Dongfang Zhao

Assistant Professor
Dongfang Zhao

Contact Information

Degrees

  • Postdoc, Computer Science, University of Washington, 2016
  • Ph.D., Computer Science, Illinois Institute of Technology, 2015
  • M.S., Computer Science, Emory University, 2008

Biography

Dr. Zhao is an assistant professor in the Department of Computer Science & Engineering at University of Nevada, Reno. Prior to joining the University, he completed his postdoctoral fellowship in the School of Computer Science & Engineering at University of Washington, Seattle. He was a recipient of the Moore-Sloan Postdoctoral Fellowship (2016) and the Oak Ridge Institute of Science and Education Fellowship (2007). He received his Ph.D. in computer science from Illinois Institute of Technology, Chicago.

Research interests

  • Database management systems
  • High-performance computing
  • Machine intelligence

Selected and recent publication

  1. Parmita Mehta, Sven Dorkenwald, Dongfang Zhao, Tomer Kaftan, Alvin Cheung, Magdalena Balazinska, Ariel Rokem, Andrew Connolly, Jacob Vanderplas, and Yusra AlSayyad. Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads. 43rd International Conference on Very Large Data Bases (VLDB). August 2017.
  2. Dongfang Zhao, Kan Qiao, Zhou Zhou, Tonglin Li, Zhihan Lu, and Xiaohua Xu. Toward Efficient and Flexible Metadata Indexing of Big Data Systems. IEEE Transactions on Big Data (TBD). 3(1):107-117, March 2017.
  3. Dongfang Zhao, Ning Liu, Dries Kimpe, Robert Ross, Xian-He Sun, and Ioan Raicu. Towards Exploring Data-Intensive Scientific Applications at Extreme Scales through Systems and Simulations. IEEE Transactions on Parallel and Distributed Systems (TPDS). 27(6):1824-1837, June 2016.
  4. Dongfang Zhao, Kan Qiao, Jian Yin, and Ioan Raicu. Dynamic Virtual Chunks: On Supporting Efficient Accesses to Compressed Scientific Data. IEEE Transactions on Services Computing (TSC). 9(1):96-109, February 2016.
  5. Dongfang Zhao and Li Yang. Incremental Isometric Embedding of High-Dimensional Data Using Connected Neighborhood Graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 31(1):86-98, January 2009.