Contributors of DMLC/XGBoost
============================
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.

Committers
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Committers are people who have made substantial contribution to the project and granted write access to the project.
* [Tianqi Chen](https://github.com/tqchen), University of Washington
  - Tianqi is a Ph.D. student working on large-scale machine learning. He is the creator of the project.
* [Tong He](https://github.com/hetong007), Amazon AI
  - Tong is an applied scientist in Amazon AI. He is the maintainer of XGBoost R package.
* [Vadim Khotilovich](https://github.com/khotilov)
  - Vadim contributes many improvements in R and core packages.
* [Bing Xu](https://github.com/antinucleon)
  - Bing is the original creator of XGBoost Python package and currently the maintainer of [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl).
* [Michael Benesty](https://github.com/pommedeterresautee)
  - Michael is a lawyer and data scientist in France. He is the creator of XGBoost interactive analysis module in R.
* [Yuan Tang](https://github.com/terrytangyuan), Ant Financial
  - Yuan is a software engineer in Ant Financial. He contributed mostly in R and Python packages.
* [Nan Zhu](https://github.com/CodingCat), Uber
  - Nan is a software engineer in Uber. He contributed mostly in JVM packages.
* [Sergei Lebedev](https://github.com/superbobry), Criteo
  - Sergei is a software engineer in Criteo. He contributed mostly in JVM packages.
* [Hongliang Liu](https://github.com/phunterlau)
* [Scott Lundberg](http://scottlundberg.com/), 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.
* [Rory Mitchell](https://github.com/RAMitchell), 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. 
* [Hyunsu Cho](http://hyunsu-cho.io/), Amazon AI
  - Hyunsu is an applied scientist in Amazon AI. He 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. 
* [Jiaming](https://github.com/trivialfis)
  - Jiaming contributed to the GPU algorithms. He has also introduced new abstractions to improve the quality of the C++ codebase.

Become a Committer
------------------
XGBoost is a opensource 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.

List of Contributors
--------------------
* [Full List of Contributors](https://github.com/dmlc/xgboost/graphs/contributors)
  - To contributors: please add your name to the list when you submit a patch to the project:)
* [Kailong Chen](https://github.com/kalenhaha)
  - Kailong is an early contributor of XGBoost, he is creator of ranking objectives in XGBoost.
* [Skipper Seabold](https://github.com/jseabold)
  - Skipper is the major contributor to the scikit-learn module of XGBoost.
* [Zygmunt Zając](https://github.com/zygmuntz)
  - Zygmunt is the master behind the early stopping feature frequently used by kagglers.
* [Ajinkya Kale](https://github.com/ajkl)
* [Boliang Chen](https://github.com/cblsjtu)
* [Yangqing Men](https://github.com/yanqingmen)
  - Yangqing is the creator of XGBoost java package.
* [Engpeng Yao](https://github.com/yepyao)
* [Giulio](https://github.com/giuliohome)
  - Giulio is the creator of Windows project of XGBoost
* [Jamie Hall](https://github.com/nerdcha)
  - Jamie is the initial creator of XGBoost scikit-learn module.
* [Yen-Ying Lee](https://github.com/white1033)
* [Masaaki Horikoshi](https://github.com/sinhrks)
  - Masaaki is the initial creator of XGBoost Python plotting module.
* [daiyl0320](https://github.com/daiyl0320)
  - daiyl0320 contributed patch to XGBoost distributed version more robust, and scales stably on TB scale datasets.
* [Huayi Zhang](https://github.com/irachex)
* [Johan Manders](https://github.com/johanmanders)
* [yoori](https://github.com/yoori)
* [Mathias Müller](https://github.com/far0n)
* [Sam Thomson](https://github.com/sammthomson)
* [ganesh-krishnan](https://github.com/ganesh-krishnan)
* [Damien Carol](https://github.com/damiencarol)
* [Alex Bain](https://github.com/convexquad)
* [Baltazar Bieniek](https://github.com/bbieniek)
* [Adam Pocock](https://github.com/Craigacp)
* [Gideon Whitehead](https://github.com/gaw89)
* [Yi-Lin Juang](https://github.com/frankyjuang)
* [Andrew Hannigan](https://github.com/andrewhannigan)
* [Andy Adinets](https://github.com/canonizer)
* [Henry Gouk](https://github.com/henrygouk)
* [Pierre de Sahb](https://github.com/pdesahb)
* [liuliang01](https://github.com/liuliang01)
  - liuliang01 added support for the qid column for LibSVM input format. This makes ranking task easier in distributed setting.
* [Andrew Thia](https://github.com/BlueTea88)
  - Andrew Thia implemented feature interaction constraints
* [Wei Tian](https://github.com/weitian)
* [Chen Qin] (https://github.com/chenqin)