PhD candidate in Computer Science at the University of Kentucky specializing in high-performance computing, error-bounded lossy compression, and data-intensive system optimization.

  • Location: Lexington, KY, USA
  • Email: [Enable JS to view]
  • Web: jpcoding.github.io
  • LinkedIn: pu-jiao-4b309b212

Technical Strengths

  • HPC & MPI/OpenMP/CUDA
  • Error-bounded compression
  • Performance modeling
  • Parallel algorithms
  • Python · C++ · CUDA
  • Scientific data systems

Research Interests

High-performance computing, quantity-of-interest preservation, storage-efficient analytics, compression artifacts mitigation, and scalable data services.

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Education

  • 2019–2026 (exp.)
    Ph.D., Computer Science — University of Kentucky

    Research on error-bounded lossy compression, QoI preservation, and large-scale runtime optimization.

  • 2016–2019
    M.S., Civil Engineering — Missouri University of Science and Technology
  • 2014–2016
    M.S., Structural Engineering — Institute of Engineering Mechanics, China Earthquake Administration
  • 2010–2014
    B.Eng., Civil Engineering — Xi'an Jiaotong University

Selected Experience

  • 2023–Present
    Graduate Research Assistant, University of Kentucky

    Developing quantization-aware interpolation, artifact mitigation techniques, and QoI-preserving compressors deployed on leadership-class HPC systems.

  • 2022–2024
    Graduate Teaching Assistant, CS218 / CS216

    Designed labs, mentored 40+ students per term, and delivered stand-in lectures on advanced programming interfaces and systems.

Selected Publications

  1. IPDPS — Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation
  2. VLDB — QPET: A Versatile and Portable Quantity-of-Interest-Preservation Framework for Error-Bounded Lossy Compression
  3. IPDPS — Improving the Efficiency of Interpolation-Based Scientific Data Compressors with Adaptive Quantization Index Prediction
  4. HiPC — Characterization and Detection of Artifacts for Error-Controlled Lossy Compressors
  5. VLDB — Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data

Teaching & Mentorship

  • CS218, Advanced Progmming and Operating System Interfaces — Spring 2026
    University of Kentucky, Computer Science
  • CS218, Advanced Progmming and Operating System Interfaces — Fall 2025
    University of Kentucky, Computer Science
  • CS216, Introductin to Software Engineering Techniques — Spring 2025
    University of Kentucky, Computer Science

Service & Leadership

  • Reviewer for HPC, compression, and systems venues (IPDPS, SC workshops, HiPC).
  • Contributor to open-source scientific workflows and compression evaluation tooling.
  • Volunteer mentor for undergraduate research projects in data analytics.