I am currently a 3+5 PhD student in Statistics and Machine Learning at the University of Copenhagen (UCPH). My research focuses on regression, interpretability, and tree-based methods such as Gradient Boosting and Random Forests. I am actively developing a machine learning library in Rust, focused on creating an interpretable "glass-box" model as an alternative to traditional black-box methods. I am also an experienced software engineer proficient in Python and R, with experience in DevOps, developing microservices on Google Cloud and utilizing CI/CD pipelines.
Jinyang Liu, Tessa Steensgaard, Marvin N. Wright, Niklas Pfister, Munir Hiabu (2025)
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:39496-39534, 2025
We provide a novel and fast method for computing partial dependence functions for tree-based prediction models such as XGBoost and Random Forests. The implementation has since been integrated into the R-package glex.
University of Copenhagen (UCPH)
Research focus on interpretable ML, high-dimensional statistics, and tree-based models. Advisor: Munir Hiabu.
University of Copenhagen (UCPH)
Took many advanced statistics courses and developed a strong foundation in measure- and probability theory.