ADMA2009 is honored to have the following prominent professors to deliver keynote speeches in our program. The detailed program will be out soon.
Edward Y. Chang
Director of Research, Google China
Title: Confucius and "its" Intelligent Disciples
Abstract: Confucius is a great teacher in ancient China. His theories and principles were effectively spread throughout China by his disciples. Confucius is the product code name of Google Knowledge Search product, which is built at Google Beijing lab by my team. In this talk, I present Knowledge Search key disciples, which are machine learning subroutines that generates labels for questions, that matches existing answers to a question, that evaluates quality of answers, that ranks users based on their contributions, that distills high-quality answers for search engines to index, etc. I will also present the scalable machine learning services that we built to make these disciples effective and efficient.
Edward Chang joined the department of Electrical & Computer Engineering at University of California, Santa Barbara, in September 1999. Ed received his tenure in March 2003, and was promoted to full professor of Electrical Engineering in 2006. His recent research activities are in the areas of distributed data mining and their applications to rich-media data management and social-network collaborative filtering. His research group (which consists of members from UC, MIT, Tsinghua, PKU, and Google) recently parallelized SVMs (NIPS 07), PLSA (KDD 08), Association Mining (ACM RS 08), Spectral Clustering (ECML 08), and LDA (WWW 09) (see MMDS/CIVR keynote slides for details) to run on thousands of machines for mining large-scale datasets. Ed has served on ACM (SIGMOD, KDD, MM, CIKM), VLDB, IEEE, WWW, and SIAM conference program committees, and co-chaired several conferences including MMM, ACM MM, ICDE, and WWW. Ed is a recipient of the IBM Faculty Partnership Award and the NSF Career Award. He heads Google Research in China since March 2006. He received his M.S. in IEOR and M.S. in Computer Science from UC Berkeley and Stanford, respectively; and received his PhD in Electrical Engineering from Stanford University in 1999.
Prof. Charles Ling
Department of Computer Science
University of Western Ontario, Canada
Title:From Machine Learning to Child Learning
Abstract:Machine Learning endeavors to make computers learn and improve themselves over time. It is originated from analyzing human learning, and is now maturing as computers can learn more effectively than human for many specific tasks, such as adaptive expert systems and data mining. The effective and fruitful research in machine learning can now be used to improve our thinking and learning, especially for our children. In this talk, I will discuss my efforts in using machine learning (and AI) for child education in Canada and China. In early 2009, I hosted a TV series (天才孩子家家有) in a major talk show in China (湖湘讲堂). The impact of such work in China and around the world can be huge.
Professor Charles X. Ling earned his Msc and PhD from the Department of Computer Science at Univ of Pennsylvania in 1987 and 1989 respectively. Since then he has been a faculty member in Computer Science at University of Western Ontario. His main research areas include machine learning, data mining, and cognitive modeling.
He has published over 100 research papers in journals (such as Machine Learning, JAIR, JMLR, JKDD, IEEE TKDE, and Bioinformatics) and international conferences (such as IJCAI, KDD, ICDM, and ICML). He is also the Director of Data Mining Lab, leading data mining development in CRM, Bioinformatics, and the Internet. He has managed several major data-mining projects for banks and insurance companies in Canada.
the School of Computer Science and Engineering, South China university of Technology, Guangzhou, China
Sensitivity Based Generalization Error for Supervised Learning Problems with Application in Feature Selection
Generalization error model provides a theoretical support for a classifier's performance in terms of prediction accuracy. However, existing models give very loose error bounds.
This explains why classification systems generally rely on experimental validation for their claims on prediction accuracy. In this talk we will revisit this problem and explore the idea of developing a new generalization error model based on the assumption that only prediction accuracy on unseen points in a neighbourhood of a training point will be considered, since it will be unreasonable to require a classifier to accurately predict unseen points "far away" from training samples. The new error model makes use of the concept of sensitivity measure for multiplayer feedforward neural networks (Multilayer Perceptrons or Radial Basis Function Neural Networks). The new model will be applied to the feature reduction problem for RBFNN classifiers. A number of experimental results using datasets such as the UCI, the 99 KDD Cup, and text categorization, will be presented.
Daniel S. Yeung (Ph.D., M.Sc., M.B.A., M.S., M.A., B.A.) is the President of the IEEE Systems, Man and Cybernetics (SMC) Society, a Fellow of the IEEE and an IEEE Distinguished Lecturer. He is currently a Chair Professor of the School of Computer Science and Engineering at the South China University of Technology, China. He received his Ph.D. degree in applied mathematics from Case Western Reserve University . His academic work includes a Chair Professor and Head at the Department of Computing, and Head of the Management Information Unit at the Hong Kong Polytechnic University . He has also served as an Associate Head/Principal Lecturer at the Department of Computer Science, City Polytechnic of Hong Kong, a tenured Assistant Professor at the School of Computer Science and Technology and an Assistant Professor at the Department of Mathematics, both at Rochester Institute of Technology, Rochester, New York. He also held industrial and business positions as a Technical Specialist/Application Software Group Leader at the Computer Consoles, Inc., Rochester , New York , an Information Resource Sub-manager/Staff Engineer at the Military and Avionics Division, TRW Inc., San Diego , California , and an Information Scientist of the Information System Operation Lab, General Electric Corporate Research and Development Centre, Schenectady , New York . His current research interests include neural-network sensitivity analysis, data mining, and pattern recognition. He was the Chairman of IEEE Hong Kong Computer Chapter (91and 92), an associate editor for both IEEE Transactions on Neural Networks and IEEE Transactions on SMC (Part B), and for the International Journal on Wavelet and Multiresolution Processing. He has served as the President-Elect, member of the Board of Governor, Vice President for Technical Activities, and Vice President for Long Range Planning and Finance for the IEEE SMC Society. He co-founded and served as a General Co-Chair since 2002 for the International Conference on Machine Learning and Cybernetics held annually in China . He also serves as a General Co-Chair (Technical Program) of the 2006 International Conference on Pattern Recognition. He is also the founding Chairman of the IEEE SMC Hong Kong Chapter.
Invited Application Speaker
Associate Professor ,the University of Technology , Sydney (UTS), Australia
Title: Data Mining in Financial Markets
Abstract :The ongoing global financial recession has dramatically affected public confidence and market development. An example is the market manipulation schemes hidden in capital markets, which have caused losses in billions of dollars, dramatically damaging public confidence and contributing to the global financial and credit crisis. While most investors lost during market falls, for instance, sophisticated speculators can manipulate markets to make money by illegally using a variety of maneuvering techniques such as wash sales. With financial globalization, manipulators are becoming increasingly imaginative and professional, employing creative tactics such as using many nominee accounts at different broker-dealers. However, regulators currently are short on effective technology to promptly identify abnormal trading behavior related to complex manipulation schemes. As a result, shareholders are complaining that too few market manipulators were being caught. In this talk, I will discuss issues related to this topic, present case studies and lessons learned in identifying abnormal trading behavior in capital markets. I will discuss the use of data mining techniques in this area such as activity mining, combined mining, adaptive mining and domain-driven data mining.
Dr. Longbing Cao is an Associate Professor in the Faculty of Engineering and Information Technology, at the University of Technology , Sydney (UTS), Australia . He is the Director of the Data Sciences & Knowledge Discovery Research Lab at the Centre for Quantum Computation and Intelligent Systems (QCIS) at UTS. He is also the Research Leader of the Data Mining Program at the Capital Markets Cooperative Research Centre, Australia . His research interests focus on data mining, multi-agent systems, and the integration of agents and data mining. He is a Senior Member of the IEEE Computer Society and SMC Society. He has over 100 publications, including monographs and edited books. He has led the investigation of around 20 research and industry projects in data mining and intelligent systems. His real-world experience and leadership covers domains such as telecommunications, capital markets, social security, health insurance and e-commerce. He has served as an organizer and program committee member on over 30 international conferences and workshops in data mining and multi-agent systems.