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.
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.
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:
- Co-Chair of the 13th International Workshop on Scheduling and Resource Management for Parallel and Distributed Systems (SRMPDS 2017).
- Technical Program Committee: SIMUL 2016; SIMUL 2015; DAAC 2017.
- Subreviewer: COMPUTATION TOOLS 2015; Euro-Par 2017; CLUSTER 2017; HiPC 2017; WoWS 2017;
- 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:
- Online Course Neural Networks and Deep Learning by deeplearning.ai on Coursera. Certificate earned on August 14, 2017
- Online Course Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai on Coursera. Certificate earned on August 15, 2017
- Online Course Structuring Machine Learning Projects by deeplearning.ai on Coursera. Certificate earned on August 17, 2017
- Online Course DAT203x: Data Science and Machine Learning Essentials HONOR CODE CERTIFICATE from edX by Microsoft.
- The 10th Marathon of Parallel Programming Contest, The First Place, Results.
- The 5th Spanish Parallel Progamming Contest, The First Place, Results, Certificate.
- Online Course Scalable Machine Learning Statement of Accomplishment (SoA) from edX by University of California, Berkeley.
- Online Course Mining Massive Datasets Statement of Accomplishment (SoA) from Coursera by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Stanford University.
- Online Course Machine Learning Statement of Accomplishment (SoA) from Coursera by Andrew Ng, Stanford University.
- 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 interests in: large scale network simulation or modeling and simulation for HPC systems or performance modeling of workflow over distributed infrastructure. .