Hi there! My name is Xing (pronunciation; similar to “H’sing”). I am a PhD student in Statistics at Imperial College London. I joined the EPSRC CDT in Modern Statistics and Statistical Machine Learning at Imperial and Oxford in 2020, where I have been advised by Professor Axel Gandy and Dr Andrew Duncan.
My research interests lie in the intersection of kernel methods and computational statistics. Specifically, I am exploring the applications of Kernelized Stein Discrepancy to address challenges in computational statistics, including areas such as distribution testing, particle-based inference and parameter estimation.
Education
PhD in Modern Statistics and Statistical Machine Learning, 2020-2024, Imperial College London
MASt (Part III) in Mathematical Statistics, 2019-2020, University of Cambridge
BSc in Mathematics with Statistics, 2016-2019, Imperial College London
Resources
Here is an unofficial LaTex poster template for maths/stats projects with a Imperial College theme. See the links therein for references.
Teaching
I am/was a Teaching Assistant for the following courses:
- Autumn 2023: M.Sc. in Statistics Orientation Week. Led by Dr Oliver Ratmann.
- Spring 2023: Mathematical Foundations of Machine Learning. Lectured by Dr Anastasia Borovykh.
- Spring 2022: Exploratory Data Analysis and Visualisation. Lectured by Dr James Martin.
- Autumn 2021: Applicable Maths. Lectured by Dr James Martin.
News
[12/2023] From 17th to 21st, I will attend the 2023 IMS International Conference on Statistics and Data Science (ICSDS) in Lisbon, where I will give a contributed talk on our COLT paper A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing.
[10/2023] Starting from October 2023, I will become an Enrichment Student at the Alan Turing Institute, where I will join the Turing’s research community for six months to broaden my research. Please do not hesitate to reach out if you are interested in collaboration!
[05/2023] Our paper A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing has been accepted by COLT 2023.
[04/2023] Our paper Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy has been accepted by ICML 2023.
[09/2022] I finished my internship at Meta, where I worked on multi-task active learning methods for e-commerce.
[01/2022] Our paper Grassmann Stein Variational Gradient Descent has been accepted by AISTATS 2022.