HighlightsThese highlights are AI-generated using the faculty's CV and Google Scholar profile
Hady W. Lauw is a leading scholar in preference mining, recommender systems, and visual analytics, with extensive contributions to machine learning, web search, and data mining, recognized by prestigious awards and international leadership roles.
Combines methodological innovation in preference mining, explainable recommendation, and multimodal analytics with practical impact in AI-driven recommendation, web search, and visual analytics; recognized for bridging theory and real-world applications, fostering interpretability, and leading collaborative research and open-source frameworks.
Focused research areas include Developing novel models and algorithms for preference mining, recommender systems, and visual analytics; advancing explainable recommendation, comparative explanations, topic modeling, and multimodal learning; bridging human and computational coherence in information retrieval and recommendation.
Combines methodological innovation in preference mining, explainable recommendation, and multimodal analytics with practical impact in AI-driven recommendation, web search, and visual analytics; recognized for bridging theory and real-world applications, fostering interpretability, and leading collaborative research and open-source frameworks.
Focused research areas include Developing novel models and algorithms for preference mining, recommender systems, and visual analytics; advancing explainable recommendation, comparative explanations, topic modeling, and multimodal learning; bridging human and computational coherence in information retrieval and recommendation.
Areas of Expertise
Web MiningRepresentation LearningRecommender Systems
Past Awarded Grant
- Slide++: Automatic Augmentation of Academic Slides Towards AI-Enabled Student-Centred Learning, Tertiary Education Research Fund (TRF), Ministry of Education (MOE), PI (Project Level): Hady W. LAUW, 2022, S$262,196
- Lifelong Learning for Recommender Systems: Continual, Cross-Domain, and Cross-Platform Approaches, Research Programme, AI Singapore, PI (Project Level): Hady W. LAUW, 2022, S$885,240
- Universal Pre-training of Graph Neural Networks, Academic Research Fund (AcRF) Tier 2, Ministry of Education (MOE), PI (Project Level): FANG Yuan, Co-PI (Project Level): Jing JIANG, 2022, S$676,468
- One-Shot Learning: A Crucial Learning Paradigm Towards Human-like Learning, AI Singapore Research Programme, AI Singapore, PI (Project Level): FANG Yuan, Co-PI (Project Level): Steven HOI, 2018, S$497,757.6
- Dimensionality Reduction for Recommender Systems: Unified Latent Co-representation of Multi-Modal Preference Signals, NRF Fellowship, National Research Foundation (NRF), PI (Project Level): Hady W. LAUW, 2016, S$2,704,332
Latest Publications
Showing up to 6 latest publications from the past 5 years.
- K Theocharidis, HW Lauw, P KarrasIEEE Transactions on Computational Social Systems, 2026
- NT Tran, HW LauwProceedings of the 33rd ACM International Conference on Multimedia, 6412-6420, 2025
- A Ledent, P Kasalický, R Alves, HW LauwIEEE Transactions on Neural Networks and Learning Systems, 2025
- HW Lauw, M Najork, E Terzi, P TsaparasACM Transactions on Intelligent Systems and Technology 16 (5), 1-3, 2025
- NT Tran, HW LauwThe 41st Conference on Uncertainty in Artificial Intelligence, 2025
- K Theocharidis, HW LauwACM SIGKDD Explorations Newsletter 27 (1), 32-51, 2025
Qualification
- PhD, Nanyang Technological University, 2008
Teaching Topics
- Computational Thinking
- IS Application Project
