Guangyan Zhang

Associate Professor
Computer Science and Technology
Tsinghua University

Room 8-208, East Main Building,
Tsinghua University, Beijing,
P.R.China. 100084

E-mail: gyzh at tsinghua dot edu dot cn

Guangyan Zhang is an associate professor in the Department of Computer Science and Technology, where he joined since July 2008. He obtained his Ph.D degree from Tsinghua University under the guidance of Prof. Weimin Zheng. Before that, he received the bachelor's and master's degrees in computer science from Jilin University in 2000 and 2003. He is a Professional Member of the ACM, a member of the CCF technical committee of information storage technology, a Communication Member of CCF Task Force on Big Data.

 

Research:

His current research focuses on Big Data Computing, Storage Systems, and Distributed Systems, especially in:

  • Deep Learning,
  • Graph Computing,
  • Stream Computing,
  • Cloud Storage,
  • RAID and Erasure Codes,
  • Flash and PCM Storage,
  • Storage Virtualization,
  • Distributed File Systems, and
  • Benchmarking.

 

Courses:

He teaches two courses, one of which is for graduate students, and the other is for undergraduate students.

  • CS 70240013: Advanced Computer Architecture, for Ph.D and master students, and
  • CS 30240522: Program Design and Training, for undergraduate students.

Publications:

His selected publications include:

  • Chengwen Wu, Guangyan Zhang, Keqin Li, and Weimin Zheng, "Large graph computing systems," Big Data Management, Architecture, and Processing, Kuan-Ching Li, Hai Jiang, and Albert Zomaya, eds., Chapman & Hall/CRC Big Data Series, CRC Press, Taylor & Francis Group, USA, 2017.
  • Shuhan Cheng, Guangyan Zhang, Jiwu Shu, Qingda Hu, and Weimin Zheng. "FastBFS: Fast Breadth-First Graph Search on a Single Server," in the Proceedings of the 30th IEEE International Parallel & Distributed Processing Symposium (IPDPS'16), Chicago, Illinois USA, May 2016. (Acceptance Ratio:23%)
  • Xiaqing Li, Guangyan Zhang, Keqin Li, and Weimin Zheng, "Deep learning and its parallel acceleration techniques," Big Data: Principles and Paradigms, Rajkumar Buyya, Rodrigo N. Calheiros, and Amir Vahid Dastjerdi, eds., Morgan Kaufmann/Elsevier, 2015.
  • Dawei Sun, Guangyan Zhang, Weimin Zheng, and Keqin Li, "Key technologies for big data stream computing," Big Data: Algorithms, Analytics, and Applications, Kuan-Ching Li, Hai Jiang, Laurence T. Yang, and Alfredo Cuzzocrea, eds., CRC Press, Taylor & Francis Group, 2015.

 

Advisees:

Current Students:

  • Xiaqing Li,
  • Le Zhou,
  • Guiyong Wu,
  • Songlin Yang,
  • Chengwen Wu,
  • Xinyang Jiang,
  • Tao Du,
  • Zhufan Wang,
  • Zican Huang .

.

Resources:

The top conferences in his concern:

  • USENIX Conference on File and Storage Technologies, FAST.
  • USENIX Annual Technical Conference, USENIX.

The top journals in his concern: