Mao Yang

About

I (Mao Yang, 杨懋) received my Ph.D degree in computer science from Beijing University, China, in 2006. Before that, I received my B.S., M.S. in computer science from Harbin Institute of Technology, Harbin, in 2000 and 2002, respectively. Since 2006, I have been with Microsoft Research Asia, Beijing, as researcher manager for Systems and Networking Research Group (Asia) (opens in new tab)

My research interests are in distributed systems, information retrieval systems, machine learning systems, and multimedia systems, especially for design, implement and deploy practical systems.

I am also an architect, and I worked on the following projects at Microsoft BING (opens in new tab) team:

• The design and implementation of Cougar, a new ranking system for supporting the-state-of-art semantic ranking models. The system starts to serve all web queries since from 2013.
• The design and implementation of Tiger, a new generation flash memory based index serving platform, and the system starts to serve all web queries since from 2012.
• The design and implementation of replication and fail over protocol of Kirin, a new web store and processing system, and the system starts to process many billions of web data since from 2010.
• Proposed a Web scale Q&A system that build into Web search engine. The system starts to provide directly answers in Bing since from 2016.

Some other research projects I’ve worked on include：

• TLA Made Live: a formal method to build distributed systems.
• The design and implementation of a large scale distributed storage system prototype PacificA. The protocol is also used by several open source projects, such as rDSN (opens in new tab), Kafka (opens in new tab).
• Reconfiguration protocol for a paxos based replication state machine library.

My current research focus is on the AI infrastructure and Tools, and algorithms for Web search. We released several projects:

• OpenPAI (opens in new tab) : An open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.
• NNI (neural network intelligence) (opens in new tab) : An open source AutoML toolkit for neural architecture search and hyper-parameter tuning.
• MMdnn (opens in new tab): A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models.
• Visual Studio Tools for AI (opens in new tab) / Visual Studio Code Tools for AI (opens in new tab) and GitHub repo (opens in new tab) : An extension for Visual Studio and Visual Studio Code to build, test, and deploy Deep Learning / AI solutions.

杨懋博士现任微软亚洲研究院副院长，领导微软亚洲研究院在计算机系统和网络领域的研究工作。

杨懋博士于2006年加入微软亚洲研究院，主要从事分布式系统、搜索引擎系统和深度学习系统的研究、设计与实现。同时领导团队在计算机系统、计算机安全、计算机网络、异构计算、边缘计算和系统算法等方向进行关键技术研究。团队及个人在OSDI、SOSP、NSDI、SIGCOMM、ATC等计算机系统和网络的顶级会议上持续发表多篇论文。团队在研究的同时还注重与实际计算机和网络系统的演进结合，与Azure云计算、Bing搜索引擎系统、Windows操作系统、SQL Server数据库系统以及多个开源社区密切合作。杨懋博士同时还是中国科学技术大学博士生导师。

杨懋博士拥有北京大学计算机体系结构专业博士学位以及哈尔滨工业大学硕士和学士学位。