Biography

Dr. Dongfang Zhao is an assistant professor of computer science at the University of Washington (UW), where he is affiliated with the Tacoma School of Engineering and Technology and the eScience Institute. His research interests include databases, high-performance computing, and artificial intelligence. Prior to joining UW, he was a faculty member at the University of California, Davis and the University of Nevada, Reno. He completed his MS, PhD, and Postdoc at Emory University, Illinois Tech, and the UW Allen School of Computer Science and Engineering. Before relocating to the U.S., he worked at IMEC and studied mathematical statistics at KU Leuven, Belgium.

Beyond his academic career, Dr. Zhao also held multiple positions at various government and industry labs, including Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL), Centers for Disease Control and Prevention (CDC), Epic Systems, and IBM Research. He is a U.S. citizen naturalized in Sacramento, California.

Research

• I found the High-Performance and Data-Intensive Computing (HPDIC) research lab
• Publications indexed by third-parties: Google Scholar (overestimated due to my popular Chinese name), DBLP (missing some works)
• My work has been recognized by:
  • Best Paper Award, 2019 International Conference on Cloud Computing, San Diego, California
  • Best Student Paper Award, 2018 IEEE International Conference on Cloud Computing, San Francisco, California
• If you are interested in working or collaborating with me, here in the following are some potential topics:
  • [PAKDD'24 Oral] Towards Nonparametric Topological Layers in Neural Networks
  • [SIGMOD'23] Toward Efficient Homomorphic Encryption for Outsourced Databases through Parallel Caching
  • [SC'21] BAASH: lightweight, efficient, and reliable blockchain-as-a-service for HPC systems
  • [ICDE'21] SciChain: Blockchain-enabled Lightweight and Efficient Data Provenance for Reproducible Scientific Computing
  • [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
  • [TPAMI'09] Incremental Isometric Embedding of High-Dimensional Data Using Connected Neighborhood Graphs

Teaching

In 2024-2025, I teach

• CSE 499: Reading and Research

Resources

• UW CSE: bicycle.cs.washington.edu
• UW Web: ovid.u.washington.edu
• UW HPC: klone.hyak.uw.edu
• TACC Cloud: 129.114.109.16