459 Winton Chung Hall
900 University Avenue
Riverside, CA 92521
Department of Computer Science and Engineering
University of California, Riverside
I am a Ph.D. candidate in Computer Science Department of University of California, Riverside, co-advised by Dr. Zizhong Chen and Dr. Franck Cappello (Argonne National Laboratory). Before coming to UCR, I got my B.S. in Computer Science from Peking University in 2014, with a minor in Mathematics. My research interests lie broadly in the area of high-performance computing, with a special focus on resilient algorithms and data management, reduction and analytics. I did two internships in Pacific Northwest National Laboratory and Los Alamos National Laboratory, working with Dr. Abhinav Vishnu on extreme scale machine learning and Dr. Qiang Guan on container-based application encapsulation and in-situ deep neural network visualization, respectively. Currently, I am taking a long-term internship in Argonne National Laboratory, working on error-bounded lossy compression for scientific datasets under the supervision of Dr. Sheng Di and Dr. Franck Cappello. I am also mentored by Dr. Hanqi Guo and Dr. Tom Peterka since this summer, researching on feature-preserving lossy compression for 2D/3D piece-wise linear vector fields in scientific visualization. More details about me can be found in my CV.
- Ph. D. in Computer Science, University of California, Riverside, 2014-present.
- B. S. in Computer Science, Peking University, 2010-2014.
- Minor in Mathematics, Peking University, 2011-2014.
- Parallel, distributed, and heterogeneous systems.
- Lossy compression and data management.
- Scientific visualization and data analytics.
- Large-scale data mining and machine learning algorithms.
- Scalable training and efficient inference for deep neural networks.
- Fault tolerant mechanics, algorithms, and implementations.
- Linear algebra and numerical algorithms.
Representative Publications (Full List)
Xin Liang, Sheng Di, Sihuan Li, Dingwen Tao, Bogdan Nicolae, Zizhong Chen, and Franck Cappello.
Significantly Improving Lossy Compression Quality based on An Optimized Hybrid Prediction Model.
Proceedings of the 31st ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, Colorado, USA, Nov 17 - 22, 2019. Acceptance Rate: 20.9% (72/344)
Xin Liang, Sheng Di, Dingwen Tao, Zizhong Chen, and Franck Cappello.
An Efficient Transformation Scheme for Lossy Data Compression with Point-wise Relative Error Bound (best paper award in Data, Storage, and Visualization Area).
Proceedings of the 2018 IEEE International Conference on Cluster Computing, Belfast, UK, September 10 - 13, 2018. Less than 2.6% (4/154) of submissions are awarded best papers.
Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Shaomeng Li, Hanqi Guo, Zizhong Chen, and Franck Cappello.
Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets.
Proceedings of the 2018 IEEE International Conference on Big Data, Seattle, WA, USA, December 10 - 13, 2018. Acceptance Rate: 18.9% (98/518)
Xin Liang, Jieyang Chen, Dingwen Tao, Sihuan Li, Panruo Wu, Hongbo Li, Kaiming Ouyang, Yuanlai Liu, Fengguang Song, and Zizhong Chen.
Correcting Soft Errors Online in Fast Fourier Transform.
Proceedings of the 29th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, Colorado, USA, Nov 12 - 17, 2017. Acceptance Rate: 18.6% (61/327).