I joined System Research Group at Microsoft Research Aisa after obtaining my Ph.D from Tsinghua University at June, 2011.
My research interests include large-scale learning system, data-parallel computing, big data, machine learning, mobile computing, program analysis, compiler optimization, computer architecture and tool development.
In the last three years, I mainly participated in research on optimizing the performance, and reducing the failure rate thus improving the user experience in distributed data-parallel computing. The research is inter-discipline among distributed data-parallel computing, database processing and program analysis.
Currently, I am more interested in building a scalable, efficient, fault-tolerant and easy-to-use distributed learning system, with the belief that learning system could be built on top of existing data-parallel execution engine, thus is treated as learning library. In this way, the entire machine learning pipeline, such as feature preparing, training, online learning and model serving, can be supported by one single platform like Apache Spark.
Before joining Microsoft Research, my interests include compiler optimization, computer architecture, and tool development. My PLDI paper on partial redundancy elimination could be treated as the final episode in that area. I have two years internship experience in Google (Mountain View), where I have developed a series of compiler optimizations both for data-center applications and Android system.
- Bokai Cao, Hucheng Zhou, Guoqiang Li, and Philip S. Yu, Multi-view Machines, WSDM, February 2016.
- Songtao He, Yunxin Liu, and Hucheng Zhou, Optimizing Smartphone Power Consumption through Dynamic Resolution Scaling, ACM – Association for Computing Machinery, September 2015.
- Rui DING, Hucheng ZHOU, Jian-Guang LOU, Hongyu ZHANG, Qingwei LIN, Qiang FU, Dongmei ZHANG, and Tao XIE, Log2: A Cost-Aware Logging Mechanism for Performance Diagnosis, USENIX Annual Technical Conference, July 2015.
- Hucheng Zhou, Jian-Guang Lou, Hongyu Zhang, Haibo Lin, Haoxiang Lin, and Tingting Qin, An Empirical Study on Quality Issues of Production Big Data Platform, ICSE SEIP, May 2015.
- Chang Liu, Jiaxing Zhang, Hucheng Zhou, Sean McDirmid, Zhenyu Guo, and Thomas Moscibroda, Automating Distributed Partial Aggregation, in 5th ACM Symposium on Cloud Computing (SOCC), ACM – Association for Computing Machinery, November 2014.
- Xiang Chen, Kent W. Nixon, Hucheng Zhou, Yunxin Liu, and Yiran Chen, FingerShadow: An OLED Power Optimization based on Smartphone Touch Interactions, HotPower'14, October 2014.
- Kent W. Nixon, Xiang Chen, Hucheng Zhou, Yunxin Liu, and Yiran Chen, Mobile GPU Power Consumption Reduction via Dynamic Resolution and Frame Rate Scaling, HotPower'14, October 2014.
- Minjie Wang, Hucheng Zhou, Minyi Guo, and Zheng Zhang, A Scalable and Topology Configurable Protocol for Distributed Parameter Synchronization, APSys 2014, 24 June 2014.
- Tian Xiao, Zhenyu Guo, Hucheng Zhou, Jiaxing Zhang, Xu Zhao, Chencheng Ye, Xi Wang, Wei Lin, Wenguang Chen, and Lidong Zhou, Cybertron: Pushing the Limit on I/O Reduction in Data-Parallel Programs, OOPSLA, June 2014.
- Xuepeng Fan, Zhenyu Guo, Hai Jin, Xiaofei Liao, Jiaxing Zhang, Hucheng Zhou, Sean McDirmid, Wei Lin, Jingren Zhou, and Lidong Zhou, Spotting Code Optimizations in Data-Parallel Pipelines through PeriSCOPE, in IEEE Transactions on Parallel & Distributed Systems, IEEE – Institute of Electrical and Electronics Engineers, May 2014.
- Yi Ying Ng, Hucheng Zhou, Zhiyuan Ji, Huan Luo, and Yuan Dong, Which Android App Store Can be Trusted in China?, COMPSAC 2014, May 2014.
- Tian Xiao, Jiaxing Zhang, Hucheng Zhou, Zhenyu Guo, Sean McDirmid, Wei Lin, Wenguang Chen, and Lidong Zhou, Nondeterminism in MapReduce Considered Harmful? An Empirical Study on Non-commutative Aggregators in MapReduce Programs, (ICSE SEIP) Software Engineering in Practice , 13 April 2014.
- Sihan Li, Hucheng Zhou, Haoxiang Lin, Tian Xiao, Haibo Lin, Wei Lin, and Tao Xie, A Characteristic Study on Failures of Production Distributed Data-Parallel Programs, in ICSE (SEIP track). Best paper!, ACM/IEEE, 22 May 2013.
- Zhenyu Guo, Xuepeng Fan, Rishan Chen, Jiaxing Zhang, Hucheng Zhou, Sean McDirmid, Chang Liu, Wei Lin, Jingren Zhou, and Lidong Zhou, Spotting Code Optimizations in Data-Parallel Pipelines through PeriSCOPE, in OSDI, USENIX, 8 October 2012.
- Cheng Zhang, Longwen Lu, Hucheng Zhou, Jianjun Zhao, and Zheng Zhang, MoonBox: Debugging with Online Slicing and Dryrun, APSys, 23 July 2012.
- Jiaxing Zhang, Hucheng Zhou, Rishan Chen, Xuepeng Fan, Zhenyu Guo, Haoxiang Lin, Jack Y.Li, Wei Lin, Jingren Zhou, and Lidong Zhou, Optimizing Data Shuffling in Data-Parallel Computation by Understanding User-Defined Functions, in NSDI, USENIX, 25 April 2012.
- Jiaxing Zhang, Hucheng Zhou, Rishan Chen, Xuepeng Fan, Zhenyu Guo, Haoxiang Lin, Jack Y. Li, Wei Lin, Jingren Zhou, and Lidong Zhou, Optimizing Data Shuffling in Data-Parallel Computation by Understanding User-Defined Functions, no. MSR-TR-2012-28, April 2012.
- Hucheng Zhou, Wenguang Chen, and Fred C. Chow, An SSA-based algorithm for optimal speculative code motion under an execution profile, PLDI, 4 June 2011.
- Shih-wei Liao, Tzu-Han Hung, Donald Nguyen, Chinyen Chou, Chiaheng Tu, and Hucheng Zhou, Machine learning-based prefetch optimization for data center applications, SC, 14 November 2009.
- Shih-wei Liao, Tzu-Han Hung, Donald Nguyen, Hucheng Zhou, Chinyen Chou, and Chiaheng Tu, Prefetch Optimizations on Large-Scale Applications via Parameter Value Prediction, ICS, 8 June 2009.