Bio
Xing Liu is a third-year PhD student at Imperial College London, specialising in statistical machine learning and kernel methods. He joined the StatML CDT in 2020, working under the supervision of Prof. Axel Gandy and Dr. Andrew Duncan. Prior to his PhD study, Xing completed a Part III in mathematical statistics at the University of Cambridge and obtained a Bachelor's degree in the Department of Mathematics at Imperial College.
Research interests
Distribution testing plays a crucial role in various scientific applications, helping us evaluate the accuracy of statistical models and make informed decisions based on them. In the face of increasingly large and complex datasets, the combination of distribution tests with kernel methods has emerged as a promising approach. Xing's research focuses on overcoming the limitations of kernel-based distribution tests in challenging data scenarios. His interests lie in advancing the theoretical foundations of existing tools and devising novel methodologies that are more scalable to big and complex datasets. By enhancing scalability and adaptability, Xing aims to contribute to the development of more robust and effective distribution testing techniques.