Biography

Since 2023, Dr. Dongfang Zhao has been a tenure-track assistant professor of computer science at the University of Washington (UW), where he is affiliated with the Tacoma SET and the eScience Institute. Prior to joining UW, he was a visiting professor at UC Davis and a postdoctoral researcher at UW CSE. Before relocating to the U.S., Dr. Zhao worked on low-power electronics at IMEC and studied mathematical statistics at KU Leuven, Belgium.

Teaching

In 2023-2024, I teach

• CSE 499: Reading and Research (selected research topics for undergrad students)
• TCSS 590: Computing and Software Systems (selected research topics for graduate students only)
• TCSS 143: Fundamentals of Object-Oriented Programming Theory and Application

I was the primary advisor of the following graduate students (degree, graduation year, name, first employment):

• Ph.D., 2023, Olamide Timothy Tawose, Tenure-track Assistant Professor at Lincoln University, USA
• Ph.D., 2022, Abdullah Al-Mamun, Tenure-track Assistant Professor at Augusta University, USA
• M.Sc., 2021, Jianing Zhuang, Software Engineer at Microsoft, USA
• M.Sc., 2020, Bin Li, Software Engineer at Google, USA

Research

• I found and direct the High-Performance and Data-Intensive Computing (HPDIC) research lab
• Publications indexed by third-parties: Google Scholar (overestimated due to my popular name), DBLP (missing some works)
• If you are interested in working or collaborating with me, here in the following are some important papers:
  • [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
  • [TSC'16] Dynamic Virtual Chunks: On Supporting Efficient Accesses to Compressed Scientific Data
  • [TPAMI'09] Incremental Isometric Embedding of High-Dimensional Data Using Connected Neighborhood Graphs

Resources

• TACC: 129.114.109.74
• UW: ovid.u.washington.edu
• UW: klone.hyak.uw.edu