I build real learning systems that has been used in the real world. Most of these projects are actively developed and maintained as open source package. I am honored to work with many outstanding collaborators on these projects.

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

TVM stack is a unified optimization stack that will close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends. The project contains the following components

XGBoost: Scalable Tree Boosting

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. XGBoost was used to solve many machine learning challenges and has been deployed in production. You can use it in any of your favorite language including python, R, Julia, java, scala. The distributed version can be easily deployed on Hadoop, MPI, SGE and more recently DataFlow frameworks(e.g. Flink and Spark)

MXNet: Efficient and Flexible Deep Learning

MXNet stands for mix and maximize. The idea is to combine the power of declartive programming together with imperative programming. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. The library is portable and lightweight, and it scales to multiple GPUs and multiple machines.