Song Li, PhD student
Song Li was employed as a PhD student at the Department of Mathematical Sciences 1 Nov. 2019. Song is part of the section for Statistics and Probability Theory, and his main supervisor is Professor Carsten Wiuf.
Song holds a Master’s degree in Statistics from the Chinese Academy of Sciences. He has worked in a private robot company and taken part in a wind data project with an electric power research institute.
He is interested in statistical methods in finance, economics, genomics, biostatistics, high-dimensional statistics, machine learning, data-analytic modeling, and time series, among others. His primary research focuses on developing and justifying statistical methods that are used to solve problems from the frontiers of scientific research. This is expanded into other disciplines where the statistics discipline is useful.
- In these areas, I devote most of my efforts to the search for computational feasible, model-free, robust nonparametric approaches and illustrate the approaches by real data and simulated examples. I’m also very interested in developing foundational statistical theory and in providing fundamental insights to sophisticated statistical models, said Song.
Recently, he is particularly interested in statistical models and inference of high-dimensional genetic data. The aim of his PhD project “Models and inference from ancient protein data using mass-spec” is to explore and develop mathematical models and statistics for ancient protein data in collaboration with experimental partners. Ancient protein data enables the possibility to learn about human life and conditions from one million years ago.
Song says he is more comfortable being called Gin, because he often writes articles and blog postings under Gin. You can find Song/Gin in office 04.3.28