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Experts



This page is to honor Experts in the field. The names are listed alphabetically,
along with their areas of expertise and contact Information.

This list is by no means exhaustive, and will always be a work in progress.

We apologize in advance from all great minds that were left out and encourage you to contact us if you know of any.



Machine Learning
Dr. Sami Bengio
Dr. Rodney Brooks
Dr. Vitit Kantabutra
Dr. Vojislav Kecman
Prof. Marvin Minsky
Prof. Eytan Ruppin
Prof. Haruhisa Takahashi
Dr. Ben Goertzel
Neuroscience
Dr. Susan McConnell
Self-Organized Maps
Prof. Johann Gasteiger
Prof. Teuvo Kohonen
Unsupervised Learning
Prof. William W. Armstrong
Jonathan Shapiro
Dynamic Systems
Prof. Ralph Abraham
Fuzzy Logic
Prof. Lotfi A Zadeh
General Neural Networks
Prof. Bart Kosko
Prof. Walter Freeman
Prof. George G. Lendaris
Paolo Marrone
Prof. Hava Siegelmann
Prof. Yoshiyasu Takefuji
Prof. John G Taylor
Dr. Stephen Thaler
Prof. Michel Verleysen
Dr. David Waltz
Dr. Paul J. Werbos
Data Mining, Knowledge Discovery & Predictive Analytics
Prof. Nik Kasabov
Prof. Rudolf Kruse
Dr. Nikolay Nikolaev
Dr. Graham Williams
Voice Recognition & Signal Processing
Dr. Andrew Back
Prof. Bayya Yegnanarayana
Pattern Recognition
Christopher M. Bishop

 


Prof. Ralph Abraham
Ralph H. Abraham has been Professor of Mathematics at the University of California at Santa Cruz since 1968. He received the Ph.D. in Mathematics at the University of Michigan in 1960, and taught at Berkeley, Columbia, and Princeton before moving to Santa Cruz. He has held visiting positions in Amsterdam, Paris, Warwick, Barcelona, Basel, and Florence, and is the author of more than 20 texts & books.
Contact: WebSite

Prof. William W. Armstrong
His research concerns adaptive logic networks (ALNs) and their applications. An ALN can fit a function to given data points by a process of training. For example, it can learn to predict a future value of some variable based on past values of that variable and other variables related to it.
Contact: WebSite

Dr. Andrew Back
His work has concentrated on developing new nonlinear models and algorithms in the field broadly known as datamining. The approaches he uses come from a range of methods, such as classical linear systems theory, statistical techniques, signal processing, control theory, hybrid systems and nonlinear models including feedforward and recurrent neural networks.
Contact: WebSite

Dr. Sami Bengio
Sami Bengio holds PhD in Computer Science from the University of Montreal, Canada.
He is currently working at DIAP (Dalle Molle Institute for Perceptual Artificial Intelligence) located in Martigny, Valais. His research is in various aspects related to statistical machine learning (neural networks, support vector machines, hidden Markov models, mixture models, adaboost and bagging, etc.).
Mr. Bengio takes part in Torch (a well known machine learning library) development.
Contact: WebSite   Email

Christopher M. Bishop
Christopher M. Bishop is the author of the well-known book "Neural Networks for Pattern Recognition" (1995, currently in its 9th edition). The main points in his biography are graduating Oxford in 1980, PhD in Theoretical Physics in 1983. Then he pointed his interests to pattern recognition and neural networks where he has a lot of publications and books. He is currently working at Microsoft Research Laboratory in Cambridge, UK.
Contact: WebSite

Dr. Rodney A. Brooks
Rodney A. Brooks is Director of the MIT Computer Science and Artificial Intelligence Laboratory, and is the Fujitsu Professor of Computer Science He is also Chairman and Chief Technical Officer of iRobot Corp
Contact: WebSite

Prof. Walter Freeman
Prof. Walter J. Freeman, currently at University of CA-Berkeley, studied physics and mathematics at M.I.T., philosophy at the University of Chicago, medicine at Yale University (M.D. cum laude 1954) and Johns Hopkins, and neurophysiology at UCLA with support from the Foundations’ Fund for Research in Psychiatry. He has taught brain science in the University of California at Berkeley since 1959, where he is a Professor of the Graduate School. He received Guggenheim Fellowship, the A.E. Bennett Award from the Society for Biological Psychiatry, the Pioneer Award from the Neural Networks Council of the IEEE, and a MERIT award from the National Institute of Mental Health. He is the author of more than 350 articles and books.
Contact:  WebSite

Prof. Johann Gasteiger
Professor Gasteiger, Computer-Chemie-Centrum , University of Erlangen-Nürnberg
Research area: Artificial Intelligence in Computational Chemistry
Contact:  WebSite

Dr. Vitit Kantabutra
As Associate Professor in NSF-Idaho Dr. Kantabutra is working on new algorithms to achieve higher and faster training convergence.
Contact:  WebSite

Prof. Nik Kasabov
Professor Nikola K. Kasabov holds a Personal Chair in the Department of Information Science, University of Otago, Dunedin, New Zealand. He received his MSc degree in Computer Science and his PhD degree in Mathematical Sciences from the Technical University in Sofia, Bulgaria. Kasabov has published over 250 works, among them over 50 journal papers, 90 conference papers, 15 book chapters, 5 text books, 3 edited research books, 5 edited conference proceedings, 21 patents and authorship certificates in the area of intelligent systems, connectionist and hybrid connectionist systems, fuzzy systems, expert systems, speech recognition, and data analysis.
Contact:  WebSite

Dr. Vojislav Kecman
Dr. Kecman is the Author of a few dozens of journal and conference papers and 13 monographs, books or other bound publications. His research area includes: Learning from data sets - support vector machines, neural networks and fuzzy logic systems. Kernel machines. Pattern recognition, multivariate function approximation. Knowledge modeling and knowledge acquiring.
Contact:  WebSite

Prof. Teuvo Kohonen
His research areas are the theory of self-organization, associative memories, neural networks, and pattern recognition, in which he has published over 300 research papers and four monography books.
Since the 1960s, Professor Kohonen has introduced several new concepts to neural computing: fundamental theories of distributed associative memory and optimal associative mappings, the learning subspace method, the self-organizing feature maps (SOMs), the learning vector quantization (LVQ) among those.
Contact: WebSite

Prof. Bart Kosko
Bart Kosko is professor of electrical engineering at the University of Southern California (USC). He is famous as the leading proponent and popularizer of fuzzy logic, and is author of several books. He is an expert on mind and machine intelligence including millitary applications and smart weapons. Bart Kosko received bachelor's degrees in economics and philosophy from the University of Southern California, the master's degree in applied mathematics from the University of California, San Diego, and the doctorate degree in electrical engineering from the University of California, Irvine.
Contact: website

Prof. Dr. Rudolf Kruse
Dr. Kruse is the Chairman of the working group "Foundations of Fuzzy Systems and Soft Computing" of the German
Contact: website

Prof. George G. Lendaris

Research from Prof. Lendaris in Portland State University includes development and application of massively parallel computation methodology known as neural networks or connectionist networks. Methodology development focuses on the idea of matching the structure/architecture of a network to structural relations in data of problem context. This requires developing a common mechanism for describing structure in data and structure of a network so a matching process can be possible. One approach is based on a knowledge representation formalism known as conceptual structures, and another is based on a structure representation formalism called general systems methodology (GSM) notation. Applications being pursued include pattern recognition and implementation of selected database/expert system operations. In the planning stage are control applications. Future work includes collaboration with other faculty in developing analog/digital VLSI implementations of neural networks.

Contact: website

Prof. Lotfi Zadeh

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).
His seminal paper on Fuzzy Sets (1965) provided a basis for a qualitative approach to the analysis of complex systems in which linguistic rather than numerical variables are employed to describe system behavior and performance. In this way, a much better understanding of how to deal with uncertainty may be achieved, and better models of human reasoning may be constructed.

Contact: website

Paolo Marrone

Paolo is an Italian IT specialist with over 20 years of experience in the field. Currently he is working at a large Italian Bank, in Rome, responsible for J2EE Architecture and System Integration. His interested in Neural Networks began in 1989, when he started to study the possibility to use the neural networks outside the academic world, making them suitable also for the industrial market. Feeling the necessity of a different approach, the focus of his activity in the last five years was to address issues in the development of a neural network framework based on the last available technologies, with particular emphasis to important features like portability, scalability, extensibility, modularity, etc. The result of that work is Joone, an Open Source neural network framework written in Java.

Contact: website

Susan McConnell

Dr. Susan K. McConnell is Professor of Humanities and Sciences in the Department of Biological Sciences at Stanford University. She received her A.B. degree Summa Cum Laude from Harvard and Radcliffe Colleges in 1980. In 1987 she earned the Ph.D. degree in Neurobiology from Harvard University, where she worked with Dr. Simon LeVay. Following postdoctoral training with Dr. Carla Shatz at the Stanford University School of Medicine, McConnell joined the Stanford faculty in 1989. McConnell has been awarded a number of research fellowships including the Searle Scholar award, Pew Scholar in the Biomedical Sciences, McKnight Scholar, Alfred P. Sloan Research Fellow, National Science Foundation Presidential Young Investigator, and McKnight Investigator awards. She has received recognition for her research and teaching skills, as evidenced by awards that include the Society for Neuroscience Young Investigator Award, the Marcus Singer Award in developmental biology, a National Science Foundation Presidential Faculty Fellowship, a Terman Fellowship, and the Walter J. Gores Award for Excellence in Teaching and the Hoagland Prize for Undergraduate Teaching at Stanford University. McConnell's primary research goal is to understand how neurons in the developing brain are produced, assigned specific phenotypes, and wired together into functional circuits. Her research has contributed to our understanding of how neurons are produced from multipotent progenitor cells and how these neurons migrate into appropriate positions within the developing brain.

Contact: website

Prof. Marvin Minsky
Marvin Minsky has made many contributions to AI, cognitive psychology, mathematics, computational linguistics, robotics, and optics. He received the BA and PhD in mathematics at Harvard and Princeton. In 1951 he built the SNARC, the first neural network simulator. His other inventions include mechanical hands and other robotic devices,the confocal scanning microscope, the "Muse" synthesizer for musical variations (with E. Fredkin), and the first LOGO "turtle" (with S. Papert).
Contact: WebSite

Dr. Nikolay I. Nikolaev
Nikolay Nikolaev lecturs in Computer Science Department of Computing Goldsmiths College, University of London. Research area: second-order backpropagation algorithms for re-training polynomial neural networks.
Contact: WebSite

Prof. Eytan Ruppin
Once in Star Trek, now Professor at School of Computer Sciences, Tel Aviv University
Research area: Artificial Life and Evolutionary Computation and Dynamics Of Neural Networks
Contact: WebSite

Prof. Hava Siegelmann
Prof. Siegelmann, Associate Professor at University of Massachusetts, neural computation, adaptive information systems, machine learning and knowledge discovery, theory of analog and adaptive systems, bioinformatics.
She has published in a variety of prestigious journals, including Science, Theoretical Computer Science, the Journal of Computer and Systems Science, IEEE Transactions on Information Theory, IEEE Transactions on Systems Man and Cybernetics, and IEEE Transactions on Neural Networks.
Contact: WebSite Info: Analog Computer

Jonathan Shapiro
He is a senior lecturer at the department of Computer Science of Manchester University since 1989, where he teaches and carry out research on neural networks and genetic algorithms.
Contact: WebSite

Prof. Haruhisa Takahashi
He was born in Shizuoka Prefecture Japan, on March 31, 1952. He graduated from the University of Electro-Communications, Tokyo. He received the Dr Eng. degree from Osaka University. He was a faculty member of the Department of Computer Science and Engineering, Toyohashi University of Technology from 1980 to 1986. Since 1986, he has been with the University of Electro-Communications, where he is currently Professor of Communications and Systems Engineering. He was previously engaged in the field of nonlinear network theory, queueing theory and performance evaluation of communication systems.
Contact: WebSite

Prof. Yoshiyasu Takefuji
Dr. Yoshiyasu Takefuji is a tenured professor of Keio University since April 1992. His research interests focus on neural computing and hyperspectral computing. He received the National Science Foundation/Research Initiation Award in 1989 and received the distinct service award from IEEE Trans. on Neural Networks in 1992 and has been an NSF advisory panelist.
Contact: WebSite

Prof. John G Taylor
John G. Taylor has been involved in Neural Networks since 1969, when he developed analysis of synaptic noise in neural transmission, which has more recently been turned into a neural chip (the pRAM) with on-chip learning.
Contact: WebSite

Dr. Stephen Thaler
Dr. Thaler holds several patents in area of Creativity Machines and self-learning Neural Networks. He has built self-learning systems for a number of large international corporations such as Anheuser- Bush, Boeing, Gillette and U.S. government agencies such as the U.S. Air Force and the State of California.
Contact: WebSite

Prof. Michel Verleysen
Prof. Verleysen is currently senior research associate of the F.N.R.S. (Belgian National Fund for Scientific Reserach) and lecturer at the Electrical Engineering Department (Machine learning group) of UCL. He has ben invited professor at the E.P.F.L. (Ecole Polytechnique Fédérale de Lausanne, Switzerland) in 1992, at the Université d'Evry Val d'Essonne (France) in 2001, and at the Université Paris I Panthéon Sorbonne in 2002 and 2003. He is chairman of the annual European Symposium on Artificial Neural Networks, and editor-in-chief of the Neural Processing Letters journal.
Contact: WebSite

Dr. David Waltz
Dr. David Waltz is Vice President, Computer Science Research at the NEC Research Institute in Princeton, NJ, and an Adjunct Professor at Brandeis University. From 1984-93, he was Director of Advanced Information Systems at Thinking Machines Corporation and Professor of Computer Science at Brandeis.
Contact: WebSite

Dr. Paul J. Werbos
Dr. Paul J. Werbos holds 4 degrees from Harvard University and the London School of Economics, covering economics, mathematical phyiscs, decision and control, and the backpropagation algorithm. He has served as President of the International Neural Network Society, where he is still on the Governing Board.
Contact: WebSite

Prof. Bayya Yegnanarayana
The focus of his research activity is to address issues in the development of speech systems for Indian languages with particular reference to speech-to-text and text- to-speech systems for Hindi. He develops algorithms for feature extraction and classification using signal processing methods and neural network models. New algorithms based on processing Fourier transform phase function (or group delay function) have been proposed.
Contact: WebSite

Dr. Ben Goertzel

Ben Goertzel (goertzel.org) is founder and CEO of two computer science firms Novamente LLC (novamente.net) and Biomind LLC (biomind.com), and of the non-profit Artificial General Intelligence Research Institute (agiri.org). He has served as a university faculty in several departments of mathematics, computer science and cognitive science, in the US, Australia and New Zealand. He is author of two books focused on the future of technology and society Creating Internet Intelligence (Plenum, 2001) and The Path to Posthumanity (Academica, 2006). He serves as Director of Research for the Singularity Institute for AI.

Contact: WebSite

Dr. Graham Williams

Dr Graham Williams is Director of Data Mining at the Australian Taxation Office, and previously Principal Computer Scientist for Data Mining with CSIRO. He is a Senior International Expert and Visiting Professor of the Chinese Academy of Sciences at the Shenzhen Institutes of Advanced Technologies, and Adjunct Professor in Data Mining, Fraud Prevention, Security, at the University of Canberra and Australian National University. Graham is an active machine learning researcher and regularly teaches data mining courses. He is author of the freely available Rattle software for data mining and of the Rattle book published by Springer in 2011: Data Mining with Rattle and R: The Art of Excavating Knowledge from Data. Graham has been involved in data mining projects for clients from government and industry for over 25 years. His research developments include ensemble learning (1988) and hot spots discovery (1997). He is involved in numerous international artificial intelligence and data mining research activities and conferences and has edited a number of books and has authored many academic and industry papers.

Contact: WebSite

 

 

Last Update: 10/21/2005