Jinyang Liu

Ph. D. Candidate
Department of Computer Science and Engineering
University of California, Riverside

Email: jliu 447 at ucr.edu



Biography

I'm Jinyang Liu, a 5th-year PhD candidate in the computer science major at the University of California, Riverside. Prior to that, I received my Master's degree in Data Science from Peking University in 2019 and received my B.S. degree in Math and Applied Math from Peking University in 2016. During my Ph.D. studies, I have been working as a long-term research intern in the ECP-EZ project at Argonne National Laboratory (ANL) and my research field is scientific data lossy compression frameworks and algorithms, including traditional methods and Deep-learning-based methods. In my master's studies, I've worked on the topic of deep-learning-based analysis and auto-completion of programming codes.

Besides my finished research projects, I also have interests broadly in the areas of high-performance computing, large-scale scientific data management and reduction, and deep learning (in HPC applications). I have presented publications in various highly prestigious conferences and journals such as ACM SIGMOD, IEEE/ACM SC, ACM ICS, IEEE IPDPS, IEEE BigData, IEEE Cluster, IEEE TPDS, etc. I have received the Dissertation Year Fellowship (DYP) award from UCR and a Best Paper Finalist from ACM ICS 23'. During my work at ANL, based on the SZ compression framework, I have developed several different scientific data error-bounded lossy compressors, namely AE-SZ, QoZ, FAZ, and cuSZ-I.

I will join the department of Computer Science at the University of Houston as a tenure-track assistant professor, and am seeking for several Ph. D. students to study under my advisory starting from Fall 2024 (only for those who have submitted the applications to UH CS) or Spring 2025. My full CV is at: My CV. Check here for more information about PhD hiring.


Education


Awards


Research Interests


Latest News


Selected Publications ( Full list in Google Scholar)

Accepted by SC '24/h6>

Jinyang Liu*, Jiannan Tian*, Shixun Wu*, Sheng Di, Boyuan Zhang, Robert Underwood, Yafan Huang, Jiajun Huang, Kai Zhao, Guanpeng Li, Dingwen Tao, Zizhong Chen, Franck Cappello.
High-ratio Scientific Lossy Compression on GPUs with Optimized Multi-level Interpolation.
arXiv preprint arXiv:2312.05492 (2023). (*: Co-first authors)

SIGMOD '24

Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang, Sian Jin, Zizhe Jian, Jiajun Huang, Shixun Wu, Zizhong Chen, Franck Cappello.
High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation.
2024 ACM SIGMOD Conference.

ICS '23
(Best Paper Finalist)

Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello.
FAZ: A flexible auto-tuned modular error-bounded compression framework for scientific data.
2023 InternationalConference on Supercomputing (ICS ’23), June 21–23, 2023, Orlando, FL, USA. (best paper nominee)

SC '22

Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello.
Dynamic quality metric oriented error bounded lossy compression for scientific datasets.
2022 SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

BigData '23

Jinyang Liu, Sheng Di, Sian Jin, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello.
Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks.
2023 IEEE International Conference on Big Data (BigData).

BigData '21

Jinyang Liu, Sihuan Li, Sheng Di, Xin Liang, Kai Zhao, Dingwen Tao, Zizhong Chen, Franck Cappello.
Improving lossy compression for SZ by exploring the best-fit lossless compression techniques.
2021 IEEE International Conference on Big Data (Big Data).

Cluster '21

Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, Franck Cappello.
Exploring autoencoder-based error-bounded compression for scientific data.
2021 IEEE International Conference on Cluster Computing (CLUSTER).

ICDE '24

Mingze Xia, Sheng Di, Franck Cappello, Pu Jiao, Kai Zhao, Jinyang Liu, Xuan Wu, Xin Liang, Hanqi Guo.
Preserving Topological Feature with Sign-of-Determinant Predicates in Lossy Compression: A Case Study of Vector Field Critical Points.
the 40th IEEE International Conference on Data Engineering.

HiPC '23

Arham Khan, Sheng Di, Kai Zhao, Jinyang Liu, Kyle Chard, Ian Foster, Franck Cappello.
SECRE: Surrogate-based Error-controlled Lossy Compression Ratio Estimation Framework.
30th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2023).

HiPC '23

Pu Jiao, Sheng Di, Jinyang Liu, Xin Liang, Franck Cappello.
Characterization and Detection of Artifacts for Error-controlled Lossy Compressors.
30th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2023).

Cluster '23

Jiajun Huang, Kaiming Ouyang, Yujia Zhai, Jinyang Liu, Min Si, Ken Raffenetti, Hui Zhou, Atsushi Hori, Zizhong Chen, Yanfei Guo, Rajeev Thakur.
PiP-MColl: Process-in-Process-based Multi-object MPI Collectives.
2023 IEEE International Conference on Cluster Computing (CLUSTER).

TPDS

Yujia Zhai, Elisabeth Giem, Kai Zhao, Jinyang Liu, Jiajun Huang, Bryan Wong, Christian Shelton, Zizhong Chen.
FT-BLAS: A Fault Tolerant High Performance BLAS Implementation on x86 CPUs.
IEEE Transactions on Parallel and Distributed Systems.

ICS '23

Shixun Wu, Yujia Zhai, Jinyang Liu, Jiajun Huang, Zizhe Jian, Bryan M. Wong, Zizhong Chen.
Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs.
2023 InternationalConference on Supercomputing (ICS ’23), June 21–23, 2023, Orlando, FL, USA.

ICS '21

Yujia Zhai, Elisabeth Giem, Quan Fan, Kai Zhao, Jinyang Liu, Zizhong Chen.
Significantly Improving Lossy Compression Quality based on An Optimized Hybrid Prediction Model.
Proceedings of the ACM International Conference on Supercomputing 2021.