Biography
Dr. Xin Liang is a tenure-track assistant professor with the Department of Computer Science at University of Kentucky (UKY). He received his Ph.D. in Computer Science from University of California, Riverside in 2019. Prior to that, he received his B.S. in Computer Science from Peking University in 2014, with a minor in Math and Applied Math. During his Ph.D. studies, he worked as student interns in the Extreme Scale Resilience Group and the Parallel Extreme-Scale Data Analytics Team at Argonne National Laboratory (ANL), the Scalable Machine Learning Group at Pacific Northwest National Laboratory (PNNL), and the Data Science at Scale Team at Los Alamos National Laboratory (LANL). Prior to joining UKY, he worked as an assistant professor at Missouri University of Science and Technology and Computer/Data Scientist in the Workflow Systems Group at Oak Ridge National Laboratory (ORNL).
Dr. Liang's research interests lie broadly in the areas of high-performance computing, parallel and distributed systems, scientific data management, large-scale data analytics, and distributed machine learning. He has published in many highly competitive conferences and journals such as IEEE/ACM SC, ACM HPDC, ACM PPoPP, ACM ICS, IEEE IPDPS, ACM PACT, IEEE BigData, IEEE Cluster, IEEE TPDS etc. He has received Dissertation Year Fellowship (DYP) from UCR and Best Paper Award from IEEE Cluster 2018. While working at ORNL, he led the ESAMR project funded by the Director's Research and Development (DRD) program as principle investigator. He is one of the key developers of SZ and major contributors of MGARD, which are two widely used data reduction software in the scientific computing community. More details about Dr. Liang can be found in his CV.
Dr. Liang is always looking for self-motivated students to work on high-performance computing, scientific data management, and big data analytics. If you're interested in his research, please contact him at xliang@uky.edu. According to CS ranking, UKY is ranked 36th considering the last ten years (2013-2023) and 24th considering the last seven years (2016-2023) among all the US universities in the area of high-performance computing.
Latest News
- 12/2023: One paper has been accepted in ICDE'24. Congratulations to Mingze!
- 11/2023: Dr. Liang has been recommended as EPSCoR Research Fellows for our RII Track-4 project awarded by the National Science Foundation (my role: PI). This project aims to improve the performance of MPI on exascale systems with adaptive lossy compression methods. Thanks a lot to the NSF!
- 11/2023: One paper has been accepted in SIGMOD'24.
- 09/2023: One paper has been accepted in HiPC'23. Congratulations to Pu!
- 08/2023: Our OAC Core project was awarded by the National Science Foundation (my role: lead PI, in collaborations with the University of Utah and the Ohio State University). This project aims to design topology-aware compressors for scientific applications. Thanks a lot to the NSF!
- 08/2023: Xuan Wu joined our team as a PhD student. Welcome Xuan!
- 06/2023: One paper has been accepted in VIS'23.
- 06/2023: Our CSSI project was awarded by the National Science Foundation (my role: lead PI, in collaborations with New Jersey Institute of Technology and Temple University). This project aims to develop progressive data management middleware to advance exascale science. Thanks a lot to the NSF!
- 05/2023: One paper has been accepted in ICS'23 and nominated in the Best Paper Finalists!
- 04/2023: Two papers have been accepted in HPDC'23.
- 02/2023: One paper has been accepted in ICDE'23.
- 01/2023: Mikhail Kovalev and Andrew Chen from Paul Laurence Dunbar High School joined our team to complete their capstone research project. Welcome Mikhail and Andrew!
- 11/2022: One paper has been accepted in VLDB'23. Congratulations to Pu!
- 11/2022: One paper has been accepted in PPoPP'23.
- 11/2022: Attended the Turing lecture by Dr. Jack Dongarra at SC'22. Dr. Dongarra received the 2021 ACM A.M. Turing Award for pioneering contributions to numerical algorithms and libraries that enabled high performance computational software. [video]
- 10/2022: We received a subcontract from Argonne National Laboratory to investigate the artifacts in lossy compressors and the possible recover. Thanks a lot to ANL!
- 10/2022: One paper has been accepted in IEEE Transactions on Visualization and Computer Graphics.
- 08/2022: Mingze Xia joined our team as a PhD student. Welcome Mingze!
- 08/2022: Our CyberTraining project was awarded by the National Science Foundation (my role: site PI, in collaboration with University of North Texas, Southern Illinois University Carbondale, and Missouri University of Science and Technology). This project aims to host training workshops to develop workforce for deep learning with GPU-based systems. Thanks a lot to the NSF!
- 08/2022: One paper has been accepted in IEEE Transactions on Big Data.
- 06/2022: One paper has been accepted in SC'22.
- 05/2022: One paper has been accepted in SSDBM'22.
- 04/2022: One paper has been accepted in HPDC'22.
- 02/2022: Our CRII project was awarded by the National Science Foundation (my role: PI). This project aims to enable the preservation of quantities-of-interest in scientific lossy compressors. Thanks a lot to the NSF!
- 01/2022: Pu Jiao joined our team as a PhD student. Welcome Pu!
Research Interests
- Parallel, distributed, and heterogeneous systems.
- Data management, reduction, refactoring, and streaming.
- Scientific visualization and data analytics.
- Large-scale machine learning and deep learning.
- Cloud and edge computing.
- Fault tolerant mechanics, algorithms, and implementations.
- Linear algebra and numerical algorithms.
Education
- Ph. D. in Computer Science, University of California, Riverside, 2014-2019.
- B. S. in Computer Science, Peking University, 2010-2014.
- Minor in Mathematics, Peking University, 2011-2014.
Selected Publications (Full List, Google Scholar)
My students are underlined.
ICDE'24
Mingze Xia, Sheng Di, Franck Cappello, Pu Jiao, Kai Zhao, Jinyang Liu, Xuan Wu, Xin Liang*, and Hanqi Guo.
Preserving Topological Feature with Sign-of-Determinant Predicates in Lossy Compression: A Case Study of Vector Field Critical Points.
Proceedings of the 40th IEEE International Conference on Data Engineering, Utrecht, Netherlands, May 13 - 16, 2024. (*: Corresponding authors)
HiPC'23
Pu Jiao, Sheng Di, Jinyang Liu, Xin Liang*, and Franck Cappello.
Characterization and Detection of Artifacts for Error-controlled Lossy Compressors.
Proceedings of the 30th IEEE International Conference on High Performance Computing, Data, and Analytics, Goa, India, Dec 18 - Dec 21, 2023. (*: Corresponding authors)
VLDB'23
Pu Jiao, Sheng Di, Hanqi Guo, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang*, and Franck Cappello.
Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data.
Proceedings of the 49th International Conference on Very Large Data Bases, Vancour, Canada, Aug 28 - Sep 1, 2023. (*: Corresponding authors)
TVCG
Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, and Hanqi Guo.
Toward Feature-Preserving Vector Field Compression.
IEEE Transactions on Visualization and Computer Graphics, 2022.
TBD
Xin Liang*, Kai Zhao*, Sheng Di, Sihuan Li, Robert Underwood, Ali M. Gok, Jiannan Tian, Junjing Deng, Jon C. Calhoun, Dingwen Tao, Zizhong Chen, and Franck Cappello.
SZ3: A Modular Framework for Composing Prediction-based Error-bounded Lossy Compressors.
IEEE Transactions on Big Data, 2022. (*: Co-first authors)
SC'21
Xin Liang, Qian Gong, Jieyang Chen, Ben Whitney, Lipeng Wan, Qing Liu, David Pugmire, Rick Archibald, Norbert Podhorszki, and Scott Klasky.
Error-controlled, Progressive, and Adaptable Retrieval of Scientific Data with Multilevel Decomposition.
Proceedings of the 33rd ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, St. Louis, Missouri, USA, Nov 14 - 19, 2021. Acceptance Rate: 23.6% (86/365)
TC
Xin Liang*, Ben Whitney*, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, and Scott Klasky.
MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction.
IEEE Transactions on Computers, 2021. (*: Co-first authors)
PacificVis'20
Xin Liang, Hanqi Guo, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen and Tom Peterka.
Towards Feature Preserving 2D and 3D Vector Field Compression.
Proceedings of the 13rd IEEE Pacific Visualization Symposium, Tianjin, China, Apr 14-17, 2020. Acceptance Rate: 24% (23/96)
SC'19
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)
Cluster'19
Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Bogdan Nicolae, Zizhong Chen, and Franck Cappello.
Improving Performance of Data Dumping with Lossy Compression for Scientific Simulation.
Proceedings of the 2019 IEEE International Conference on Cluster Computing, Albuquerque, New Mexico USA, September 23 - 26, 2019. Acceptance Rate: 27.7% (39/141)
Cluster'18
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.
BigData'18
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)
SC'17
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).