Sheng Di
Biography
Dr. Sheng Di is an assistant computer scientist at Argonne National Laboratory and a senior member of IEEE. He is a fellow of Northwestern Unvierity Argonne National Laboratory Institute of Science and Engineering (NAISE).
Dr. Di received his master degree from Huazhong University of Science and Technology in 2007 and Ph.D degree from The University of Hong Kong in 2012. With 10+ years solid experience in developing distributed computing projects (including Cloud, HPC/Grid, P2P) via C, Java, Fortran, and Bash shell, his strengths include theoretical analysis, system design/development and performance optimization. He has a broad research interest, including data compression, data analysis, fault tolerance, resource discovery in HPC, Grid computing, P2P and Cloud computing, analysis and prediction of Google workload based on Google trace, optimization of resource allocation, virtual machine migration over WAN, case-based reasoning via Mapreduce, etc. He published 60+ refereed journal and conference papers (including TPDS, TC, TCC, TKDE, JPDC, SC’XY, IPDPS, HPDC, ICPP, CLUSTER, IWQoS, CCGrid, HiPC, Grid, CLOUD, UCC, EuroPar, ICPADS, and so on) and serve as program committee members 10+ times.
Dr. Di is participating in 6 important projects, four of which are DOE ECP projects. He developed several critical libraries, including SZ (a lossy compressor for scientific data) and Z-checker (a tool for assessing lossy compression quality), which are parts of the two ECP projects. He developed an adaptive impact-driven detector for silent data corruptions (called AID) based on data analytics and prediction methods, which serves as a critical module in the NSF ALETHEIA project.
More detailed information can be found in http://www.mcs.anl.gov/~shdi
Dr. Di received his master degree from Huazhong University of Science and Technology in 2007 and Ph.D degree from The University of Hong Kong in 2012. With 10+ years solid experience in developing distributed computing projects (including Cloud, HPC/Grid, P2P) via C, Java, Fortran, and Bash shell, his strengths include theoretical analysis, system design/development and performance optimization. He has a broad research interest, including data compression, data analysis, fault tolerance, resource discovery in HPC, Grid computing, P2P and Cloud computing, analysis and prediction of Google workload based on Google trace, optimization of resource allocation, virtual machine migration over WAN, case-based reasoning via Mapreduce, etc. He published 60+ refereed journal and conference papers (including TPDS, TC, TCC, TKDE, JPDC, SC’XY, IPDPS, HPDC, ICPP, CLUSTER, IWQoS, CCGrid, HiPC, Grid, CLOUD, UCC, EuroPar, ICPADS, and so on) and serve as program committee members 10+ times.
Dr. Di is participating in 6 important projects, four of which are DOE ECP projects. He developed several critical libraries, including SZ (a lossy compressor for scientific data) and Z-checker (a tool for assessing lossy compression quality), which are parts of the two ECP projects. He developed an adaptive impact-driven detector for silent data corruptions (called AID) based on data analytics and prediction methods, which serves as a critical module in the NSF ALETHEIA project.
More detailed information can be found in http://www.mcs.anl.gov/~shdi
Presentations
ACM Student Research Competition
Poster
Reception
