Da Zheng
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
38
Venues
18
Active years
2013–2026
Best venue rank
A*
Where they publish
Papers
38 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | AAAI | Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study. | Yuqi Zhu, Yi Zhong, Jintian Zhang, Ziheng Zhang, Shuofei Qiao, Yujie Luo, Lun Du, Da Zheng, Ningyu Zhang, Huajun Chen |
| 2026 | ACL | What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations. | Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen |
| 2026 | ACL | KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality. | Baochang Ren, Shuofei Qiao, Ningyu Zhang, Da Zheng, Huajun Chen |
| 2026 | HCI | Research on Interaction Design Application Methods Based on Smart Materials' Perceptual Experience. | Ping Gong, Tingyi Gao, Da Zheng, Zhiyong Fu |
| 2026 | ICPR | SigRef: Verification-Driven Reflection for Faithful Paper-to-Code Development. | Mingyang Zhou, Quanming Yao, Lun Du, Lanning Wei, Da Zheng |
| 2025 | ACL | Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models. | Junfeng Tian, Da Zheng, Yang Chen, Rui Wang, Colin Zhang, Debing Zhang |
| 2025 | EMNLP | Retrieval-Augmented Language Models are Mimetic Theorem Provers. | Wenjie Yang, Ruiyuan Huang, Jiaxing Guo, Zicheng Lyu, Tongshan Xu, Shengzhong Zhang, Lun Du, Da Zheng, Zengfeng Huang |
| 2025 | EMNLP | LightThinker: Thinking Step-by-Step Compression. | Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang |
| 2025 | WWW | OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System. | Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan, Mengshu Sun, Ningyu Zhang, Lei Liang, Zhiqiang Zhang, Jun Zhou, Lanning Wei, Da Zheng, Haofen Wang, Huajun Chen |
| 2024 | ASPLOS | RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing. | Zheng Wang, Yuke Wang, Jiaqi Deng, Da Zheng, Ang Li, Yufei Ding |
| 2024 | ASPLOS | Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures. | Kun Wu, Mert Hidayetoglu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-Mei Hwu |
| 2024 | CIKM | Revisit Orthogonality in Graph-Regularized MLPs. | Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu |
| 2024 | ICLR | NetInfoF Framework: Measuring and Exploiting Network Usable Information. | Meng-Chieh Lee, Haiyang Yu, Jian Zhang, Vassilis N. Ioannidis, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos |
| 2024 | KDD | GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. | Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis |
| 2023 | KDD | 19th International Workshop on Mining and Learning with Graphs (MLG). | Neil Shah, Shobeir Fakhraei, Da Zheng, Bahare Fatemi, Leman Akoglu |
| 2023 | KDD | Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications. | Han Xie, Da Zheng, Jun Ma, Houyu Zhang, Vassilis N. Ioannidis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, Belinda Zeng, Trishul Chilimbi |
| 2023 | KDD | GraphStorm an Easy-to-use and Scalable Graph Neural Network Framework: From Beginners to Heroes. | Jian Zhang, Da Zheng, Xiang Song, Theodore Vasiloudis, Israt Nisa, Jim Lu |
| 2023 | PPoPP | DSP: Efficient GNN Training with Multiple GPUs. | Zhenkun Cai, Qihui Zhou, Xiao Yan, Da Zheng, Xiang Song, Chenguang Zheng, James Cheng, George Karypis |
| 2023 | WWW | PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction. | Shichang Zhang, Jiani Zhang, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos, Yizhou Sun |
| 2023 | SC | TANGO: re-thinking quantization for graph neural network training on GPUs. | Shiyang Chen, Da Zheng, Caiwen Ding, Chengying Huan, Yuede Ji, Hang Liu |
| 2023 | SC | DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training. | Hongkuan Zhou, Da Zheng, Xiang Song, George Karypis, Viktor K. Prasanna |
| 2022 | KDD | Nimble GNN Embedding with Tensor-Train Decomposition. | Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutsos, George Karypis, Richard W. Vuduc |
| 2022 | KDD | Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs. | Da Zheng, Xiang Song, Chengru Yang, Dominique LaSalle, George Karypis |
| 2021 | KDD | Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. | Jialin Dong, Da Zheng, Lin F. Yang, George Karypis |
| 2021 | SC | Dr. Top-k: delegate-centric Top-k on GPUs. | Anil Gaihre, Da Zheng, Scott Weitze, Lingda Li, Shuaiwen Leon Song, Caiwen Ding, Xiaoye S. Li, Hang Liu |
| 2021 | WSDM | Scalable Graph Neural Networks with Deep Graph Library. | Da Zheng, Minjie Wang, Quan Gan, Xiang Song, Zheng Zhang, George Karypis |
| 2021 | SDM | Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks. | Balasubramaniam Srinivasan, Da Zheng, George Karypis |
| 2020 | KDD | Scalable Graph Neural Networks with Deep Graph Library. | Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, George Karypis |
| 2020 | WWW | Learning Graph Neural Networks with Deep Graph Library. | Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, George Karypis |
| 2020 | WWW | Collective Multi-type Entity Alignment Between Knowledge Graphs. | Qi Zhu, Hao Wei, Bunyamin Sisman, Da Zheng, Christos Faloutsos, Xin Luna Dong, Jiawei Han |
| 2020 | SIGIR | DGL-KE: Training Knowledge Graph Embeddings at Scale. | Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis |
| 2020 | SC | FeatGraph: a flexible and efficient backend for graph neural network systems. | Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang |
| 2020 | SC | DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs. | Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, George Karypis |
| 2018 | PPoPP | FlashR: parallelize and scale R for machine learning using SSDs. | Da Zheng, Disa Mhembere, Joshua T. Vogelstein, Carey E. Priebe, Randal C. Burns |
| 2017 | HPDC | knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library. | Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal C. Burns |
| 2015 | FAST | FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs. | Da Zheng, Disa Mhembere, Randal C. Burns, Joshua T. Vogelstein, Carey E. Priebe, Alexander S. Szalay |
| 2013 | HPCC | Parallel Set Determination and K-Means Clustering for Data Mining on Telecommunication Networks. | Da-Qi Ren, Da Zheng, Guowei Huang, Shujie Zhang, Zane Wei |
| 2013 | SC | Toward millions of file system IOPS on low-cost, commodity hardware. | Da Zheng, Randal C. Burns, Alexander S. Szalay |