Profile picture

3+5 PhD student

University of Copenhagen
Universitetsparken 5 (Department of Mathematical Sciences)

2100 Copenhagen

GitHub LinkedIn jl (at) math.ku.dk

About

– Born on 14. August 2002 in Shenzhen, China

I am currently a 3+5 PhD student in statistics and machine learning at the University of Copenhagen (UCPH). My research focuses on regression, intepretability, and tree-based methods such as gradient boosting and random forests. I hold a Bachelor’s degree in Mathematics from UCPH (June 2023), where I specialized in statistics and developed a strong foundation in measure and probability theory.

In addition, I am an experienced software engineer and have worked extensively with DevOps. I am proficient in Python and R, which I have used to develop microservices on Google Cloud.

I am developing a machine learning library for regression in Rust, focusing on creating an interpretable “glass-box” model as an alternative to traditional black-box methods.


Education

Research

, ,
Fast Estimation of Partial Dependence Functions using Trees
arXiv preprint, 2024.
We provide a novel and fast method for computing partial dependence funtions for tree-based prediction models such as XGBoost and Random Forests. The implementation has since been integrated in the R-package glex.