报告1：大数据的理论基础 特邀讲者：陈国良

深圳大学和中国科学技术大学教授、博士生导师，中国科学院院士，全国首届高等学校教学名师。现任深圳大学计算机与软件学院和中国科学技术大学软件学院院长，国家高性能计算中（合肥）主任，国际高性能计算（亚洲）常务理事和中国计算机学会理事等。

陈国良教授主要研究领域为并行算法和高性能计算及其应用等。先后承担了国家863计划、国家攀登计划、国家973计划、国家自然科学基金等10多项科研项目。取得了多项被国内外广泛引用、达国际先进水平的研究成果。发表论文200多篇，出版学术著作和教材10部。曾获国家科技进步二等奖、教育部科技进步一等奖、中科院科技进步二等奖、国际级教学成果二等奖、水利部大禹一等奖、安徽省科技进步二等奖、2009年度安徽省重大科技成就奖等共20余项，并获863计划15周年先进个人中亚贡献奖和宝钢教育基金优秀教师特等奖以及安徽省劳动模范光荣称号。

多年来，陈国良教授围绕着并行算法的教学与研究，逐渐形成了“算法理论-算法设计-算法实现-算法应用”一套完整的并行算法学科体系，提出了“并行机结构-并行算法-并行编程”一体化的并行计算研究方法，建立了我国第一个国家高性能计算中心，营造了我国并行算法类的科研和教学基地，培养了100多名研究生，是我国非数值并行算法研究的学科带头人，在国内外学术界和教育界有一定的影响和地位。

报告提要：大数据应用是当前IT领域的研究和应用热点。但是，目前的研究多集中于系统和应用层面，理论基础方面的探讨相对较少。我们以计算复杂性理论为基础，研究大数据的可计算性问题和计算复杂性问题，探索大数据处理的基本策略、处理技术和研究方法学，以其变革大数据的研究模式，推动大数据的应用发展。

 

报告2：大数据抽象：度量空间数据管理与挖掘-以索引为例 特邀讲者：毛睿

深圳大学计算机与软件学院副教授，主要研究方向为大数据索引分析和高性能计算。分别于1997年和2000年在中国科学技术大学获计算机科学学士和硕士学位;于2006年和2007年在美国得克萨斯大学奥斯汀分校获统计学硕士和计算机科学博士学位。2007～2010年在甲骨文美国公司任高级技术员。于2010年加入深圳大学计算机与软件学院，现任国家高性能计算中心深圳分中心、广东省普及型高性能计算机重点实验室和深圳市服务计算与应用重点实验室常务副主任，中国计算机学会高性能计算专业委员会及数据库专业委员会委员。先后在国内外期刊会议上发表论文40多篇，提出了通用相似性索引领域理论模型--支撑点空间模型，获SISAP2010和BIBE2003国际会议Best Paper。

报告提要：数据种类的多样性是大数据问题带来的主要挑战之一。通用的数据处理技术因其广泛的适用性和相对低的开发成本，一直受到商业数据库管理系统的亲睐。从专用到通用的演进一直贯穿于数据库管理系统的发展历程中。度量空间数据处理技术把数据抽象成度量空间中的点，把数据间相似性的衡量抽象成满足三角不等式的距离函数，只利用三角不等式进行数据的索引、筛选和挖掘等处理工作，具有高度的通用性。以此模型为基础构建的通用大数据管理挖掘框架是应对大数据variety挑战的有效手段之一。经过多年的研究，度量空间索引领域已经形成了基本体系，取得了较为丰富的成果，为度量空间数据管理挖掘研究提供了一定的基础。

 

报告3：Learning in the Model Space for Cognitive Fault Diagnosis 特邀讲者：姚新

Bio: Xin Yao received the B.Sc. degree from the University of Science and  Technology of China (USTC), Hefei, China, in 1982, the M.Sc. degree from the North China Institute of Computing Technology, Beijing, China, in 1985, and the Ph.D. degree from USTC in 1990.

From 1985 to 1990, he was an Associate Lecturer and Lecturer with USTC, while working toward the Ph.D. degree in simulated annealing and evolutionary algorithms. In 1990, he was a Postdoctoral Fellow with the Computer Sciences Laboratory, Australian National University, Canberra, Australia, where he continued his work on simulated annealing and evolutionary algorithms. In 1991, he was with the Knowledge-Based Systems Group, Commonwealth Scientific and Industrial Research Organization, Division of Building, Construction and Engineering, Melbourne, Australia, where he worked primarily on an industrial project on automatic inspection of sewage pipes. In 1992, he returned to Canberra to take up a lectureship in the School of Computer Science, University College, University of New South Wales, Australian Defense Force Academy, Sydney, Australia, where he was later promoted to a Senior Lecturer and Associate Professor. Since April 1999, he has been a Professor (Chair) of computer science in the University of Birmingham, Birmingham, U.K. He is currently the Director of the Center of Excellence for Research in Computational Intelligence and Applications, School of Computer Science, University of Birmingham, Birmingham, U.K. and also a Changjiang (Visiting) Chair Professor (Cheung Kong Scholar) with the Nature Inspired Computation and Applications Laboratory, School of Computer Science and Technology, USTC. He has given more than 50 invited keynote and plenary speeches at conferences and workshops worldwide. He has more than 300 referenced publications. His major research interests include evolutionary artificial neural networks, automatic modularization of machine learning systems, evolutionary optimization, constraint-handling techniques, computational time complexity of evolutionary algorithms, coevolution, iterated prisoners dilemma, data mining, and real-world applications. Dr. Yao was the Editor-in-Chief of the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION from 2003 to 2008, an Associate Editor or editorial board member of 12 other journals, and the Editor of the World Scientific Book Series on Advances in Natural Computation. He was the recipient of the Presidents Award for Outstanding Thesis by the Chinese Academy of Sciences for his Ph.D. work on simulated annealing and evolutionary algorithms in 1989. He was the recipient of the 2001 IEEE Donald G. Fink Prize Paper Award for his work on evolutionary artificial neural networks.

报告提要： It is a great challenge to learn from noisy and high dimensional data streams, especially when the data volume is large and concept drift occurs in the data. This talk first introduces the basic ideas behind the learning-in-the-model-space approach, which carries out learning in a model space instead of the signal space. It then illustrates the application of this approach using case studies in fault diagnosis. Some of the key research issues in this approach, including model distance calculation and the co-learning of model parameters and model distance metrics, will be mentioned. Finally, the implication of such an approach in dealing with the Big Data will be discussed and other related topics highlighted.