Zhengchun Liu (刘正春)

Postdoctoral Appointee at Argonne National Laboratory

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Short Bio.

Zhengchun is a Postdoctoral Appointee in the Mathematics and Computer Science Division at Argonne National Laboratory. He received his bachelor’s degree in Manufacturing Engineering of Flight Vehicles in 2010 and his master’s degree in Guidance Navigation and Control in 2013, both from the Northwestern Polytechnic University, China; and his Ph.D. in Computer Science (awarded Summa Cum Laude, the highest honor; and International doctoral research component mention) in 2016, from the Universitat Autònoma de Barcelona, Spain. For more details, please see his curriculum vitae, with pdf or html.

Research Highlights

Zhengchun develops end-to-end analytical performance models to transform understanding of the behavior of science workflows in extreme-scale science environments. These models are developed to predict the behavior of a science workflow before it is implemented, to explain why performance does not meet design goals, and to architect science environments to meet workflow needs. Powered by the ability of explain and predict performance, He also works on smart computing which adds smartness to computing edges. He contributes to the DoE RAMSES (Robust Analytic Models for Science at Extreme Scales) project.

Research Interests:

He is interested in Computer Science related research, mostly include:

  • Wide-area data transfer, GridFTP/Globus.org, explaining, modeling & optimizing data transfer
  • High Performance Computing, Big-Data and Data Mining, Feature Engineering
  • Machine Learning and Artificial Intelligence
  • Complex System, Modeling and Simulation, Agent Based Modeling and Simulation
  • Embedded Technique, Real-Time Operating System
  • Decision Support System, Operations Research

Recent Professional Service:


  • Proficient in C/C++, Python, MATLAB and Embedded C Programming.
  • Extensive experience with Parallel software development, including MPI and programming models for multicore and heterogeneous architectures (e.g. CUDA, OpenMP, OpenCL).
  • Familiar with cluster computing framework (e.g., Apache Spark) and massive datasets mining (e.g., numpy, pandas, scikit-learn and matplotlib).
  • Extensive development experience with backend software on Linux.
  • Extensive experience with embedded system, real-time OS, hardware and firmware development.
  • Simulation with NS3, Netlogo etc.

Professional Training & Certifications:

NEWS: We are looking for intern students, contact me if you have experience or interests in: large scale network simulation or modeling and simulation for HPC systems or performance modeling of workflow over distributed infrastructure. .


Prototype before polishing. Get it working before you optimize it.