XGBoost has been developed and used by a group of active community. Everyone is more than welcomed to is a great way to make the project better and more accessible to more users.

The Project Management Committee(PMC) consists group of active committers that moderate the discussion, manage the project release, and proposes new committer/PMC members.

• Tianqi Chen, University of Washington
  • Tianqi is a Ph.D. student working on large-scale machine learning. He is the creator of the project.
• Michael Benesty
  • Michael is a lawyer and data scientist in France. He is the creator of XGBoost interactive analysis module in R.
• Yuan Tang, Red Hat
  • Yuan is a principal software engineer at Red Hat. He contributed mostly in R and Python packages.
• Nan Zhu, Uber
  • Nan is a software engineer in Uber. He contributed mostly in JVM packages.
• Jiaming Yuan
  • Jiaming contributed to the GPU algorithms. He has also introduced new abstractions to improve the quality of the C codebase.
• Hyunsu Cho, NVIDIA
  • Hyunsu is the maintainer of the XGBoost Python package. He also manages the Jenkins continuous integration system (https://xgboost-ci.net/). He is the initial author of the CPU 'hist' updater.
• Rory Mitchell, University of Waikato
  • Rory is a Ph.D. student at University of Waikato. He is the original creator of the GPU training algorithms. He improved the CMake build system and continuous integration.
• Hongliang Liu

Committers are people who have made substantial contribution to the project and granted write access to the project.

• Tong He, Amazon AI
  • Tong is an applied scientist in Amazon AI. He is the maintainer of XGBoost R package.
• Vadim Khotilovich
  • Vadim contributes many improvements in R and core packages.
• Bing Xu
  • Bing is the original creator of XGBoost Python package and currently the maintainer of XGBoost.jl.
• Sergei Lebedev, Criteo
  • Sergei is a software engineer in Criteo. He contributed mostly in JVM packages.
• Scott Lundberg, University of Washington
  • Scott is a Ph.D. student at University of Washington. He is the creator of SHAP, a unified approach to explain the output of machine learning models such as decision tree ensembles. He also helps maintain the XGBoost Julia package.
• Egor Smirnov, Intel
  • Egor has led a major effort to improve the performance of XGBoost on multi-core CPUs.

XGBoost is a open source project and we are actively looking for new committers who are willing to help maintaining and lead the project. Committers comes from contributors who:

• Made substantial contribution to the project.
• Willing to spent time on maintaining and lead the project.

New committers will be proposed by current committer members, with support from more than two of current committers.

• Full List of Contributors
  • To contributors: please add your name to the list when you submit a patch to the project:)
• Kailong Chen
  • Kailong is an early contributor of XGBoost, he is creator of ranking objectives in XGBoost.
• Skipper Seabold
  • Skipper is the major contributor to the scikit-learn module of XGBoost.
• Zygmunt Zając
  • Zygmunt is the master behind the early stopping feature frequently used by Kagglers.
• Ajinkya Kale
• Boliang Chen
• Yangqing Men
  • Yangqing is the creator of XGBoost java package.
• Engpeng Yao
• Giulio
  • Giulio is the creator of Windows project of XGBoost
• Jamie Hall
  • Jamie is the initial creator of XGBoost scikit-learn module.
• Yen-Ying Lee
• Masaaki Horikoshi
  • Masaaki is the initial creator of XGBoost Python plotting module.
• daiyl0320
  • daiyl0320 contributed patch to XGBoost distributed version more robust, and scales stably on TB scale datasets.
• Huayi Zhang
• Johan Manders
• yoori
• Mathias Müller
• Sam Thomson
• ganesh-krishnan
• Damien Carol
• Alex Bain
• Baltazar Bieniek
• Adam Pocock
• Gideon Whitehead
• Yi-Lin Juang
• Andrew Hannigan
• Andy Adinets
• Henry Gouk
• Pierre de Sahb
• liuliang01
  • liuliang01 added support for the qid column for LIBSVM input format. This makes ranking task easier in distributed setting.
• Andrew Thia
  • Andrew Thia implemented feature interaction constraints
• Wei Tian
• Chen Qin
• Sam Wilkinson
• Matthew Jones
• Jiaxiang Li
• Bryan Woods
  • Bryan added support for cross-validation for the ranking objective
• Haoda Fu
• Evan Kepner
  • Evan Kepner added support for os.PathLike file paths in Python