Welcome to Da Zheng’s personal websites

I’m an applied scientist at AWS AI, where I’m working on Deep Graph Library and DGL-KE for graph neural networks and knowledge graphs. My research interest covers a wide range of areas, including high-performance computing, large-scale data analysis systems, data mining and machine learning. I got a PhD from the department of computer science at the Johns Hopkins University. During my PhD, I worked on FlashGraph and FlashR, frameworks for large-scale graph analysis and data analysis on solid-state drives (SSDs).

Selected Publications

  • Balasubramaniam Srinivasan, Da Zheng, George Karypis, Learning over Families of Sets – Hypergraph Representation Learning for Higher Order Tasks, SDM 2021 pdf

  • Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, George Karypis, DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs, arXiv:2010.05337, 2020 pdf

  • Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang, Featgraph: A flexible and efficient backend for graph neural network systems, in SC 2020 pdf

  • Vassilis N Ioannidis, Da Zheng, George Karypis, PanRep: Universal node embeddings for heterogeneous graphs, arXiv:2007.10445, 2020 pdf

  • Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis, DGL-KE: Training knowledge graph embeddings at scale, in SIGIR 2020 pdf

  • Qi Zhu, Hao Wei, Bunyamin Sisman, Da Zheng, Christos Faloutsos, Xin Luna Dong, Jiawei Han, Collective Multi-type Entity Alignment Between Knowledge Graphs, in the Web Conference 2020 pdf

  • Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander Smola, Zheng Zhang, Deep graph library: Towards efficient and scalable deep learning on graphs, arXiv:1909.01315, 2019 pdf

  • Da Zheng, Disa Mhembere, Joshua Vogelstein, Carey E. Priebe, Randal Burns, FlashR: parallelize and scale R for machine learning using SSDs, in PPoPP 2018 pdf

  • Disa Mhembere, Da Zheng, Carey E Priebe, Joshua T Vogelstein, Randal Burns, knor: A NUMA-optimized in-memory, distributed and semi-external-memory k-means library, in HPDC 2017 pdf

  • Da Zheng, Disa Mhembere, Vince Lyzinski, Joshua Vogelstein, Carey E. Priebe, Randal Burns, Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture, in Transactions on Parallel and Distributed Systems, 2017 pdf

  • Heng Wang, Da Zheng, Randal Burns, Carey Priebe, Active Community Detection in Massive Graphs, in SDM-Networks 2015 [pdf]

  • Da Zheng, Disa Mhembere, Randal Burns, Joshua Vogelstein, Carey Priebe, Alexander S. Szalay, FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs, in FAST 2015 pdf

  • Da Zheng, Randal Burns, Alexander S. Szalay, Toward Millions of File System IOPS on Low-Cost, Commodity Hardware, in Supercomputing 2013 pdf

  • Da Zheng, Randal Burns, Alexander S. Szalay, A Parallel Page Cache: IOPS and Caching for Multicore Systems, in HotStorage 2012 pdf