🧑‍🎓 About Me

This is Ying Li. I am currently a Research Fellow at the National University of Singapore (NUS), working under the supervision of Prof. Alexandre Thiéry and Prof. Jeremy Heng. I obtained my Ph.D. degree from the School of Computing and Data Science at The University of Hong Kong in 2025, under the supervision of Prof. Michael Zhang. Prior to that, I received my M.Phil. degree from The Chinese University of Hong Kong, Shenzhen in 2022 and my B.Eng. degree from Shandong University in 2019. My research interests include Bayesian nonparametrics, Bayesian deep learning, and their applications to neuroscience.

🔥 News

• 2026.01.26: Paper entitled “Revisiting Nonstationary Kernel Design for Multioutput Gaussian Processes” has been accepted by ICLR 2026.
• 2025.12.05: Paper entitled “Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems” has been accepted by IEEE Transactions on Signal Processing (TSP).
• 2025.09.25: Paper entitled “Multi-View Oriented GPLVM: Expressiveness and Efficiency” has been accepted by NeurIPS 2025.
• 2025.07.12: Paper entitled “Scalable Random Feature Latent Variable Models” has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

📚 Publications

(* indicates equal contributions, † indicates corresponding author).

Manuscript

• A Continuous-Time Perspective on Deep Gaussian Processes
  Ying Li, Sanyou Wu, Zhidi Lin, Zi Yang, Qiaochu Xu, Michael Minyi Zhang, Alexandre H. Thiery, Petar M. Djurić. 2025.
  

• cGPSSM: A Continuous-Time Gaussian Process State-Space Model with Latent SDEs
  Ying Li, Zi Yang, Zhidi Lin, Yuhao Liu, Michael Minyi Zhang, Petar M. Djurić. 2025.
  

• On Model Collapse in Gaussian Process Latent Variable Models
  Ying Li, Zhidi Lin, Yuhao Liu, Michael Minyi Zhang, Feng Yin, Petar M. Djurić. 2025.
  

• Random Feature Gaussian Process Attention: Linear-Time Probabilistic Attention with Calibrated Uncertainty
  Amir Mohammad Mahfoozi, Zi Yang, Ying Li, Michael Minyi Zhang. 2025.
  

Journal

• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems
  Zhidi Lin, Ying Li, Feng Yin, Juan Maroñas, Alexandre H Thiéry. IEEE Transactions on Signal Processing, 2025.
  

• Scalable Random Feature Latent Variable Models
  Ying Li, Zhidi Lin, Yuhao Liu, Michael Minyi Zhang, Pablo M. Olmos, Petar M. Djurić. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
  

• Enhancing Multi-Stream Beamforming Through CQIs For 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme
  Kai Li, Ying Li, Lei Cheng, Zhi-Quan Luo. IEEE Transactions on Wireless Communications, 2024.
  

• Online/Offline Learning to Enable Robust Beamforming: Limited Feedback Meets Deep Generative Models
  Ying Li, Zhidi Lin, Kai Li, Michael Minyi Zhang. Journal of Communications and Networks (JCN), 2025.
  

• Downlink channel covariance matrix reconstruction for FDD massive MIMO systems with limited feedback
  Kai Li, Ying Li, Lei Cheng, Qingjiang Shi, Zhi-Quan Luo. IEEE Transactions on Signal Processing, 2024.
  

Conference

• Revisiting Nonstationary Kernel Design for Multioutput Gaussian Processes
  Qiaochu Xu, Zi Yang, Ying Li†, Michael Minyi Zhang, Pablo M. Olmos. International Conference on Learning Representations (ICLR), 2026.
  

• Multi-View Oriented GPLVM: Expressiveness and Efficiency
  Zi Yang*, Ying Li*, Zhidi Lin, Michael Minyi Zhang, Pablo M. Olmos. Advances in Neural Information Processing Systems (NeurIPS), 2025.
  

• Preventing Model Collapse in Gaussian Process Latent Variable Models
  Ying Li*, Zhidi Lin*, Feng Yin, Michael Minyi Zhang. International Conference on Machine Learning (ICML), 2024.

• Overcoming posterior collapse in variational autoencoders via EM-type training
  Ying Li, Lei Cheng, Feng Yin, Michael Minyi Zhang, Sergios Theodoridis. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
  

• Digital twin-aided learning to enable robust beamforming: Limited feedback meets deep generative models
  Ying Li, Kai Li, Lei Cheng, Qingjiang Shi, Zhi-Quan Luo. 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021.
  

• Learning enhanced beamforming vector from CQIs in 5G NR FDD massive MIMO systems: A tuning-free approach
  Kai Li, Ying Li, Lei Cheng, Qingjiang Shi, Zhi-Quan Luo. 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021.
  

• Pushing the limit of type I codebook for FDD massive MIMO beamforming: A channel covariance reconstruction approach
  Kai Li*, Ying Li*, Lei Cheng, Qingjiang Shi, Zhi-Quan Luo. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4785–4789, 2021.
  

🏫 Services

• Journal Reviewer of TSP
• Conference Reviewer of ICML, NeurIPS, ICLR, AISTATS, ICASSP, FUSION