Dr. Dongfang Zhao joined the University of Washington (UW) in 2023,
as a tenure-track assistant professor of computer science in the UW Tacoma School of Engineering and Technology.
Before that, he was a faculty member at the University of Nevada and the University of California, Davis.
Professional Services
- Editorial Board: IEEE Transactions on Parallel and Distributed Systems (TPDS)
- Senior Program Committee: AAAI'27, AAAI'26
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Open-Source Contributor (GitHub ID:
HPDIC):
- Meta/Facebook Faiss (CUDA kernel)
- Microsoft DiskANN (C++ main branch)
Recent papers (after joining UW in 2023)
- Technical Reports (not peer-reviewed)
- [KDD'26] RAE: A Neural Network Dimensionality Reduction Method for Nearest Neighbors Preservation in Vector Search (acceptance rate 18%)
- [HPDC'26] SIVF: GPU-Resident IVF Index for Streaming Vector Analytics (acceptance rate 14%)
- [ICDE'26] QPAD: Quantile-Preserving Approximate Dimension Reduction for Nearest Neighbors Preservation in High-Dimensional Vector Search
- [CVPR'26] BadRSSD: Backdoor Attacks on Regularized Self-Supervised Diffusion Models
- [WWW'26] Reliable Non-Leveled Homomorphic Encryption for Web Services (acceptance rate 20%)
- [AAAI'26] Order-Preserving Dimension Reduction for Multimodal Semantic Embedding (acceptance rate 17%)
- [ICDM'25] ProHD: Projection-Based Hausdorff Distance Approximation (acceptance rate 13%)
- [ECAI'25] IPBA: Imperceptible Perturbation Backdoor Attack in Federated Self-Supervised Learning (acceptance rate 23%)
- [ICWS'24] Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach (acceptance rate 19%)
Some older papers (before joining UW in 2023)
- [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
- [AAAI'20] 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
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