Wei Jiang
Dept. of Computer Science and Engineering
The Ohio State University

395 Dreese Labs, 2015 Neil Ave
Columbus, OH 43210
Email: jiangwei at cse dot ohio-state dot edu
Office: DL778

I am a fifth-year Ph.D. student in the Department of Computer Science and Engineering at The Ohio State University.
My advisor is Dr. Gagan Agrawal. My research interests include MapReduce and cloud computing, data-intensive comupting, and distributed computing.

I have been working on building map-reduce-like infrastructure systems for large-scale data processing on emerging parallel architecture like clusters of multi-core CPUs and/or many-core GPUs. Specifically, my work focuses on performance modeling/evaluation, code optimization, auto-tuning, and fault tolerance for Map-Reduce-Like models with the goal of providing better programmer productivity and performance efficiency, targeting applications in data mining, graph mining, scientific computing, and irregular programs. Besides, I have done some work in parallel I/O for scientific data, and data storage and management.

In 2011 summer, I was a PhD intern at Pacific Northwest National Laboratory. I participated in the project Future Power Grid Initiative, working on a distributed architecture for deploying Distributed State Estimation (DSE) algorithms in power grid on multiple HPC clusters.

Education

  • 2007 - present, Ph.D. candidate, Dept. of Computer Science and Engineering, The Ohio State University
  • 2003 - 2007, B.S., College of Software, Nankai University, Tianjin, China

    • Teaching

  • Grader, Spring 2012, CSE760 --- Advanced Operating System
  • Instructor, Winter 2012, CSE459.22 --- Programming in C++
  • Instructor, Autumn 2011, CSE459.22 --- Programming in C++
  • Instructor, Spring 2011, CSE459.23 --- Programming in Java
  • Instructor, Winter 2011, CSE459.11 --- The Unix Programming Environment
  • Grader, Autumn 2010, CSE621 --- Introduction to High Perfomance Computing
  • Grader, Spring 2010, CSE651 --- Introduction to Network Security
  • Instructor, Winter 2010, CSE459.11 --- The Unix Programming Environment
  • Grader, Autumn 2009, CSE760 --- Advanced Operating System
  • Grader, Autumn 2008, CSE780 --- Analysis of Algorithms
    		•

    Academic Service

  • Program Committee Member, 2012'WWW Posters Session, 2012

    • Publications

  • SciMATE: A Novel MapReduce-Like Framework for Multiple Scientific Data Formats [PDF]
        Yi Wang, Wei Jiang and Gagan Agrawal.
        IEEE/ACM CCGrid (CCGrid'12), May 2012, Ottawa, Canada.

  • Scheduling Concurrent Applications on a Cluster of CPU-GPU Nodes [PDF]
        Vignesh Ravi, Michela Becchi, Wei Jiang, Gagan Agrawal and Srimat Chakradhar.
        IEEE/ACM CCGrid (CCGrid'12), May 2012, Ottawa, Canada.

  • MATE-CG: A MapReduce-Like Framework for Accelerating Data-Intensive Computations on Heterogeneous Clusters [PDF]
        Wei Jiang and Gagan Agrawal.
        IEEE IPDPS (IPDPS'12), May 2012, Shanghai, China.

  • Ex-MATE: Data-Intensive Computing with Large Reduction Objects and Its Application to Graph Mining [PDF]
        Wei Jiang and Gagan Agrawal.
        IEEE/ACM CCGrid (CCGrid'11), May 2011, Newport Beach, CA, USA.

  • A Map-Reduce System with an Alternate API for Multi-Core Environments [PDF]
        Wei Jiang, Vignesh T. Ravi, and Gagan Agrawal.
        IEEE/ACM CCGrid (CCGrid'10), May 2010, Melbourne, Australia.

  • Supporting Fault Tolerance in a Data-Intensive Computing Middleware [PDF]
        Tekin Bicer, Wei Jiang, and Gagan Agrawal.
        IEEE IPDPS (IPDPS'10), Apr 2010, Atlanta, USA.

  • Comparing Map-Reduce and FREERIDE for Data-Intensive Applications [PDF]
        Wei Jiang, Vignesh T. Ravi, and Gagan Agrawal.
        IEEE Cluster (CLUSTER'09), Aug 2009, New Orleans, USA.

  • Construct U-Disk File System Based on Flash Memory
        Wei Jiang and Bo Zhang.
        Outstanding Bachelor's Thesis, Nankai University, 2007.

    • Software

  • We have been developing several systems that are based on generalized reduction. Our system targets parallelizing data-intensive applications to process large-scale data on clusters of multi-core cpu's and/or many-core gpu's. Our goal is to provide an easy-to-use API as well as an efficient runtime to leverage the computing power in parallel architectures.

    • Links

  • I LOVE NK, Happy her 90th birthday!

  • DBLP

  • PHD Comics

  • ICE SKATING

  • FENCING

    		•