Dr Xiongjie Chen

Postdoctoral Researcher
B.Sc, M.Eng, PhD


Areas of specialism

Statistical machine learning; Bayesian inference; Monte Carlo sampling; Uncertainty quantification; AI in dentistry


Research interests

Research projects




An up-to-date list of my publications can be found on my Google Scholar profile.

Xiongjie Chen, Yunpeng Li (2022)Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow, In: 2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022)pp. 782-786 IEEE

Tuning of measurement models is challenging in real-world applications of sequential Monte Carlo methods. Recent advances in differentiable particle filters have led to various efforts to learn measurement models through neural networks. But existing approaches in the differentiable particle filter framework do not admit valid probability densities in constructing measurement models, leading to incorrect quantification of the measurement uncertainty given state information. We propose to learn expressive and valid probability densities in measurement models through conditional normalizing flows, to capture the complex likelihood of measurements given states. We show that the proposed approach leads to improved estimation performance and faster training convergence in a visual tracking experiment.

Wenhan Li, Xiongjie Chen, Wenwu Wang, Victor Elvira, Yunpeng Li (2023)Differentiable Bootstrap Particle Filters for Regime-Switching Models, In: 2023 IEEE Statistical Signal Processing Workshop (SSP)2023-pp. 200-204 IEEE

Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models. In real-world applications, both the state dynamics and measurements can switch between a set of candidate models. For instance, in target tracking, vehicles can idle, move through traffic, or cruise on motorways, and measurements are collected in different geographical or weather conditions. This paper proposes a new differentiable particle filter for regime-switching state-space models. The method can learn a set of unknown candidate dynamic and measurement models and track the state posteriors. We evaluate the performance of the novel algorithm in relevant models, showing its great performance compared to other competitive algorithms.