Dan Liu received the B.E. degree in Mathematics and Ph.D. degree in Mechanical Engineering from the Harbin Institute of Technology. He is currently a research associate of mechanical engineering with the School of Mechatronics Engineering, Harbin Institute of Technology. He has published more than 9 SCI papers, including 7 CAS TOP papers, and has been cited more than 260 times. His research interests include machine learning powered fault diagnosis, prognostics, and health management of mechanical and electrical systems. —Research associate, Harbin Institute of Technology, 2025 to present —1. Liu D, Zhong S, Lin L, et al. Highly imbalanced fault diagnosis of gas turbines via clustering-based downsampling and deep siamese self-attention network[J]. Advanced Engineering Informatics, 2022, 54: 101725. —2. Liu D, Zhong S, Lin L, et al. Feature-level SMOTE: Augmenting fault samples in learnable feature space for imbalanced fault diagnosis of gas turbines[J]. Expert Systems with Applications, 2024, 238: 122023. —3. Liu D, Zhong S, Lin L, et al. Deep attention SMOTE: Data augmentation with a learnable interpolation factor for imbalanced anomaly detection of gas turbines[J]. Computers in Industry, 2023, 151: 103972. —4. Liu D, Zhong S, Lin L, et al. HOOST: A novel hyperplane-oriented over-sampling technique for imbalanced fault detection of aero-engines[J]. Knowledge-Based Systems, 2024, 300: 112142.