Zhengchun Liu (刘正春)

Assistant Computer Scientist at Argonne National Laboratory

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Short Bio.

Zhengchun Liu is an Assistant Computer Scientist at the Data Science and Learning division of Argonne National Laboratory, and a Scientist At-Large of the Consortium for Advanced Science and Engineering at The University of Chicago. Before this, he was a research scientist at the University of Chicago from 2018.03 to 2019.08, and a Postdoc at the Mathematics and Computer Science division of Argonne National Laboratory from 2016.09 to 2018.03.

For more details and contact info, please see his resume, in pdf or html.

Research Highlights

Zhengchun Liu works on applying data science and machine learning techniques to transform understanding of the behavior of computer systems in extreme-scale science environments. Specifically, it includes explaining why performance does not meet design goals, predicting the behavior of computer systems and optimizing it for application in production. Recently, he focuses his effort on developing deep learning models to advance X-ray tomography images. In a long run, he also works on exploring the architecture, methods, and algorithms needed to support future scientific computing systems that are capable of self-configuration, self-optimization and self-manage etc.

Research Interests:

  • Wide-area data transfer, performance modeling and optimization;
  • Data Science and Learning for science;
  • High Performance Computing and Artificial Intelligence;
  • Embedded System, Real-Time Operating System and Edge Computing.

Professional Memberships and Activities:


  • C/C++, Python, MATLAB and Embedded C Programming;
  • Parallel software development, including MPI, CUDA, OpenMP, OpenCL;
  • Massive datasets mining.
  • Embedded system, real-time OS; hardware and firmware development.

Professional Training, Honors & Awards:

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 or smart computing/storage/network. Visiting scholar/student is welcomed as well.

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