Professor Xiaoli Li and co‑authors receive the DASFAA 2026 10+ year best paper award for impactful contributions to predictive maintenance research
Professor Xiaoli Li and co‑authors receive the DASFAA 2026 10+ year best paper award for impactful contributions to predictive maintenance research
The Information Systems Technology and Design (ISTD) pillar of Singapore University of Technology and Design (SUTD) is pleased to share that Prof. Xiaoli Li has been awarded the 10+ Year Best Paper Award at the DASFAA 2026 , a leading international conference in data management and data science known for its strong academic and industry impact. The award ceremony was held in Jeju Island on 29 April 2026.
The award recognizes the long-term impact of the paper titled “Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life” (2016), co-authored with G. Sateesh Babu and Peilin Zhao. The work has received over 1,300 citations, reflecting its sustained influence in the field. The DASFAA 10+ Year Best Paper Award is highly selective and is conferred only on papers that demonstrate significant long-term research impact and achieve exceptional citation performance.
This research introduced the earliest deep learning approaches for remaining useful life (RUL) estimation, a core problem in predictive maintenance. By designing novel deep convolutional neural networks to automatically extract features from time-series data, the work laid important foundations for subsequent advances in equipment health monitoring and degradation modeling. Over the years, this direction has gained substantial traction, influencing both academic research and real-world industrial applications.
The ISTD pillar congratulates Prof. Li and collaborators on this significant achievement, which highlights the importance of sustained, high-impact research in data science and artificial intelligence.
Reference:
- DASFAA 2026 – The 31st International Conference on Database Systems for Advanced Applications : https://dasfaa2026.github.io/program/awards.html