Keynote Speakers


Keynote Lecture

IEEE Information Reuse and Integration Conference, Las Vegas Hilton, July 13, 2008.

Computation with Imprecise Probabilities

Lotfi A. Zadeh  


Computation with imprecise probabilities is not an academic exercise—it is a bridge to reality.  In the real world, imprecision of probabilities is the norm rather than exception.  In large measure, real-world probabilities are perceptions of likelihood.  Perceptions are intrinsically imprecise. Imprecision of perceptions entails imprecision of probabilities.

In applications of probability theory it is a common practice to ignore imprecision of probabilities and treat imprecise probabilities as if they were precise.  A problem with this
 practice is that it leads to results whose validity is open to question.

Publication of Peter Walley's seminal work “Statistical Reasoning with Imprecise Probabilities,” in l99l, sparked a rapid growth of interest in imprecise probabilities.  Today, there is a substantive literature.  The approach described in this lecture is a radical departure from the mainstream.  First, imprecise probabilities are dealt with not in isolation, as in the mainstream literature, but in an environment of imprecise events, imprecise relations and imprecise constraints.  Second, imprecise probability distributions are assumed to be described in a natural language.  The approach is based on the formalism of  Computing with Words (CW) (Zadeh 1999, 2006).

In the CW-based approach, the first step involves precisiation of information described in natural language.  Precisiation is achieved through representation of the meaning of a proposition, p, as a generalized constraint.  A generalized constraint if an expression of the form X isr  R , where X is the constrained variable, R is a constraining relation and r is an indexical variable which defines the modality of the constraint, that is, its semantics.  The primary constraints are possibilistic, probabilistic and veristic.  Computation follows precisiation.  In the CW-based approach the objects of computation are generalized constraints.

The CW-based approach to computation with imprecise probabilities enhances the ability of probability theory to deal with problems in fields such as economics, operations research, decision sciences, theory of evidence, analysis of causality and diagnostics.  


Professor Lotfi A. Zadeh

Lotfi A. Zadeh , Professor in the Graduate School, Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California, USA. 

 LOTFI A. ZADEH is a Professor in the Graduate School, Computer Science Division, Department of EECS, University of California, Berkeley . In addition, he is serving as the Director of BISC (Berkeley Initiative in Soft Computing).

 Lotfi Zadeh is an alumnus of the University of Tehran, MIT and Columbia University . He held visiting appointments at the Institute for Advanced Study, Princeton, NJ; MIT, Cambridge, MA; IBM Research Laboratory, San Jose, CA; AI Center, SRI International, Menlo Park, CA; and the Center for the Study of Language and Information, Stanford University. His earlier work was concerned in the main with systems analysis, decision analysis and information systems. His current research is focused on fuzzy logic, computing with words and soft computing, which is a coalition of fuzzy logic, neurocomputing, evolutionary computing, probabilistic computing and parts of machine learning.

 Lotfi Zadeh is a Fellow of the IEEE, AAAS, ACM, AAAI, and IFSA. He is a member of the National Academy of Engineering and a Foreign Member of the Russian Academy of Natural Sciences, the Finnish Academy of Sciences, the Polish Academy of Sciences, Korean Academy of Science & Technology and the Bulgarian Academy of Sciences. He is a recipient of the IEEE Education Medal, the IEEE Richard W. Hamming Medal, the IEEE Medal of Honor, the ASME Rufus Oldenburger Medal, the B. Bolzano Medal of the Czech Academy of Sciences, the Kampe de Feriet Medal, the AACC Richard E. Bellman Control Heritage Award, the Grigore Moisil Prize, the Honda Prize, the Okawa Prize, the AIM Information Science Award, the IEEE-SMC J. P. Wohl Career Achievement Award, the SOFT Scientific Contribution Memorial Award of the Japan Society for Fuzzy Theory, the IEEE Millennium Medal, the ACM 2001 Allen Newell Award, the Norbert Wiener Award of the IEEE Systems, Man and Cybernetics Society, Civitate Honoris Causa by Budapest Tech (BT) Polytechnical Institution, Budapest, Hungary, the V. Kaufmann Prize, International Association for Fuzzy-Set Management and Economy (SIGEF), the Nicolaus Copernicus Medal of the Polish Academy of Sciences, the J. Keith Brimacombe IPMM Award, the Silicon Valley Engineering Hall of Fame, the Heinz Nixdorf MuseumsForum Wall of Fame, other awards and twenty-six honorary doctorates. He has published extensively on a wide variety of subjects relating to the conception, design and analysis of information/intelligent systems, and is serving on the editorial boards of over sixty journals.

Professor in the Graduate School , Computer Science Division

Department of Electrical Engineering and Computer Sciences

University of California

Berkeley, CA 94720 -1776

Director, Berkeley Initiative in Soft Computing (BISC)


Keynote Lecture

IEEE Information Reuse and Integration Conference, Las Vegas Hilton, July 13, 2008

Inventing the Future of Neurology: Integrated Wavelet-Chaos-Neural Network Models for Knowledge Discovery and Automated EEG-Based Diagnosis of Neurological Disorders  

Hojjat Adeli

Abba G. Liechtenstein Professor

The Ohio State University

The author has been advancing a multi-paradigm integrated approach for solution of complicated and intractable dynamic pattern recognition problems. The focus of this keynote lecture is data mining and knowledge discovery from time-series signals obtained from complex phenomena. Novel wavelet-chaos-neural network models are presented for signal processing of brain waves as recorded by electroencephalographs (EEGs) for automated EEG-based diagnosis of neurological disorders such as epilepsy and the Alzheimer’s disease (AD). Through extensive parametric studies and information reuse and integration certain combinations of parameters from the EEG sub-bands were discovered to be effective markers for seizure detection and epilepsy diagnosis. The model can distinguish among healthy, interictal, and ictal EEGs with a high accuracy of more than 96% substantially better than practicing neurologists and epileptologists. The extension the methodology for early onset diagnosis of the AD will be delineated.

Hojjat Adeli is Professor in the Departments of Civil and Environmental Engineering and Geodetic Science, Biomedical Engineering, Biomedical Informatics, Electrical and Computer Engineering, Neuroscience, Neurological Surgery, and the Interdisciplinary Biophysics Graduate Program at The Ohio State University (OSU). He received his Ph.D. from Stanford University in 1976 at the age of 26 after receiving his B.S.-M.S. degrees from the University of Tehran in 1973 with the highest rank in the College of Engineering. He has authored over 425 research and scientific publications in various fields of computer science, engineering, applied mathematics, and medicine including 11 books such as Machine Learning - Neural Networks, Genetic Algorithms, and Fuzzy Systems, Wiley, 1995; Neurocomputing for Design Automation, CRC Press, 1998; Wavelets in Intelligent Transportation Systems, Wiley, 2005; His forthcoming book is titled Intelligent Infrastructure – Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures (Taylor & Francis). He is the Founder and Editor-in-Chief of Computer-Aided Civil and Infrastructure Engineering, now in 23rd year of publication and Integrated Computer-Aided Engineering, now in 16th year of publication. He is also the Editor-in-Chief of International Journal of Neural Systems. He is the quadruple winner of the OSU College of Engineering Lumley Award for outstanding research accomplishments.  In 1998 he received the Distinguished Scholar Award from OSU, the university’s highest research award, “in recognition of extraordinary accomplishment in research and scholarship”. In 2005, he was elected Honorary/Distinguished Member, ASCE: "for wide-ranging, exceptional, and pioneering contributions to computing in civil engineering and extraordinary leadership in advancing the use of computing and information technologies in many engineering disciplines throughout the world.” In 2007, he received the OSU College of Engineering Peter L. and Clara M. Scott Award for Excellence in Engineering Education “for sustained, exceptional, and multi-faceted contributions to numerous fields including computer-aided engineering, knowledge engineering, computational  intelligence, large-scale design  optimization, and smart structures with worldwide impact,“ as well as the Charles E. MacQuigg Outstanding Teaching Award.