of Aalto University and Professor Zhao Shengdong of City University of Hong Kong, proposes that both are governed by a single principle: the brain selects eye movements to maximise expected understanding while minimising cognitive and time costs.

This principle operates across three nested timescales. Fixation decisions support word recognition. Sentence-level processing guides when the eyes skip ahead or move back to reread. Text-level comprehension goals shape memory and rereading over longer stretches of text. The team built this framework into a computational model, which reproduced a wide range of established findings in reading research, from effects at the level of individual words to outcomes as broad as overall comprehension.

The model offers a general account of how the brain coordinates perception, memory and action under limited resources, a principle that extends beyond reading. Potential applications include tools that support people with reading difficulties, educational technology that adapts to individual learners, and embodied AI systems designed to interpret visual information the way humans do.

The team plans to extend the model beyond text to more visual forms of reading, integrate it with embodied AI systems, and build simulators to support future studies of reading behaviour.

Assistant Professor Bian Yatao and NUS Presidential Young Professor Zhang Jiaheng have been awarded the National Research Foundation (NRF) Fellowship (Class of 2026), one of Singapore’s most competitive programmes for early-career researchers.

NRF Fellowship supports outstanding researchers to conduct five years of independent work in Singapore, with the aim of strengthening the country’s scientific and technological capabilities. Fellows are selected for the quality and ambition of their research, and their potential to deliver breakthrough outcomes.

Their projects: 

• Bian Yatao – Towards the Era of Reasoning AI for Science: Reasoning-Empowered Foundation Models for Atomic Systems
• Zhang Jiaheng – Efficient Zero-Knowledge Proofs for Large-Scale Computation and Applications

The awards reflect the range of research at NUS Computing, spanning AI for scientific discovery and the foundations of secure computation.

Sean Jean (Year 4, Computer Science) has won the Singapore National Finals of the International Quant Championship (IQC), organised by WorldQuant. He will represent Singapore at the Global Finals on 6-7 October, competing against champions from 12 other countries. 

Sean entered IQC this year after coming across it through a high school friend and later through Associate Professor Steven Halim’s CS3233 Competitive Programming course, which introduced him to quantitative finance. He submitted two alphas – trading signals or strategies used to predict market movements and generate returns – for the first stage during a break from his summer internship, without expecting to advance, and eventually qualified for stage two ranked outside the top 10,000. 

With 10 days left before the deadline, he built an automation pipeline that generates trading alphas using large language models and reinforcement learning, drawing on coursework from NUS’ AI-focused modules – CS3263, CS3264, and CS4248. His ranking rose to first place by the final presentation round, where placement was based on both score and presentation. 

IQC is a team-based competition, with teams of up to four members. Sean competed alone, under the name Sharpe Mind.

He is now refining his pipeline ahead of the Global Finals, drawing on conversations with WorldQuant researchers and other teams from the presentation round.

Three faculty members from NUS Computing have been awarded the prestigious National Research Foundation Investigatorship (NRF-I) under its 11th call, recognising their leadership and potential to drive high-impact research in artificial intelligence.

The NRF Investigatorship is one of Singapore’s most competitive research awards. It supports a small number of outstanding Principal Investigators to pursue ambitious, high-risk research that can lead to significant scientific breakthroughs. The programme is part of NRF’s broader mission to strengthen Singapore’s research ecosystem and develop world-class scientific talent.

The awardees are:

Abhik Roychoudhury - Agentic AI based Software of the future: from Scale to Trust

Angela Yao -  From Perception to Understanding: Contextual AI for Anticipatory and Proactive Assistance

Harold Soh - Learning Persistent Agent-Centric Representations for Embodied AI

Each award supports a five-year programme, enabling investigators to push the boundaries of their fields and contribute to Singapore’s position as a global hub for innovation.

Together, these projects reflect the strength and ambition of research at NUS Computing.