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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.

• Natural Language Processing
• Large Language Models
• Digital Libraries
• +7 more

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.

• Sound and Music Computing
• Music Information Retrieval
• eHealth
• +12 more

LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.

• Machine Learning
• Planning Under Uncertainty
• Approximate Inference
• +3 more

Natalie Agus is a Senior Lecturer at the Singapore University of Technology and Design (SUTD), specifically within the ISTD (Information Systems Technology and Design) pillar. Her office is located at 1.402.28. She specializes in teaching undergraduate computing courses, including programming languages such as Python, Java, C#, and C/C++. Agus is dedicated to innovating teaching methods to enhance student engagement with academic materials. Her research focuses on room acoustics and music information retrieval, with a particular emphasis on binaural audio processing. Her doctoral work centered on developing perceptually realistic real-time binaural room reverberation algorithms. Agus holds a B.Eng. (Robotics, 2015) and a Ph.D. (Digital Audio Signal Processing, 2018), both from SUTD. Her recent publications explore topics such as binaural room modeling, perceptual evaluation of spectral variance, and energy-based acoustic simulation techniques. She actively participates in collaborative research projects within the audio engineering domain.

• Interactive Computing
• Room Acoustics
• Music Information Retrieval
• +3 more

Min-Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore (NUS), with a focus on the Department of Computer Science. He earned his Ph.D., M.Sc., and B.S. in Computer Science from Columbia University. Associate Professor, NUS Department of Computer Science Vice Dean of Undergraduate Studies, NUS School of Computing His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with applications in Human-Computer Interaction , Web Systems , and Recommendation Technologies . He leads the Web, Information Retrieval, and NLP Group (WING.NUS), driving projects in scholarly document analysis, dialogue systems, and AI ethics. Recent publications highlight his work on LLM alignment , multimodal deception detection , and evaluation frameworks . Key trends include ethical NLP, knowledge aggregation in vision-language models, and combating misinformation through AI. Scientific Awards: Best Paper Award, CIKM 2019 Distinguished Service Awards, ACL Vannevar Bush Best Paper Award, JCDL 2012 As a PhD advisor , his students have secured roles at institutions like Google and USTC. He also serves on the ACL Ethics Committee and as associate editor for Information Retrieval and JAIR .

• Natural Language Processing
• Large Language Models
• Digital Libraries
• +6 more

Rudy Setiono is an Associate Professor and Assistant Dean of Graduate Studies at the School of Computing, National University of Singapore (NUS). He has been with NUS since August 1990, following the completion of his Ph.D. in Computer Science from the University of Wisconsin-Madison. Previously, he served as Vice Dean (Undergraduate Affairs) from November 2001 to July 2005 at the School of Computing. B.Sc. in Computer Science from Eastern Michigan University (1984) M.Sc. in Computer Science from University of Wisconsin-Madison (1986) Ph.D. in Computer Science from University of Wisconsin-Madison (1990) Professor Setiono's research focuses on neural networks, particularly in rule extraction, neural network construction and pruning, and applications in optimization. His work spans theoretical foundations to practical implementations in credit scoring, poverty analysis, and business intelligence. He has made significant contributions to making neural networks more interpretable through rule extraction techniques, bridging the gap between black-box models and transparent decision systems. His recent publications show a strong trend toward practical applications of neural network rule extraction in credit scoring, poverty analysis, and document processing. The research demonstrates consistent evolution from theoretical neural network construction to real-world applications across finance, social sciences, and business analytics, with an emphasis on model interpretability and practical implementation. Senior Member of IEEE Associate Editor of IEEE Transactions on Neural Networks (2000-2005) Professor Setiono has supervised numerous research projects and taught courses including BT4103 Business Analytics Capstone Project, BT4240 Machine Learning for Predictive Data Analytics, IS5152 Data-Driven Decision Making, and IS4240 Business Intelligence Systems. His work has been published in reputable journals including IEEE Transactions on Neural Networks, IEEE Transactions on Data and Knowledge Engineering, and Neurocomputing.

• Data Science
• Business Analytics
• Intelligent Systems
• +4 more

Wang Zhaoxia is an Associate Professor of Computer Science (Practice) at the School of Computing and Information Systems, Singapore Management University. Her expertise lies in Artificial Intelligence, Data Science, and Machine Learning with a focus on Natural Language Processing (NLP), Sentiment Analysis, and Knowledge Graphs. She holds a PhD from Nankai University (2004). Research Interests: Her work spans AI-driven solutions for sentiment analysis, maritime risk management, audio-to-music conversion, and social media analytics. Recent projects include leveraging Large Language Models (LLMs) for tasks like logical reasoning, translation optimization, and risk assessment. Publications: Recent work highlights include developing metrics for LLM performance (RAP), emotion analysis in images, and automated music transcription. Her research demonstrates cross-disciplinary applications of AI in fields like finance (ICO success prediction) and health (disease surveillance). Awards: No awards explicitly listed Advising: Supervises 3 PhD/Master students including HUANG Donghao and Nicole Anne Hui Ying TEO Contact: zxwang@smu.edu.sg | +65 68261347

• Artificial Intelligence
• Data Science
• Machine Learning
• +7 more

Dr. Kat Agres is an Assistant Professor in Contextual Studies – Music Cognition at the Yong Siew Toh Conservatory of Music, National University of Singapore, and a Research Scientist & founder of the Music Cognition group at A*STAR’s Institute of High Performance Computing (IHPC). Her work bridges cognitive science, music technology, and healthcare, focusing on music perception, computational creativity, and music-based MedTech solutions for health and well-being. She holds a PhD in Experimental Psychology from Cornell University and conducted postdoctoral research at Queen Mary University of London, supported by an EU grant. Her research explores auditory statistical learning, music-brain interfaces (BCIs), and the therapeutic applications of music for special populations. Key grants include NIH and NIMH fellowships. Agres actively collaborates across disciplines, presenting globally and publishing in journals like Cognitive Science and Neural Computing and Applications . Beyond academia, she is a cellist with professional orchestral experience and integrates jazz and rock performance into her creative practice. Educational Background PhD in Experimental Psychology, Cornell University (with minor in Cognitive Science) Bachelor’s in Cognitive Psychology & Cello Performance, Carnegie Mellon University Postdoctoral Fellowship, Queen Mary University of London Research Interests Music cognition mechanisms (e.g., memory, expectation) Computational models of creativity and emotion in music Music technology for healthcare (e.g., stroke rehabilitation, dementia therapy) Music’s role in mental health and social connection Grants & Awards Fellowship from the National Institute of Health (NIH) Fellowship from the National Institute of Mental Health (NIMH) Labs & Teams Founder of the Music Cognition group at IHPC/A*STAR

• Music Cognition
• Computational Creativity
• Music Technology
• +5 more