Dr. Peichen Zhong is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He leads the Applied Machine Learning and Materials Modeling (AM³) Group, focused on advancing computational methods for clean energy technologies. His research integrates machine learning with atomistic simulations to tackle challenges in battery materials, disordered materials, and sustainable energy systems. Education: B.S. in Physics from University of Science and Technology of China (2018); Ph.D. in Materials Science from UC Berkeley (2023, advised by Prof. Gerbrand Ceder); Postdoctoral training at Lawrence Berkeley National Lab and BIDMaP, co-advised by Persson, Cheng, and Krishnapriyan. Research Interests: Computational modeling of battery cathodes/electrolytes, AI-driven interatomic potentials, statistical mechanics in disordered materials, and generative models for scientific discovery. Key areas include Li/Na-ion batteries, solid-state reactions, and sustainable energy materials. Awards: BIDMaP Emerging Scholar Fellowship (UC Berkeley CDSS, 202?), 2023 Rising Stars in Materials Science (CMU/MIT/Stanford). Labs/Teams: The AM³ Group at NUS MSE focuses on interdisciplinary research combining theory, computation, and AI4Science. Current openings include PhD students and postdoctoral researchers.
Brian Y. Lim is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), leading the NUS Ubicomp Lab. His research integrates Explainable AI (XAI) , Human-Computer Interaction (HCI) , and Ubiquitous Computing to address societal challenges in healthcare , wellness , and urban sustainability . He holds a Ph.D. and M.S. in Human-Computer Interaction from Carnegie Mellon University and a B.S. in Engineering Physics from Cornell University. Education: B.S., Engineering Physics, Cornell University M.S., Human-Computer Interaction, Carnegie Mellon University Ph.D., Human-Computer Interaction, Carnegie Mellon University His research focuses on designing human-centric AI systems that prioritize transparency and user trust. Key areas include explainable AI algorithms , context-aware computing , data visualization , and trustworthy machine learning . Projects like RexNet and Directed Diversity demonstrate his work in creating relatable AI explanations and enhancing crowd creativity through algorithmic prompting. Recent publications (2022–2025) highlight advancements in faithful visual explanations , user-centric XAI frameworks , and healthcare analytics . Notable awards include the CHI'22 Best Paper Award , IMWUT Distinguished Paper Award , and MOE Outstanding Mentor Award . He has advised numerous PhD and Masters students and leads the NUS Ubicomp Lab, which explores applications like TasteHealthy (food recommendation app) and Food Loves Fellowship (narrative data visualization).
Kian-Lee Tan is a Tan Sri Runme Shaw Senior Professor and Professor of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a Ph.D. (1994), M.S. (1992), and B.Sc. (1st Class Honours) from NUS. His academic career spans decades of contributions to database systems and data analytics. Ph.D. in Computer Science, National University of Singapore (1994) M.S. in Computer Science, National University of Singapore (1992) B.Sc. in Computer Science (1st Class Honours), National University of Singapore As a leading researcher in database systems, Tan focuses on query processing and optimization in multiprocessor/distributed systems, database performance, security, and multimedia information retrieval. His work extends to computational biology applications like genome databases and real-time influence analysis on social streams. His recent publications highlight trends in GPU-accelerated graph analytics, trajectory pattern mining, and computational journalism. These works emphasize parallel processing, performance optimization, and social/media data analysis. IEEE Technical Achievement Award (2013) President Science Awards, Singapore (2011) NUS Graduate School Excellent Mentor Award (2010/2011) Outstanding University Researchers Award (1997/1998) Tan has supervised numerous research projects and mentored students contributing to database systems. He secured significant grants including a US$1 million Ripple Foundation grant (2024) for financial technology education. His editorial roles include ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. He leads the FinTech Lab at NUS Computing and has served on the VLDB Endowment Board (2012-2017). His work bridges database foundations with emerging applications in AI, fintech, and computational journalism.
Zaw Myo TUN is a Research Fellow (Adjunct) at the Saw Swee Hock School of Public Health, National University of Singapore, and serves as Senior Manager (Research and Evaluation) at IRD Global Singapore. His academic career includes roles such as Postdoctoral Fellow (2022) and Research Associate (2013–2021) at the same school. He holds a PhD from NUS (2016–2021), an MSc in Epidemiology from the London School of Hygiene & Tropical Medicine (2011–2012), and an MBBS from the University of Medicine, Mandalay, Myanmar (2003–2008). His research focuses on epidemiology, global health, tuberculosis vaccines, and public health policy. Key interests include maternal vaccination coverage in low-resource settings, hospital-acquired infections (e.g., MRSA), antibiotic resistance, and the ethical implications of pandemic control measures. He has contributed to studies on SARS-CoV-2 transmission dynamics, road traffic injury patterns in Bhutan, and malaria drug policy compliance in India. His publications analyze public perceptions of health policies, such as during the COVID-19 pandemic, and explore factors influencing healthcare behaviors in diverse populations. His work bridges clinical data (e.g., electronic medical records) and population-level health surveillance to inform evidence-based interventions. While no awards are explicitly mentioned, his research has addressed critical global health challenges like tuberculosis control in Myanmar and antimicrobial stewardship in Singapore primary care settings. Zaw Myo TUN’s career emphasizes translational research, linking academic findings to real-world health system improvements. His roles span academia and non-profit sectors, reflecting a commitment to addressing public health disparities through collaborative, policy-driven solutions.
Dai Zhongxiang is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHKSZ), where he joined in August 2024. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (January-June 2024) and a Postdoctoral Fellow at the National University of Singapore's Department of Computer Science (April 2021-December 2023). He completed his Ph.D. in Artificial Intelligence at NUS under the supervision of Bryan Kian Hsiang Low and Patrick Jaillet. Dr. Dai's research focuses on the intersection of theoretical and practical AI, with particular emphasis on large language models (LLMs) and optimization techniques. His work spans both theoretical foundations of multi-armed bandits and Bayesian optimization, as well as practical applications in LLM inference, including prompt optimization, in-context learning, personalization of LLMs, LLM-based agents, and scaling up test-time computation of LLMs. His research approach often bridges theoretical principles with real-world applications, particularly in AI4Science problems. His recent publications demonstrate a clear trend toward advancing LLM capabilities through optimization techniques, with increasing focus on practical deployment challenges. The research spans both theoretical contributions to optimization theory and applied work on enhancing LLM performance in real-world scenarios. His work on dueling bandits, neural bandits, and zeroth-order optimization has been consistently published in top-tier venues including NeurIPS, ICML, ICLR, and ACL. Presidential Young Fellow, CUHKSZ (2024) Dean's Graduate Research Excellence Award, NUS (2021) Research Achievement Award × 2, NUS (2019 & 2020) Singapore-MIT Alliance Graduate Fellowship (2017) Dr. Dai actively mentors multiple Ph.D. students and research assistants, with several of his students' papers accepted to top conferences. His research has received significant attention, with invitations to serve as Area Chair for NeurIPS 2025 and ICLR 2025, reflecting his growing influence in the machine learning community. His work bridges theoretical machine learning with practical applications in large-scale AI systems.
Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.
Associate Professor Kiat Boon, Daniel SENG is a leading academic at the National University of Singapore (NUS), specializing in technology law and infocommunications law. He currently serves as Director of the LLM (Intellectual Property & Technology Law) program and IT Coordinator. Previously, he held roles as Director of Research at the Singapore Academy of Law (2001–2003), partner and head of technology practice at Rajah & Tann, and non-residential fellow at Stanford Law School’s Center for Legal Informatics (CodeX). His education includes a JSD and JSM from Stanford University, BCL (Oxford, Rupert Cross Prize recipient, 1994), and LLB (NUS with first-class honors). His research employs machine learning, NLP, and big data to analyze digital law challenges, particularly in copyright takedown notices and AI governance. Research interests span information technology law, empirical legal studies, quantitative research, and AI’s intersection with legal reasoning. He contributes to global legal reform via advisory roles at the World Intellectual Property Organization (WIPO), focusing on copyright exceptions, music licensing, and intermediary liability. Key awards include the 1994 Rupert Cross Prize. His work bridges law and technology through publications on digital evidence, metaverse legal frameworks, and AI’s impact on private law. He is actively involved in shaping Singapore’s legal infrastructure through government committees and academic initiatives.
WANG Ye is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD in Information Technology from Tampere University of Technology, Finland, and has been a tenured faculty member at NUS since 2002, following his industry research role at Nokia Research Center. He is the director of the Sound and Music Computing Lab at NUS, leading cutting-edge research in AI-driven music and health technologies. PhD, Information Technology, Tampere University of Technology, Finland (2002) MSc, Telecommunications, Braunschweig University of Technology, Germany (1993) BSc, Telecommunications, South China University of Technology, China (1983) His research is centered on Sound and Music Computing for Human Health and Potential (SMC4HHP) , with a focus on eHealth, eLearning, mobile/wearable computing, and music information retrieval. His work spans AI for stroke rehabilitation, language learning through singing, singing voice synthesis, and automatic music transcription. He has pioneered systems like SLIONS (language learning via karaoke), CocoLyricist (AI co-creation for stroke recovery), and SinTechSVS (expressive singing voice synthesis). The latest articles highlight a strong trend in AI-driven music and health technologies , particularly in controllable lyric generation, singing voice synthesis, automatic pronunciation assessment, and multimodal music transcription. The research increasingly integrates large language models, explainable AI, fairness, and real-world deployment, reflecting a shift from theoretical exploration to practical, human-centered applications in healthcare and education. Dr. Wang has received numerous scientific honors, including: Best Paper Awards at ACM MM, ISMIR, IEEE ISM, and CHI First Prize, Asia Pacific Assistive, Rehabilitative, and Therapeutic Technologies Challenge (2015) Faculty Teaching Excellence Award, NUS School of Computing (2024) Top Paper Award, ACM Multimedia 2022 AI in Medicine Collaborative Grant for CocoLyricist project He has supervised over 11 PhD and 20 MComp students and is currently guiding six PhD candidates. His grants come from MOE, NRF, A*STAR, Nokia, and Smule. He has served as General Chair of ISMIR2017 and TPC Co-Chair of ICOT2017, and is on the editorial boards of IEEE Transactions on Multimedia and Journal of New Music Research. He has also developed and taught the first course on Sound and Music Computing in Singapore. Dr. Wang leads the Sound and Music Computing Lab (SMC Lab) , a multidisciplinary team exploring the synergy of music computing, AI, mobile technology, and cloud systems for health and education. The lab actively collaborates with medical institutions such as NUS Yong Loo Lin School of Medicine, Singapore General Hospital, and Harvard Medical School, and is currently working on projects in AI-supported language learning, stroke rehabilitation, and intelligent music interfaces.
Joel Goh is Associate Professor at the Department of Analytics and Operations, NUS Business School, National University of Singapore. He serves as Director of the J.Y. Pillay Comparative Asia Research Centre (under NUS Global Asia Institute) and PhD Program Director at the Institute of Operations Research and Analytics (IORA). Previously, he was Assistant Professor at Harvard Business School (2014-2017) and Visiting Scholar (2017-2022). BSc, MSc, PhD in Operations, Information, and Technology from Stanford University His research focuses on healthcare analytics (preventing health conditions, hospital operations, frailty assessment), supply chain analytics (digital business models, platform leakage), and service platform operations (hospital-at-home programs, incentive design). He co-created the Robust Optimization Made Easy (ROME) software. Recent publications analyze workplace psychological safety (2024), hospital-at-home models (2024), and platform leakage dynamics (2023). His work spans 18+ journals with 740+ citations for burnout cost studies (2022) and 606+ citations for physician well-being research (2017). Teaching Honors : 2023: Best MBA Teaching & Skinner Innovation Award 2021: NUS Annual Teaching Excellence Award 2020: Early Career Research Excellence Award & 40 Under 40 Best MBA Professors Advising & Grants : Served as PhD Program Director. Received NUS Start-Up Grant R-314-000-110-133 (2021) and Humanities & Social Sciences Fellowship (2021). Editorial roles include Associate Editor at Management Science , Manufacturing & Service Operations Management , and Senior Editor at Production and Operations Management .
Reza Shokri is a Dean's Chair Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research lies at the intersection of data privacy, security, and trustworthy machine learning, with a focus on quantifying privacy risks and developing robust, fair, and interpretable models. PhD in Computer Science, EPFL His research interests center on data privacy and trustworthy machine learning , particularly in the context of deep learning and federated systems. He investigates how machine learning models memorize training data, leading to privacy leakage, and designs frameworks to audit and mitigate such risks. His work bridges theoretical guarantees with practical applications, emphasizing the trade-offs among privacy, fairness, robustness, and utility. His recent publications (2023–2025) reveal a strong trend in analyzing privacy in large language models (LLMs), membership inference attacks, federated learning, and fairness. These works are published in top venues such as NeurIPS, ICML, ICLR, CCS, and FAccT, highlighting his leadership in both AI and security communities. Notable scientific awards include: Asian Young Scientist Fellowship (2023) Intel Outstanding Researcher Award (2023) Best Paper Award, ACM FAccT (2023) IEEE S&P Test-of-Time Award (2021) Caspar Bowden Award for Privacy Enhancing Technologies (2018) NUS Presidential Young Professorship (2019–2023) VMware Early Career Faculty Award (2021) He has advised numerous PhD and Master’s students, many of whom have contributed to high-impact publications. He has also received research grants from major industry partners including Meta, Google, Intel, and VMware. He leads the Data Privacy and Trustworthy Machine Learning Lab at NUS and has served on program committees for top conferences such as IEEE S&P, ACM CCS, and FAccT, including co-chairing roles at HotPETs and Shadow PC of IEEE S&P. He has delivered tutorials at ICML and CCS on privacy auditing in machine learning. His lab focuses on developing tools and frameworks—such as the ML Privacy Meter—for assessing and improving the privacy properties of machine learning models, with applications in regulatory compliance and secure AI deployment.
Seth Lewis Gilbert is a Professor and Head of the Department of Computer Science at the National University of Singapore (NUS), within the School of Computing . He holds the Dean's Chair Associate Professor title and focuses on algorithms for large-scale distributed systems , emphasizing scalability and fault-tolerance . His work spans wireless networks , contention resolution , dynamic networks , and blockchain protocols . Ph.D. in Computer Science, MIT (2007) M.S. in Computer Science, MIT (2003) B.S. in Electrical Engineering & Mathematics, Yale University (1999) His research explores trusted coordination in systems with untrusted and unreliable participants, addressing challenges like Byzantine agreement , load balancing , and contention resolution . He has pioneered formal frameworks for accountability in distributed protocols and developed novel algorithms for asynchronous task allocation . Recent publications include work on consensus protocols , leader election , and contention resolution , reflecting his focus on dynamic network environments . His research has earned recognition at venues like DISC, ICDCS, and CCS. Scientific Awards & Honors : Young Researcher Award (NUS, 2014) Faculty Teaching Excellence Award (2013/14, 2014/15, 2015/16) School of Computing Teaching Excellence Honour Roll (2016-2021) Best Paper Awards at DISC (2023, 2022), ICDCS (2022), IPDPS (2022) Best Student Paper Award at DISC (2022) He serves on the Steering Committee for DISC as Treasurer and has chaired program committees for DISC, OPODIS, and SPAA. His work bridges foundational algorithmic theory with practical applications in blockchains and dynamic networked systems .
Di Zhu is a Presidential Young Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He holds a B.Eng. from Nanyang Technological University and M.Sc./Ph.D. from MIT, both in Electrical Engineering. His postdoctoral research at Harvard focused on lithium niobate integrated photonics and superconducting detectors. He previously worked as a research scientist and PI at A*STAR's Institute of Materials Research and Engineering (IMRE). Research Interests : Integrated quantum photonics, superconducting detectors, nonlinear optics, and nanofabrication. His group develops scalable quantum photonic devices using lithium niobate and superconducting materials, emphasizing applications in quantum computing, communication, and sensing. Awards : National Research Foundation (NRF) Fellowship Harvard Quantum Initiative (HQI) Postdoctoral Fellowship MIT Jin-Au Kong Thesis Award Advising & Recruitment : Actively recruiting postdocs, PhD students, and interns in areas like integrated photonics, quantum optics, and superconducting detectors. Positions include work on thin-film lithium niobate, quantum simulation, and microwave-optical transduction. Group website: dizhulab.org .
Associate Professor Ng Bing Feng leads research in Additive Manufacturing, Aerospace Engineering, and Thermofluids at Nanyang Technological University's School of Mechanical & Aerospace Engineering. As Cluster Director for Smart & Sustainable Building Technologies at NTU's Energy Research Institute, he oversees projects spanning energy-efficient cooling technologies, advanced filtration systems, and bio-inspired materials. His interdisciplinary work integrates advanced manufacturing with environmental engineering, focusing on sustainable solutions for air quality management and thermal regulation. Current research explores radiative cooling technologies, acoustic agglomeration for emissions control, and crashworthy structures via biomimetic design. Professor Ng's publications demonstrate consistent innovation in multi-scale manufacturing and fluid dynamics, with growing emphasis on sustainable urban technologies. Methodologies combine experimental fluid dynamics with computational modeling and AI-driven design. Leadership Roles: Cluster Director, Energy Research Institute @ NTU Principal Investigator for multiple industry collaborations in sustainable materials Research Output: 42+ publications in high-impact journals Multiple patents in filtration technologies and cooling systems
**Dr. JIN Chen** is an **Associate Professor** in the **Department of Information Systems and Analytics** at the **National University of Singapore (NUS) School of Computing**. He holds a PhD in Industrial Engineering and Management Science from Northwestern University (U.S.) and joined NUS in 2018 after postdoctoral work at the Wharton School, University of Pennsylvania. His research focuses on **online platforms**, **information systems operations**, and **digital transformation**, with a particular interest in addressing challenges like fake information dissemination and platform competition. His work bridges **operations management** and **information systems**, emphasizing practical applications in e-commerce and fintech. **Education**: B.Sc. (Shanghai Jiao Tong University) M.Sc. & Ph.D. (Northwestern University, U.S.) **Research Interests**: Platform design and competition Information bundling and pricing strategies Consumer search behavior Right-to-repair policies and environmental impacts **Awards & Recognition** (selected): Finalist, MSOM Service Management SIG Best Paper Award (2024) Nomination for NUS Young Researcher Award (2022–2024) Winner, INFORMS ENRE Young Researcher Prize (2022) Teaching Excellence Awards (2021–2023) **Key Projects**: Optimal information structure design to combat fake orders/reviews Environmental and welfare implications of right-to-repair laws Strategic bundling and pricing in multi-service platforms
Jason CHAN is a Senior Lecturer of Computer Science at the School of Computing and Information Systems (SCIS) within Singapore Management University (SMU). He holds a PhD from the University of Sydney (2009). His research focuses on Technology-Enhanced Learning & Pedagogy, Learning & Work dynamics, and Digital Transformation initiatives. He teaches courses including Programming Fundamentals I and CS Project Experience. Education: PhD, University of Sydney, 2009 Research interests emphasize integrating technology into education and workforce development. His strategic priorities include advancing digital transformation in academic and professional settings. Contact: +65 68264915 | jasonchan@smu.edu.sg Office Locations: 1. 80 Stamford Road, Singapore 178902 2. Level 4, 90 Stamford Road, Singapore 178903 No academic awards or funded grants were explicitly mentioned in the profile. Advising and laboratory affiliations remain unspecified.
