I am a first-year master's student at the Department of Computer Science, ETH Zurich. I am broadly interested in computer systems research, especially in distributed systems, cloud computing, operating systems and programming languages. I am extremely excited about building underlying systems for emerging new applications, as well as designing and analyzing systems from a theoretical point of view.
I received my bachlor's degree in the Computer Science Experimental Class of Harbin Institute of Technology (HIT), Harbin, China in July 2017. Playing with all kinds of novel technologies while failing to go deep in any direction, I had been a member of the HIT IBM Technology Center (a.k.a. FoOTOo Lab) since the summer of my freshman year. Having studied as a semester exchange student in the Department of Computer Science, The University of Hong Kong (HKU) in my junior year, I found my great interests in systems and programming languages. During the final-year internship in the Systems Research Group of Microsoft Research Asia (MSRA), I researched on distributed machine learning systems with excitement under the guidance of Dr. Lintao Zhang, Dr. Jilong Xue and Dr. Hucheng Zhou, which made me determined to pursue an academic career and dream for a faculty position.[CV] [中文简历] [GitHub] [LinkedIn] [Email]
A distributed machine learning framework based on Gradient Boosting Decision Tree (GBDT, GBRT, GBM or MART). The current version is built on top of ChaNa, the RDMA-optimized distributed computing engine developed by MSRA Systems Group. With both system-level and algorithm-level optimizations, memory usage and communication cost are reduced, and the performance is better than state-of-the-art tools (e.g. LightGBM and XGBoost). A Spark version of the framework has been opensourced on GitHub as SparkTree.