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.
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.
- 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:
- Professional Membership: Association for Computing Machinery (ACM).
- Co-Chair: 16th SRMPDS 2020;15th SRMPDS 2019; 14th SRMPDS 2018; 13th SRMPDS 2017; 2nd AI-Science 2019;
- Editorship: Journal of Future Generation Computer Systems, FGCS.
- Technical Program Committee: IARIA SIMUL’16; IARIA SIMUL’15; DAAC’17; DAAC’18; IARIA ICDS’19; AI-Science 2019;DLS’19;ICDCS’20; SC’20(ML and HPC).
- Conference Reviewer: COMPUTATION TOOLS’15; Euro-Par’17; CLUSTER 2017; HiPC’17; WoWS’17; IPDPS’18; HiPC; ICACCP; ICDS’19
- Journal Reviewer: Algorithms-MDPI; Sustainability–MDPI; Sensors; FGCS(Outstanding Reviewer); AJCSIT–iMedPub (Outstanding Reviewer); IEEE-Access; JOCS(Rec.).
- 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:
- [2020/05] The IMPACT ARGONNE award for notable achievement in Innovation [Award Plaque].
- [2019/11] Our solution: “Real-Time Analysis of Streaming Synchrotron Data over High-Speed WAN using Deep Learning and HPC” won the Top Recognition Award in the Technical Challenge at SC’19 by SciNet, for an Exemplary Blend of Network, Computing, and Storage [Recognition Ceremony, Award, Photo] .
- [2019/03] The 2019 Data Science and Learning Devision Pacesetter Award by the Argonne National Laboratory, photo.
- [ 2018/06]The Extraordinary Doctorate Award by the Universitat Autònoma de Barcelona, certificate.
- [2018/02] Online Course Sequence Models by deeplearning.ai on Coursera. Certificate earned on February 4, 2018
- [2017/11] Online Course Convolutional Neural Networks by deeplearning.ai on Coursera. Certificate earned on November 30, 2017
- [2017/08] Online Course Structuring Machine Learning Projects by deeplearning.ai on Coursera. Certificate earned on August 17, 2017
- [2017/08] Online Course Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai on Coursera. Certificate earned on August 15, 2017
- [2017/08] Online Course Neural Networks and Deep Learning by deeplearning.ai on Coursera. Certificate earned on August 14, 2017
- [2015/11] Online Course DAT203x: Data Science and Machine Learning Essentials HONOR CODE CERTIFICATE from edX by Microsoft.
- [2015/10] The 10th Marathon of Parallel Programming Contest, The First Place, Results, Certificate.
- [2015/09] The 5th Spanish Parallel Progamming Contest, The First Place, Results, Certificate.
- [2015/06] Online Course Scalable Machine Learning Statement of Accomplishment (SoA) from edX by University of California, Berkeley.
- [2015/08] Online Course Mining Massive Datasets Statement of Accomplishment (SoA) from Coursera by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Stanford University.
- [2014/09] Online Course Machine Learning Statement of Accomplishment (SoA) from Coursera by Andrew Ng, Stanford University.
- [2014/09] The IV Spanish Parallel Progamming Contest , The Second Place, Results, Certificate, how.
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.