Contact

• Email: dzhao@cs.washington.edu
• Zoom: washington.zoom.us/j/2066010000

Biography

As of 2023, Dr. Dongfang Zhao has been a tenure-track assistant professor of computer science at the University of Washington (UW) Tacoma SET and a member of the UW eScience in Seattle. He is currently visiting UW CSE, where he was previously a postdoc advised by Prof. Magdalena Balazinska.

Prof. Zhao is a U.S. citizen naturalized in Sacramento, California; he currently lives with his wife and children in Bellevue, Washington. Before moving to the United States, he completed his thesis in the Department of Mathematics at the Katholieke Universiteit Leuven, Belgium.

Services

• Associate Editor: Journal of Big Data (Springer)
• Technical Review Board: IEEE Transactions on Parallel and Distributed Systems
• Reviewer: zbMATH (European Mathematical Soceity)

Research

• I found the High-Performance Data-Intelligence Computing (HPDIC) research lab
• Publications indexed by third-parties: Google Scholar (overestimated due to my popular Chinese name), DBLP (missing some works)
• If you are interested in working with me on databases/HPC/AI, here in the following are some potential topics:
  • [ICWS'24] Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach
  • [PAKDD'24] Towards Nonparametric Topological Layers in Neural Networks
  • [SIGMOD'23] Toward Efficient Homomorphic Encryption for Outsourced Databases through Parallel Caching
  • [IPDPS'22a] Topological Modeling and Parallelization of Multidimensional Data on Microelectrode Arrays
  • [IPDPS'22b] DEAN: A Lightweight and Resource-efficient Blockchain Protocol for Reliable Edge Computing
  • [ICDE'21] SciChain: Blockchain-enabled Lightweight and Efficient Data Provenance for Reproducible Scientific Computing
  • [SC'21] BAASH: lightweight, efficient, and reliable blockchain-as-a-service for HPC systems
  • [AAAI'20 oral] HDK: Toward High Performance Deep-Learning-Based Kirchhoff Analysis
  • [SC'19] Swift machine learning model serving scheduling: a region based reinforcement learning approach
  • [VLDB'17] Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads
  • [TPDS'16] Towards Exploring Data-Intensive Scientific Applications at Extreme Scales through Systems and Simulations
  • [IPDPS'13] ZHT: A Light-Weight Reliable Persistent Dynamic Scalable Zero-Hop Distributed Hash Table
  • [TPAMI'09] Incremental Isometric Embedding of High-Dimensional Data Using Connected Neighborhood Graphs
• If you are interested in working with me on algebraic geometry, I suggest reading the following texts first:
  • Introduction to Algebraic Geometry, by Brendan Hassett. (Mostly computational perspective)
  • Algebraic Geometry, by Robin Hartshorne. (More abstract perspective, e.g., Chapters II & III)

Resources

• UW CSE: bicycle.cs.washington.edu
• UW Web: ovid.u.washington.edu
• UW HPC: klone.hyak.uw.edu
• TACC CPU: 129.114.109.16
• TACC GPU: 129.114.109.59
• UChicago GPU: 192.5.87.159