Dr. Lotfi A. Zadeh
(IEEE Medal of Honor - "Nobel" Prize in EE), UC Berkeley, USA

Abstract

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Bio

Lotfi A. Zadeh joined the Department of Electrical Engineering at the University of California, Berkeley, in 1959, and served as its chairman from 1963 to 1968. Earlier, he was a member of the electrical engineering faculty at Columbia University. In 1956, he was a visiting member of the Institute for Advanced Study in Princeton, New Jersey. In addition, he held a number of other visiting appointments, among them a visiting professorship in Electrical Engineering at MIT in 1962 and 1968; a visiting scientist appointment at IBM Research Laboratory, San Jose, CA, in 1968, 1973, and 1977; and visiting scholar appointments at the AI Center, SRI International, in 1981, and at the Center for the Study of Language and Information, Stanford University, in 1987-1988. Currently he is a Professor in the Graduate School, and is serving as the Director of BISC (Berkeley Initiative in Soft Computing).

Until 1965, Dr. Zadeh's work had been centered on system theory and decision analysis. Since then, his research interests have shifted to the theory of fuzzy sets and its applications to artificial intelligence, linguistics, logic, decision analysis, control theory, expert systems and neural networks. Currently, his research is focused on fuzzy logic, soft computing, computing with words, and the newly developed computational theory of perceptions and precisiated natural language.

An alumnus of the University of Teheran, MIT, and Columbia University, Dr. Zadeh is a fellow of the IEEE, AAAS, ACM and AAAI, and a member of the National Academy of Engineering. He held NSF Senior Postdoctoral Fellowships in 1956-57 and 1962-63, and was a Guggenheim Foundation Fellow in 1968. Dr. Zadeh was the recipient of the IEEE Education Medal in 1973 and a recipient of the IEEE Centennial Medal in 1984. In 1989, Dr. Zadeh was awarded the Honda Prize by the Honda Foundation, and in 1991 received the Berkeley Citation, University of California.

In 1992, Dr. Zadeh was awarded the IEEE Richard W. Hamming Medal "For seminal contributions to information science and systems, including the conceptualization of fuzzy sets." He became a Foreign Member of the Russian Academy of Natural Sciences (Computer Sciences and Cybernetics Section) in 1992 and received the Certificate of Commendation for AI Special Contributions Award from the International Foundation for Artificial Intelligence. Also in 1992, he was awarded the Kampe de Feriet Prize and became an Honorary Member of the Austrian Society of Cybernetic Studies.

In 1993, Dr. Zadeh received the Rufus Oldenburger Medal from the American Society of Mechanical Engineers "For seminal contributions in system theory, decision analysis, and theory of fuzzy sets and its applications to AI, linguistics, logic, expert systems and neural networks." He was also awarded the Grigore Moisil Prize for Fundamental Researches, and the Premier Best Paper Award by the Second International Conference on Fuzzy Theory and Technology. In 1995, Dr. Zadeh was awarded the IEEE Medal of Honor "For pioneering development of fuzzy logic and its many diverse applications." In 1996, Dr. Zadeh was awarded the Okawa Prize "For outstanding contribution to information science through the development of fuzzy logic and its applications."

In 1997, Dr. Zadeh was awarded the B. Bolzano Medal by the Academy of Sciences of the Czech Republic "For outstanding achievements in fuzzy mathematics." He also received the J.P. Wohl Career Achievement Award of the IEEE Systems, Science and Cybernetics Society. He served as a Lee Kuan Yew Distinguished Visitor, lecturing at the National University of Singapore and the Nanyang Technological University in Singapore, and as the Gulbenkian Foundation Visiting Professor at the New University of Lisbon in Portugal. In 1998, Dr. Zadeh was awarded the Edward Feigenbaum Medal by the International Society for Intelligent Systems and the Richard E. Bellman Control Heritage Award by the American Council on Automatic Control. In addition, he received the Information Science Award from the Association for Intelligent Machinery and the SOFT Scientific Contribution Memorial Award from the Society for Fuzzy Theory in Japan. In 1999, he was elected to membership in Berkeley Fellows and received the Certificate of Merit from IFSA (International Fuzzy Systems Association). In 2000, he received the IEEE Millennium Medal; the IEEE Pioneer Award in Fuzzy Systems; the ASPIH 2000 Lifetime Distinguished Achievement Award; and the ACIDCA 2000 Award for the paper, "From Computing with Numbers to Computing with Words -- From Manipulation of Measurements to Manipulation of Perceptions." In addition, he received the Chaos Award from the Center of Hyperincursion and Anticipation in Ordered Systems for his outstanding scientific work on foundations of fuzzy logic, soft computing, computing with words and the computational theory of perceptions. In 2001, Dr. Zadeh received the ACM 2000 Allen Newell Award for seminal contributions to AI through his development of fuzzy logic. In addition, he received a Special Award from the Committee for Automation and Robotics of the Polish Academy of Sciences for his significant contributions to systems and information science, development of fuzzy sets theory, fuzzy logic control, possibility theory, soft computing, computing with words and computational theory of perceptions.

Dr. Zadeh holds honorary doctorates from Paul-Sabatier University, Toulouse, France; State University of New York, Binghamton, NY; University of Dortmund, Dortmund, Germany; University of Oviedo, Oviedo, Spain; University of Granada, Granada, Spain; Lakehead University, Canada; University of Louisville, KY; Baku State University, Azerbaijan; the Silesian Technical University, Gliwice, Poland; the University of Toronto, Toronto, Canada; the University of Ostrava, the Czech Republic; the University of Central Florida, Orlando, FL; the University of Hamburg, Hamburg, Germany; and the University of Paris(6), Paris, France. Dr. Zadeh has single-authored over two hundred papers and serves on the editorial boards of over fifty journals. He is a member of the Advisory Board, Fuzzy Initiative, North Rhine-Westfalia, Germany; Advisory Board, Fuzzy Logic Research Center, Texas A&M University, College Station, Texas; Advisory Committee, Center for Education and Research in Fuzzy Systems and Artificial Intelligence, Iasi, Romania; Senior Advisory Board, International Institute for General Systems Studies; the Board of Governors, International Neural Networks Society; and is the Honorary President of the Biomedical Fuzzy Systems Association of Japan and the Spanish Association for Fuzzy Logic and Technologies. In addition, he is a member of the International Steering Committee, Hebrew University School of Engineering; a member of the Advisory Board of the National Institute of Informatics, Tokyo; a member of the Governing Board, Knowledge Systems Institute, Skokie, IL; and an honorary member of the Academic Council of NAISO-IAAC.

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Tzung-Pei Hong
National University of Kaohsiung, Taiwan

Abstract

Data mining is commonly used in attempts to induce association rules from transaction data. Most previous studies focused on mining from binary valued data. Transactions in real-world applications, however, usually consist of quantitative values. Designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field. In this speech, I will first introduce some techniques for mining fuzzy association rules from quantitative transactions when the membership functions are known. I will then propose several GA-based fuzzy data-mining methods for automatically extracting membership functions for the rules. All the genetic-fuzzy mining methods first use evolutional computation to find membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. Experimental results show that the designed fitness functions can avoid the formation of bad kinds of membership functions and can provide important mining results to users.

Bio

Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992.

From 1987 to 1994, he was with the Laboratory of Knowledge Engineering, National Chiao-Tung University, where he was involved in applying techniques of parallel processing to artificial intelligence. He was an associate professor at the Department of Computer Science in Chung-Hua Polytechnic Institute from 1992 to 1994, and at the Department of Information Management in I-Shou University (originally Kaohsiung Polytechnic Institute) from 1994 to 1999. He was a professor in I-Shou University from 1999 to 2001. He was in charge of the whole computerization and library planning for National University of Kaohsiung in Preparation from 1997 to 2000 and served as the first director of the library and computer center in National University of Kaohsiung from 2000 to 2001 and as the Dean of Academic Affairs from 2003 to 2006. He is currently a professor at the Department of Electrical Engineering and at the Department of Computer Science and Information Engineering. He is also the Vice President of National University of Kaohsiung from 2007.

He has published more than 250 research papers in international/national journals and conferences and has planned more than fifty information systems. He is also the board member of more than ten journals and the program committee member of more than sixty conferences. His current research interests include parallel processing, machine learning, data mining, soft computing, management information systems, and www applications.

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Janusz Kacprzyk
Polish Academy of Sciences

Abstract

First, we briefly advocate a need of natural language based methods in data mining, notably whew domain experts have a limited knowledge of modern tools of information technology. We present some approaches to linguistic summarization of sets of (numeric and/or textual) data, and show that a fuzzy logic based approach by Yager (1982), notably in its extended and implementable version of Kacprzyk and Yager (2001), and Kacprzyk, Yager and Zadrozny (2000), offers a simplicity and intuitive appeal, in particular in its new setting by Kacprzyk and Zadrozny (2005) based on Zadeh’s computing with words and protoforms.

We consider the derivation of linguistic summaries - exemplified, for a personnel database, by "most young workers earn much less that the average salary" - in terms of a sequence of fuzzy queries with linguistic quantifiers due to Kacprzyk and Zadrozny (1987 - 2005).

The above setting for linguistic data (base) summaries, devised originally for static data, is then extended to dynamic data represented by time series, following the approach of Kacprzyk, Wilbik and Zadrozny (2006).

We explicitly recast the models developed in terms of Zadeh's computing with words, and its related granularity whose essence is a proper use of linguistic terms and their combinations.

We consider, first, for the static data, linguistic summaries of a sales database of a computer retailer. Then, for time series data, we consider an example of an absolute performance analysis of daily quotations of a mutual fund over an 8 year horizon. We show an analysis of crucial characteristics in terms of the granulation assumed. Then, we present some interesting and relevant linguistic summaries obtained, potentially useful to users, under different protoforms and granulations. We mention their relevance for making decisions.

Finally, we summarize the results obtained from the point of view of various possible protoforms of linguistic summaries and granulations, and indicate some possible extensions exemplified by the use of some elements of NLG (natural language generation tools).

Bio

Janusz Kacprzyk is Professor of Computer Science at the Systems Research Institute, Polish Academy of Sciences, and Honorary External Professor at the Department of Mathematics, Yli Normal University, Shanxi, China. He has been a visiting professor at many universities in the USA, England, Italy and Mexico. He is Academician (Member of the Polish Academy of Sciences).

His research interests include soft computing, fuzzy logic, decision making, decision analysis and decision support, database querying, information retrieval, data analysis, data mining, etc.

He is President of IFSA (International Fuzzy Systems Association), and President of the Polish Society for Operational and Systems Research.

He is Fellow of IEEE and IFSA. He received The 2005 IEEE CIS Fuzzy Pioneer Award for pioneering works on multistage fuzzy control, notably fuzzy dynamic programming, and The Sixth Kaufmann Prize and Gold Medal for pioneering works on the use of fuzzy logic in economics and management.

His publication record is: 5 books, 30 volumes, 300 papers. He is Editor in chief of 3 Springer's book series, a member of advisory board of one Springer's book series, is on editorial boards of ca. 30 journals, and a member of the IPC at more than 200 conferences.

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Tsau Young (T.Y.) Lin
San Jose State University, USA

Abstract

Granular computing (GrC) is a pragmatic approach to Data and Knowledge engineering. Roughly, it is a methodology that involves elements(data) and subsets (knowledge). We will illustrate the idea via a web application.

In traditional web processing, we often represent a document by a set of keywords. In GrC, let us call it granular representation, we include the knowledge. More precisly, a document is not only represented by its keywords, but also by its granules. Here by a granule we mean a keyword association, which is a set of frequent co-ocurring near by keywords. For a trivial example, the association, "Wall street", as a financial concept, is in the granular representation.

The granular representation has a interesting geometric interpretation. We can regard the set of keywords as a set of vertices, and the set of keyword associations as a set of simplexes. Interestingly in such a tranlation, the apriori principle is converted into the closed condition of simplical complexes. In other words, the collection of keywords and keyword associations is an abstract simplicial complex in algebraic topology. Note that a simplicial complex is a combinatorial structure of polyhedron. So in this fashion the granular representation represents a document by a polyhedron. Based on such a polyhedron, documents can be clustered into various categories.

Bio

Tsau Young (T.Y.) Lin received his Ph.D. from Yale University, and now is a Professor in the Department of Computer Science, San Jose State University. Currently, he is also the Chapter Chair of IEEE Computer Society in Silicon Valley. He has served as editor, associate editor, member of the advisory/editorial board of several international journals, and chair, co-chair and member of program committees of conferences. His interests include approximation retrievals, data mining, data warehouse, data security, and computing methodology (granular computing, rough set, and soft computing).

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Ling Liu
Georgia Institute of Technology

Abstract

The bulk of contents out on the Internet continue to grow at an astounding pace. As computing and communications become ubiquitous, we are entering the Mobile Internet Computing era, where people, devices, and vehicles are connected at all time and the Internet access capability is being embedded in billions of wireless devices such as PDAs, cellular phones, and computers embedded in vehicles (e.g., navigational systems on cars). By extending the Internet through mobile information access, the Mobile Internet is on a trajectory to offer all of the same features and value propositions as the traditional Internet, with the promise of greater information access opportunity, richer and device-spanning Internet services and experiences, thanks to continuous availability and location awareness.

While location-aware computing promises convenience, new business opportunities, and a wide array of new quality of life enhancing services, the ability to locate users and mobile objects accurately also opens door for new threats - intrusion of location privacy. Location privacy is defined as the ability to prevent other unauthorized parties from learning one's current or past location. In location-aware computing, there are conceivably two types of location privacy - personal subscriber level privacy and corporate enterprise-level privacy. Companies need enterprise-level privacy to preserve corporate secrets and maintain competitive edge. Location privacy aware computing studies the general computational intelligence and theory for effectively using granules such as clusters, subsets, groups and intervals to build an efficient and yet location privacy preserving computational model for location-aware computing applications.

In this keynote, I will discuss location privacy threats and the granular computing challenges for protecting location privacy in the mobile Internet era. I will first review the concept of location privacy and the risks of unauthorized location disclosure. Then I will describe some representative location privacy models and techniques effective in either the privacy policy based framework or the location anonymization based framework. The discussion will address a number of important issues in location privacy research, including the location utility and location privacy trade-offs, the need for a careful combination of policy-based location privacy mechanisms and location anonymization based privacy schemes, as well as the set of safeguards for secure transmission, use and storage of location information, reducing the risks of unauthorized disclosure of location information. I will end the talk by discussing some important computational intelligence challenges in location privacy aware computing.

Bio

Ling Liu - is an Associate Professor in the College of Computing at Georgia Institute of Technology. There she directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining performance, security, privacy, and data management issues in building large scale data intensive systems. Dr. Liu and the DiSL research group have been working on various aspects of distributed data intensive systems, ranging from decentralized overlay networks, mobile computing and location based services, sensor network and event stream processing, to service oriented computing and architectures. She has published over 200 international journal and conference articles in the areas of Internet Computing systems, Internet data management, distributed systems, and information security. Her research group has produced a number of open source software systems, including WebCQ and XWRAPElite. Dr. Liu has received distinguished service awards from both the IEEE and the ACM and has played key leadership roles on program committee, steering committee, and organizing committees for several IEEE and ACM conferences, including IEEE International Conference on Data Engineering (ICDE), IEEE International Conference on Distributed Computing (ICDCS), International Conference on Web Services (ICWS), International Conference on Collaborative Computing (CollaborateCom), ACM International Conference on Information and Knowledge Management (CIKM). Dr. Liu is currently on the editorial board of several international journals, including IEEE Transactions on Knowledge and Data Engineering, International Journal of Very Large Database systems (VLDBJ), International Journal of Web Services Research, International Journal of Wireless Networks (WINET), and Springer International Journal on Peer to Peer Network and applications. Dr. Liu is a recipient of the best paper award of WWW 2004 and the best paper award of IEEE ICDCS 2003, and the 2005 Pat Goldberg Memorial Best Paper Award, and a recipient of IBM faculty award in 2003, 2006. Her research is primarily sponsored by NSF, DARPA, DoE, and IBM.

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Prof. Richard M. Karp
(1985 Turing Award Recipient)
University of California at Berkeley, USA

Abstract

This talk will trace the growing influence of fundamental ideas from computer science on the nature of research in a number of scientific fields. There is a growing awarenessthat information processing lies at the heart of the processes studied in fields as diverse as quantum mechanics, statistical physics, nanotechnology, neuroscience, linguistics, economics and sociology. Increasingly, mathematical models in these fields are expressed in algorithmic languages and describe algorithmic processes. The speaker will briefly describe connections between quantum computing and the foundations of quantum mechanics, and between statistical mechanics and phase transitions in computation. He will indicate how the growth of the Web has created new phenomena to be investigated by sociologists and economists. He will then focus in greater detail on computational molecular biology, where the view of living cells as complex information processing systems has become the dominant paradigm, and will discuss specific algorithmic problems arising in the sequencing of genomes, the comparative analysis of the resulting genomic sequences,the modeling of networks of interacting proteins, and the associations between genetic variation and disease.

Bio

Richard M. Karp was born in Boston, Massachusetts on January 3, 1935. He attended Boston Latin School and Harvard University, receiving the Ph.D. in 1959. From 1959 to 1968 he was a member of the Mathematical Sciences Department at IBM Research. From 1968 to 1994 and from 1999 to the present he has been a Professor at the University of California, Berkeley, where he held the Class of 1939 Chair and is currently a University Professor. From 1988 to 1995 and 1999 to the present he has been a Research Scientist at the International Computer Science Institute in Berkeley. From 1995 to 1999 he was a Professor at the University of Washington. During the 1985-86 academic year he was the co-organizer of a Computational Complexity Year at the Mathematical sciences research Institute in Berkeley. During the 1999-2000 academic year he was the Hewlett-Packard Visiting Professor at the Mathematical Sciences Research Institute. The unifying theme in Karp's work has been the study of combinatorial algorithms. His 1972 paper, "Reducibility Among Combinatorial Problems," showed that many of the most commonly studied combinatorial problems are NP-complete, and hence likely to be intractable. Much of his work has concerned parallel algorithms, the probabilistic analysis of combinatorial optimization algorithms and the construction of randomized algorithms for combinatorial problems.

His current activities center around algorithmic methods in genomics and computer networking. He has supervised thirty-six Ph.D. dissertations. His honors and awards include: U.S. National Medal of Science, Turing Award, Fulkerson Prize, Harvey Prize (Technion), Centennial Medal (Harvard), Lanchester Prize, Von Neumann Theory Prize, Von Neumann Lectureship, Distinguished Teaching Award (Berkeley), Faculty Research Lecturer (Berkeley), Miller Research Professor (Berkeley), Babbage Prize and eight honorary degrees. He is a member of the U.S. National Academies of Sciences and Engineering, the American Philosophical Society and the French Academy of Sciences, and a Fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the Association for Computing Machinery and the Institute for Operations Research and Management Science.

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Anant Jhingran
VP and CTO, IBM Silicon Valley Laboratory, USA

Abstract

There is a fundamental transformation that is taking place on the web around information composition through mashups. We first describe this transformation and then assert that this will also affect enterprise architectures. Currently the state-of-the-art in enterprises around information composition is federation and other integration technologies. These scale well, and are well worth the upfront investment for enterprise class, long-lived applications. However, there are many information composition tasks that are not currently well served by these architectures. The needs of Situational Applications (i.e. applications that come together for solving some immediate business problems) are one such set of tasks. Augmenting structured data with unstructured information is another such task. Our hypothesis is that a new class of integration technologies will emerge to serve these tasks, and we call it an enterprise information mashup fabric. In the talk, we discuss the information management primitives that are needed for this fabric, the various options that exist for implementation, and pose several, currently unanswered, research questions.

Bio Sketch

Dr. Anant Jhingran is a Distinguished Engineer, Vice President and the Chief Technology Officer for IBM's Information Management Division. Anant is responsible for the technical strategy of IBM's Information Management Software business, which has products and solutions in databases, content management, business intelligence, search and discovery, information integration and master data management. Previous to this job, Dr. Jhingran led the IBM team designing and building world-class solutions to meet the requirements of business analytics on structured and unstructured data. He has also been the director of Computer Science at the IBM Almaden Research Center where he managed the research scientists dedicated to advancing technology across foundations, software and services. Prior to joining Almaden Research, Dr. Jhingran was senior manager for e-Commerce and data management at IBM's T. J. Watson Research Center. He received his Ph.D. from UC at Berkeley in 1990. Anant has received several patents, outstanding innovation awards, and a corporate award for his contributions to DB2. He has authored over 20 academic papers, and is also a member of IBM Academy of Technology.

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Eric Brill
Principal Researcher and head of the Text Mining, Search and Navigation Group at Microsoft Research

Abstract

While search engines are great tools, they are still ineffective at helping users with a vast array of information needs. In this talk, we will discuss some of the problems with search today, and research opportunities that will help us realize vastly more effective search engines in the future.

Bio Sketch

Eric Brill is a research director and head of the Text Mining, Search and Navigation Group at Microsoft Research, which primarily conducts research in the areas of Web search and advertising. Prior to joining Microsoft, he was a faculty member in the Department of Computer Science and Center for Language and Speech Processing at Johns Hopkins University. Academically, Eric has been active in the fields of information retrieval, natural language processing, machine learning, speech recognition and artificial intelligence.

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Alon Halevy
Research Scientist, Google

Abstract

Data integration is a pervasive challenge faced in applications that need to query across multiple autonomous and heterogeneous data sources. Data integration is crucial in large enterprises, large-scale scientific projects, and government agencies. Data integration also holds the promise of fueling the next revolution of data content on the Web. This talk will review some the impressive progress on data integration made in research and in industry, but will argue that despite the progress, data integration is either still too hard for most users or does not address the real needs in applications. I will describe a new abstraction, dataspaces, that attempts to address these two challenges. I will give examples of data management at Web-scale at Google that motivate the need for dataspaces.

Bio Sketch

Alon Halevy is a member of technical staff at Google Inc. Before joining Google, Alon was a professor of Computer Science at the University of Washington, Seattle. Alon is the founder of two data integration companies, Nimble Technology and Transformic Inc. He is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and a 10-year Best Paper Award at the International Conference on Very Large Databases (VLDB 2006) for his work on data integration. In 2006, Alon was elected Fellow of the ACM.

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Neel Sundaresan
Director, eBay Research Labs

Abstract

This talk will discuss the challenges and approaches to findability in an online marketplace. The focus is beyond search and relevance. In a marketplace that affords social commerce, findability is critical and serendipity affords stickiness. We will discuss these topics and more.

Bio Sketch

Neel Sundaresan is a Senior Director and Head of eBay Research Labs. His current research interests include Social Networks, Community Computing, Search and Classification, and Trust and Reputation. Prior to joining eBay , he was a founder and CTO of a startup focused on multi-attribute fuzzy search and network CRM. Prior to this, he was headed the eMerging Internet Technologies group at the IBM Research Center. There he built the first XML-based Search Engine. He was one of the early leaders in building XML technologies including schema-aware compression algorithms, application component generators and pattern-match systems and compilers. He built the first RDF reference implementation as a W3C standard recommendation. He led research work in other areas like domain specific search engines, multi-modal interfaces and assistive technologies, semantic transcoding, and web mining. Prior to this he worked on C++ compiler and runtime systems for massively parallel machines and for shared memory systems and also on retargetable compilers, program translators and generators. He has over 40 research publications and several patents to his credit. He has been a frequent speaker at several national and international technology conferences. He has advised several masters and PhD students. He has a masters degree in Mathematics and in Computer Science from IIT Mumbai and a PhD in Computer Science from Indiana University, Bloomington.

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Andrew Tomkins
Yahoo VP for Search Research

Abstract

Non-professional creation of public online content has outstripped professional content creation of all forms, both online and offline. However, two orders of magnitude more content are created daily to flow through social networks, with as much as two more orders of magnitude still to come as user engagement increases. At the same time, content is diversifying in creation, consumption, and nature. In this talk, I'll cover these trends and their implications. I'll present some research results regarding online communities and semantic structures, and will close with some challenges for the future.

Bio Sketch

Andrew Tomkins is Chief Scientist of Search at Yahoo!, where his research interests include web search, web analysis, and online communities. Prior to joining Yahoo!, Andrew spent 8 years at IBM's Almaden Research Center, where he managed the information management principles group and served as Chief Scientist of the WebFountain project. He has published over eighty technical papers, and serves on the editorial boards of IEEE Internet Computing and ACM Transactions on the Web. Andrew received his PhD in Computer Science from Carnegie Mellon University in 1997.

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Dr.Yong Shi
Research Center on Fictious Economy & Data Science
Chinese Academy of Sciences

Abstract

For last ten years, the researchers have extensively applied quadratic programming into classification, known as V. Vapnik's Support Vector Machine, as well as various applications. However, using optimization techniques to deal with data separation and data analysis goes back to more than thirty years ago. According to O. L. Mangasarian, his group has formulated linear programming as a large margin classifier in 1960's. In 1970's, A. Charnes and W.W. Cooper initiated Data Envelopment Analysis where a fractional programming is used to evaluate decision making units, which is economic representative data in a given training dataset. From 1980's to 1990's, F. Glover proposed a number of linear programming models to solve discriminant problems with a small sample size of data. Then, the speaker and his colleagues have extended such a research idea into classification via a number of methods in multiple criteria decision making: multiple criteria linear programming (MCLP) and multiple criteria quadratic programming (MQLP). This approach differs from statistics, decision tree induction, and neural networks. This talk intends to promote the research interests in the connection of optimization and data mining as well as real-life applications among the growing data mining communities.

Bio Sketch

Professor Yong Shi serves as the Executive Deputy Director, Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science and Associate Dean, School of Management, Graduate University of the Chinese Academy of Sciences. He has been the Charles W. and Margaret H. Durham Distinguished Professor of Information Technology, College of Information Science and Technology, Peter Kiewit Institute, University of Nebraska, USA since1999. Dr. Shi's research interests include business intelligence, data mining, multiple criteria decision making, information overload, and telecommunication management. He has published ten books, more than 100 papers in various journals and numerous conferences/proceedings papers. He is the Editor-in-Chief of International Journal of Information Technology and Decision Making (SCIE), an Area Editor of International Journal of Operations and Quantitative Management, a member of Editorial Board for a number of academic journals, including International Journal of Data Mining and Business Intelligence. Dr. Shi has received many distinguished awards including Outstanding Young Scientist Award, National Natural Science Foundation of China, 2001; Member of Overseas Assessor for the Chinese Academy of Sciences, May 2000; and Speaker of Distinguished Visitors Program (DVP) for 1997-2000, IEEE Computer Society. He has consulted or worked on business projects for a number of international companies in data mining and knowledge management.

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