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MU Wenchuan

Lecturer
EMAIL
wenchuan_mu
sutd.edu.sg
RESEARCH AREAS
Artificial and Augmented Intelligence
Data Science
Interactive Computing

Biography

Wenchuan Mu is a Lecturer in the ISTD pillar at SUTD. He received his BEng (Hons) in computer engineering from SUTD in 2017 and his PhD in computer science from SUTD in 2022. He has remained affiliated with SUTD as a researcher since 2022. His research interests include natural language processing, machine learning, model robustness, and GPU-accelerated distributed computing systems for large-scale AI infrastructure. His work has been published in venues including the Journal of Big Data, JCDL, CIKM, and IJCNN.

Education
  • 2022 – PhD, Singapore University of Technology and Design
  • 2017 – BEng Hons, Singapore University of Technology and Design
Selected publications
  • Mu, W., Lim, K.H., Liu, J., Karunasekera, S., Falzon, L., Harwood, A., “A clustering-based topic model using word networks and word embeddings,” Journal of Big Data, 9(1):38, 2022.
  • Mu, W., Lim, K.H., “Fast bibliography pre-selection based on dual vector semantic modelling,” Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries (JCDL ’24), ACM, 2024.
  • Mu, W., Lim, K.H., “Bayesian privacy guarantee for user history in sequential recommendation using randomised response,” Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM ’25), ACM, 2025, pp. 5041–5046.
  • Mu, W., Lim, K.H., “Get global guarantees: On the probabilistic nature of perturbation robustness,” Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM ’25), ACM, 2025, pp. 2190–2200.
  • Mu, W., Lim, K.H., “Modelling text similarity: A survey,” Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM ’23), ACM, 2024, pp. 698–705.
  • Mu, W., Lim, K.H., “Towards precise observations of neural model robustness in classification,” Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion ’24), ACM, 2024, pp. 388–389.
  • Mu, W., Lim, K.H., “Explicitly stating assumptions reduces hallucinations in natural language inference,” The Second Tiny Papers Track at ICLR 2024, 2024.