| High-ratio lossy compression: Exploring the autoencoder to compress scientific data T Liu, J Wang, Q Liu, S Alibhai, T Lu, X He IEEE Transactions on Big Data 9 (1), 22-36, 2021 | 53 | 2021 |
| Compression ratio modeling and estimation across error bounds for lossy compression J Wang, T Liu, Q Liu, X He, H Luo, W He IEEE Transactions on Parallel and Distributed Systems 31 (7), 1621-1635, 2019 | 19 | 2019 |
| Identifying Latent Reduced Models to Precondition Lossy Compression H Luo, D Huang, Q Liu, Z Qiao, H Jiang, J Bi, H Yuan, M Zhou, J Wang, ... 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2019 | 19 | 2019 |
| Exploring Transfer Learning to Reduce Training Overhead of HPC Data in Machine Learning T Liu, S Alibhai, J Wang, Q Liu, X He, C Wu 2019 IEEE International Conference on Networking, Architecture and Storage …, 2019 | 14 | 2019 |
| Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks Z Qin, J Wang, Q Liu, J Chen, D Pugmire, N Podhorszki, S Klasky IEEE Letters of the Computer Society 3 (1), 5-8, 2020 | 7 | 2020 |
| Locality-based transfer learning on compression autoencoder for efficient scientific data lossy compression N Wang, T Liu, J Wang, Q Liu, S Alibhai, X He Journal of Network and Computer Applications 205, 103452, 2022 | 6 | 2022 |
| DuoModel: Leveraging Reduced Model for Data Reduction and Re-Computation on HPC Storage H Luo, Q Liu, Z Qiao, J Wang, M Wang, H Jiang IEEE Letters of the Computer Society 1 (1), 5-8, 2018 | 5 | 2018 |
| Robust and scalable deep learning for X-ray synchrotron image analysis N Meister, Z Guan, J Wang, R Lashley, J Liu, J Lhermitte, K Yager, H Qin, ... Scientific Data Summit (NYSDS), 2017 New York, 1-6, 2017 | 5 | 2017 |
| SIRIUS: Enabling Progressive Data Exploration for Extreme-Scale Scientific Data Z Qiao, T Lu, H Luo, Q Liu, S Klasky, N Podhorszki, J Wang IEEE Transactions on Multi-Scale Computing Systems 4 (4), 900-913, 2018 | 3 | 2018 |
| Zperf: A Statistical Gray-Box Approach to Performance Modeling and Extrapolation for Scientific Lossy Compression J Wang, Q Chen, T Liu, Q Liu, X He IEEE Transactions on Computers, 2023 | 2 | 2023 |
| Analyzing the Impact of Lossy Data Reduction on Volume Rendering of Cosmology Data J Wang, P Grosset, TL Turton, J Ahrens 2022 IEEE/ACM 8th International Workshop on Data Analysis and Reduction for …, 2022 | 1 | 2022 |
| Unbalanced Parallel I/O: An Often-Neglected Side Effect of Lossy Scientific Data Compression X Wang, L Wan, J Chen, Q Gong, B Whitney, J Wang, A Gainaru, Q Liu, ... 2021 7th International Workshop on Data Analysis and Reduction for Big …, 2021 | 1 | 2021 |
| Visualization Quality Assessment A Grosset, J Ahrens, T Turton, J Wang Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2023 | | 2023 |
| Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network J Wang, X Liang, B Whitney, J Chen, Q Gong, X He, L Wan, S Klasky, ... 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2727-2739, 2023 | | 2023 |
| Locality-based transfer learning on compression autoencoder for high-performance lossy compression of scientific data N Wang, T Liu, J Wang, Q Liu, S Alibhai, X He Journal of Network and Computer Applications 205 (C), 2022 | | 2022 |
| Reducing the Training Overhead of the HPC Compression Autoencoder via Dataset Proportioning T Liu, S Alibhai, J Wang, Q Liu, X He 2021 IEEE International Conference on Networking, Architecture and Storage …, 2021 | | 2021 |
| DRBSD 2022 R Underwood, J Bessac, J Wang, P Grosset, H Sather, A Pinard | | |
Qing LiuAssociate Professor, New Jersey Institute of TechnologyVerified email at njit.edu
