• About me

    I am a Ph.D. student of Georgia Tech majoring in Computational Science and Engineering. I obtained both my bachelor's and Master's degree from Huazhong University of Science and Technology (HUST), China in Electronic Information Engineering. After graduation from HUST in 2006, I started to work for IBM as a Staff R&D Engineer in Shanghai till July 2009. My work at IBM mainly focused on programming model research, CELL SDK, library development, and workload optimization on IBM CELL/B.E. multi-core processor.

  • research interests

    • My research interests are primarily in the areas of High Performance Computing, including numerical and discrete algorithms, high performance computional biology, large scale physical simulations, parallel graph algorithms and GPU-based computing.

    research projects

    Large Scale Simulation for Stokesian Dynamics of Biological Macromolecules

    Computational simulation of the motions and interactions between macromolecules in the cell is an indispensible tool for gaining insight into the mechanisms of many cellular processes. Brownian dynamics has been a staple technique for such simulations, but by itself it is not able to accurately model the crowded condition in vivo, a condition which has only recently become appreciated. We have proposed using a more advanced technique, called Stokesian dynamics, which models both the long-range and short-range hydrodynamic interactions between macromolecules, the latter being critical in crowded conditions. To perform such simulations on a scale approaching that of an entire cell and on time-scales of celluar processes, advanced computational techniques, including parallel computing, are necessary. The goal is to develop a powerful omputational tool that is useful to the cell biology and biomedical communities for easily studying a wide variety of processes ultimately important to human health.

    Parallel de novo Assembler for Next Generation Genome Sequencing

    Recent Next Generation Sequencing (NGS) technologies produce a very large number of reads in a short amount of time. They have reduced the experimental cost per base significantly with their high throughput. This way they have opened up opportunities to study organisms at the genome level, promising a deeper understanding of genome regulation and biological mechanisms. A thorough study can assist in designing more effective drugs to cure diseases. Moreover, with NGS technologies researchers can study the evolution of viruses and bacteria at an unprecedented pace, for example during a recent E. coli outbreak in Europe. Such studies can for example help to accelerate vaccine development.

    The large data produced by sequencing machines requires an efficient assembly process in terms of running time and memory consumption. Our assembler PASQUAL, short for PArallel SeQUence AssembLer, is designed for shared memory parallelism, using OpenMP due to its good tradeoff between performance and programmer productivity. Shared memory parallelism has become mainstream with the widespread production of multicore commodity processors. For PASQUAL we follow the OLC approach and use a careful combination of tailored algorithms and data structures to obtain high-quality solutions.