Pu Jiao

I am a PhD candidate in Computer Science at the University of Kentucky. My research focuses on high-performance computing, error-bounded lossy compression, and large-scale system optimization.

Highlights

Programming: C++ (Advanced), Python (Advanced), JavaScript, SQL, R, MATLAB
Frameworks: MPI, OpenMP, CUDA, NumPy, Pandas, SciPy, scikit-learn, TensorFlow
Cloud & Infra: AWS, Docker, Kubernetes, Linux/Unix, Git, CI/CD
Databases: MongoDB, MySQL, SQLite, Redis
Specializations: HPC, algorithm optimization, data analytics, ML

Education

Ph.D. in Computer Science (Expected May 2026), University of Kentucky, Lexington, KY
M.S. in Civil Engineering, Missouri University of Science and Technology
B.Eng. in Civil Engineering, Xi’an Jiaotong University

Selected Publications

[IPDPS’26] Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation. Jiao, Pu, Sheng Di, Jiannan Tian, Mingze Xia, Xuan Wu, Yang Zhang, Xin Liang, Franck Cappello.
[VLDB’25] QPET: A Versatile and Portable Quantity-of-Interest-Preservation Framework for Error-Bounded Lossy Compression. Liu, Jinyang*, Jiao, Pu*, Zhao, Kai, Liang, Xin, Di, Sheng, Cappello, Franck (*Equal contribution)
[IPDPS’25] Improving the Efficiency of Interpolation-Based Scientific Data Compressors with Adaptive Quantization Index Prediction. Jiao, Pu, Di, Sheng, Xia, Mingze, Wu, Xuan, Liu, Jinyang, Liang, Xin, Cappello, Franck
[HiPC’23] Characterization and Detection of Artifacts for Error-Controlled Lossy Compressors. Jiao, Pu, Di, Sheng, Liu, Jinyang, Liang, Xin, Cappello, Franck
[VLDB’22] Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data. Jiao, Pu, Di, Sheng, Guo, Hanqi, Zhao, Kai, Tian, Jiannan, Tao, Dingwen, Liang, Xin, Cappello, Franck

Contact

Email: jiaopujp [at] gmail [dot] com
Website: https://jpcoding.github.io
LinkedIn: https://www.linkedin.com/in/pu-jiao-4b309b212/
GitHub: https://github.com/jpcoding
Google Scholar: https://scholar.google.com/citations?user=6blhwnoAAAAJ&hl=en