Zhengchun Liu (刘正春)

Postdoctoral Appointee at Argonne National Laboratory

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Short Bio.

I am a Postdoctoral Appointee in the Mathematics and Computer Science Division at Argonne National Laboratory, closely working with Dr. Rajkumar Kettimuthu and Prof. Ian Foster. I received my bachelor’s degree in Manufacturing Engineering of Flight Vehicles in 2010 and my master’s degree in Guidance, Navigation and Control in 2013, both from the Northwestern Polytechnic University, China; and my Ph.D. in Computer Science (awarded the highest honor Summa Cum Laude; and International doctoral research component mention) in 2016, from the Universitat Autònoma de Barcelona, Spain, under the supervision of Prof. Emilio Luque.

For more details and contact info, please see my curriculum vitae with pdf or html.

Research Highlights

At Argonne, I develop 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, I also work on smart computing which adds smartness to computing edges. I contribute to the DoE RAMSES (Robust Analytic Models for Science at Extreme Scales) project.

Research Interests:

I am 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 are interested in any one of: performance modeling, simulation, high performance computing, distributed systems, workflow over distributed infrastructure and smart computing/storage/network.

Call for paper for the 14th International Workshop on Scheduling and Resource Management for Parallel and Distributed Systems SRMPDS’18.

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