Keynote Speakers
Title: Computing Issues for Big Data – Theory, Systems, and Applications
Dr. Chunming Hu
Vice Dean of School of Computer Science and Engineering
Beihang University
胡春明博士
北京航空航天大学计算机学院
Biographical Sketch: Dr. Chunming Hu is an associate professor at School of Computer Science, Beihang University. He got his Ph.D degree in 2006 at Beihang University. Now he is the vice dean of School of Computer Science, Beihang University. His research interests are distributed systems, software middleware, system virtualization and resource scheduling in cloud systems. He is elected as the Chair of the Academic Committee of YOCSEF, and member of System Software Technical Committee of China Computer Forum. He is also serving for W3C China to promote the web standards in China.
Abstract: Big data may contain big values, but also brings lots of challenges to the computing theory, architecture, framework, knowledge discovery algorithms, and domain specific tools and applications. Beyond the 4-V or 5-V characters of big datasets, the data processing shows the features like inexact, incremental, and inductive manner. This brings new research opportunities to research community across theory, systems, algorithms, and applications. Is there some new “theory” for the big data? How to handle the data computing algorithms in an operatable manner? This report shares some view on new challenges identified, and covers some of the application scenarios such as micro-blog data analysis and data processing in building next generation search engines.
Title: Big Data Research From Management and Business Perspective: Thoughts For Debating
Biographical Sketch: Professor of MIS at Ohio University, USA. Main research interests: big data management and application, business analytics and intelligence, using IS/IT to support decision-making, Cross-culture research in IS, eLearning & Education; Having teaching/research experience in established research univ. in Australia, Singapore, Hong Kong, US and mainland China, including Harvard University, University of New South Wales, National Univ. of Singapore, etc.
The talk with bring some initial thoughts from management and business perspective, for the purpose of discussion and debating, rather than concluding.
Title: Scaling Distributed Machine Learning with the Parameter Server
Biographical Sketch: Mu Li is a senior architect of Institute of Deep Learning at Baidu. His research interests lie in machine learning and distributed system. He built a core machine learning system for Baidu’s online computational advertising, which scales to hundreds of billions samples and parameters. He is also a Ph.D candidate at CSD of CMU working with Alex Smola and Dave Andersen. He got his master and bachelor degrees from Shanghai Jiao Tong University.
Abstract: We propose a parameter server framework for distributed machine learning problems. Both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices. The framework manages asynchronous data communications between nodes, and supports flexible consistency models, elastic scalability, and continuous fault tolerance. To demonstrate the scalability of the proposed framework, we show experimental results on petabytes of real data with billions of samples and parameters on problems ranging from Sparse Logistic Regression to Latent Dirichlet Allocation and Distributed Sketching.
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