人员

I am an Assistant Professor in the Department of Mathematics and Information Technology at The Education University of Hong Kong (EdUHK). My research lies at the intersection of statistical signal processing, probabilistic machine learning, and dynamical systems, with a focus on uncertainty-aware modeling, inference, and decision-making.

I completed my PhD at The Chinese University of Hong Kong, Shenzhen in 2024 under the supervision of Prof. Feng Yin and Prof. Shuguang (Robert) Cui. After my Ph.D., I held postdoctoral research positions at the National University of Singapore and The University of Hong Kong, where I worked with Prof. Alexandre Thiéry, Prof. Jeremy Heng, and Prof. Edwin Fong.

For more information, please visit my personal webpage.

• Probabilistic Machine Learning
• Statistical Signal Processing
• Dynamical Systems and State-Space Models
• Bayesian Inference and Data Assimilation
• Gaussian Processes
• Uncertainty Quantification

(∗: Co-first author,  †: Corresponding author)

• Z. Lin, Y. Li, F. Yin, J. Maroñas, and A. H. Thiéry, “Efficient transformed Gaussian process state-space models for high-dimensional non-stationary dynamical systems,” IEEE Transactions on Signal Processing, vol. 73, pp. 5229–5243, Dec. 2025.
• Z. Lin, Y. Sun, F. Yin, and A. H. Thiéry, “Ensemble Kalman filtering meets Gaussian process SSM for non-mean-field and online inference,” IEEE Transactions on Signal Processing, vol. 72, pp. 4286–4301, Aug. 2024.
• R. C. Suwandi∗, Z. Lin∗, F. Yin, Z. Wang, and S. Theodoridis, “Sparsity-aware distributed learning for Gaussian processes with linear multiple kernel,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 8, pp. 14869–14883, Jan. 2025.
• Y. Li∗, Z. Lin∗, F. Yin, and M. M. Zhang, “Preventing model collapse in Gaussian process latent variable models,” in International Conference on Machine Learning (ICML), PMLR, Jul. 2024.
• Y. Li, Z. Lin†, Y. Liu, M. M. Zhang, P. M. Olmos, and P. M. Djurić, “Scalable random feature latent variable models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 11, pp. 9813–9826, Jul. 2025.
• Z. Yang, Y. Li, Z. Lin†, M. M. Zhang, and P. M. Olmos, “Multi-view oriented GPLVM: Expressiveness and efficiency,” in Conference on Neural Information Processing Systems (NeurIPS), Dec. 2025.
• Z. Lin, J. Maroñas, Y. Li, F. Yin, and S. Theodoridis, “Towards efficient modeling and inference in multidimensional Gaussian process state-space models,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Apr. 2024, 12881–12885.
• Z. Lin, L. Cheng, F. Yin, L. Xu, and S. Cui, “Output-dependent Gaussian process state-space model,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Jun. 2023, 1–5.