Big Data and High Performance Computing Big data and high performance computing focus on academic research and technology development in areas of high performance computing platform architecture, parallel algorithm design and optimization, bio-computing modeling, information retrieval, massive information processing, high-performance computing, and cloud computing application development. In recent years, we have published more than 100 papers on international journals and conferences, e.g. Physical Review E, Applied Soft Computing, Computer Methods in Biomechanics and Biomedical Engineering, Neural Computing and Applications, Cluster Computing, Journal of Supercomputing. We are undertaking more than 10 state-level research projects including National 863 program, the Ministry of Education specialized major project, the National Natural Science Foundation of major research projects, the National Development and Reform Commission next generation of Internet demonstration projects. We also undertake more than 20 provincial and ministerial level research projects including Guangdong provincial cutting-edge & key technology innovation specialized major project and natural science Foundation major basic research project. We are awarded second prize of national science and technology progress award and the first prize of Ministry of Education science and technology progress award. We have a board academic exchange and cooperation in related scientific directions with Carnegie Mellon University, USA, Georgia State University, USA, and the Research School of Beijing Genomics Institute in Shenzhen. Our team has 2 professors, 1 associate professor, and 2 lecturers. Professors in the team: Prof. Shoubin Dong Prof. Kejing He Information Retrieval and Big Data Processing Our major research areas include distributed search engine architecture and key technology,
real-time massive data processing technology, new massive data storage architecture, Bio-gene big data compression and indexing, image retrieval, news and advertisement synergism and personalized recommendation, information filtering, deep learning and its applications. Figure: Distributed search engine platform for next generation internet High Performance Computing and Cloud Computing Major research directions include application-oriented high performance computing and cloud computing platform structure, computational task modeling and scheduling optimization, parallel and distributed algorithm design, parallel algorithm optimization under heterogeneous computer structures, application modeling and high performance computing method such as bio-computing, continuous and discrete elements computational model, and application development for high performance computing and cloud computing.
Figure: Bioinformatics intelligent computing platform for high throughput sequencing 大数据与高性能计算 大数据与高性能计算学科方向在高性能计算平台体系结构 并行算法设计及优化 生物计算建模 信息检索 海量信息处理 高性能计算和云计算应用开发等领域开展学术研究与技术开发工作, 近年来在 Physical Review E Applied Soft Computing Computer Methods in Biomechanics and Biomedical Engineering Neural Computing and Applications Cluster Computing Journal of Supercomputing 等国际期刊及会议上发表论文 100 余篇 承担了国家 863 计划项目 教育部重大专项 国家自然科学基金重大研究计划项目 国家发改委下一代互联网示范工程项目等国家级项目 10 余项, 广东省前沿与关键技术创新重大科技专项 自然科学基金重大基础研究培育等省部级项目 20 多项, 获国家科技进步二等奖 教育部科技
进步一等奖和广东省科技进步二等奖各 1 项 学科方向开展了广泛的学术交流与合作, 与美 国卡耐基 - 梅隆大学 美国佐治亚州立大学 深圳华大基因研究院等进行合作研究 学科方 向现有教授 2 人, 副教授 1 人, 讲师 2 人 团队教授 : 董守斌教授 何克晶教授 信息检索与大数据处理 主要研究方向有分布式搜索引擎体系结构及关键技术 实时海量数据处理技术 新型海量数 据存储架构 生物基因大数据压缩及索引 图像检索 新闻及广告协同 / 个性化推荐 信息 过滤 深度学习及其应用等 图 : 下一代互联网分布式搜索引擎平台
高性能计算和云计算 主要研究方向有面向应用的高性能计算 / 云计算平台体系结构 计算任务建模及调度优化 并行 / 分布式算法设计 异构计算体系架构下的并行算法优化 生物计算等的应用建模和高 性能计算方法 连续元和离散元计算模型 高性能计算和云计算应用开发等 图 : 面向高通量测序的生物信息智能计算平台