Rate My Professor Feng Chen

Chen's research specializes in nonparametric and semiparametric statistics, point processes and their statistical inference, statistical computing, and financial data modelling. He has developed influential R packages for fitting self-exciting point processes, earning the UNSW Science 2023 Staff Impact Award for Research Innovation due to their adoption in diverse fields. His grants include the Australian Research Council Discovery Project 'Inference for Hawkes processes with challenging data' (2024-2026, $463,452) with Tom Stindl and William Dunsmuir, and a Taiwan Ministry of Science and Technology grant 'Exploring the Impact of Social Media and Internet Use on Suicide Behavior on Youth' (2022-2025). Key publications encompass 'Inference for a nonstationary self-exciting point process with an application in ultra-high frequency financial data modeling' (Chen and Hall, Journal of Applied Probability, 2013), 'Marked Self-Exciting Point Process Modelling of Information Diffusion on Twitter' (Chen and Tan, Annals of Applied Statistics, 2018), 'Estimating the Hawkes process from a discretely observed sample path' (Chen, Stindl, and Kwan, Journal of Computational and Graphical Statistics, 2025), 'Ergodic properties of the Hawkes process with a general excitation kernel' (Kwan, Chen, and Dunsmuir, Journal of Applied Probability, 2025), and 'Stochastic declustering of earthquakes with the spatiotemporal renewal ETAS model' (Stindl and Chen, Annals of Applied Statistics, 2023). Chen supervises postgraduate students in computational statistics, point processes, financial data analysis, and semi- and non-parametric inference. He serves as Associate Editor for Journal of Statistical Planning and Inference since 2019 and ACM Transactions on Probabilistic Machine Learning since 2023, and was Guest Editor for a special issue of Journal of Agricultural, Biological, and Environmental Statistics on Hawkes processes.