Advisory Board

Professor Asim Roy

Asim Roy, Ph.D. is a Professor of Information Systems at Arizona State University. He earned his B.E. in Mechanical Engineering from Calcutta University, India, his M.S. in Operations Research from Case Western Reserve University, Cleveland, Ohio, and his Ph.D. in Operations Research from University of Texas at Austin. He also studied Industrial Engineering at Rutgers University, New Brunswick, New Jersey. He has been a Visiting Scholar at Stanford University, visiting Professor David Rumelhart in the Psychology Department, and a Visiting Scientist at the Robotics and Intelligent Systems Group at Oak Ridge National Laboratory, Oak Ridge, Tennessee.
Asim is a member of the Board of Governors of the International Neural Network Society (INNS) and founder and chair of two INNS Sections — one on Autonomous Machine Learning and the other on Big Data Analytics. He was the Guest Editor-in-Chief of a special issue of Neural Networks on autonomous learning and currently the one on big data analytics. He is the Guest Editor-in-Chief of a Special Research Topic — “Representation in the Brain” of Frontiers in Psychology which will be published as an open access eBook. He is the Senior Editor of Big Data Analytics, an open access publication of BioMed Central, and also serves on the editorial boards of Neural Networks, Cognitive Computation, and Neural Information Processing – Letters and Reviews. He has been the Letters Editor of IEEE Transactions on Neural Networks and has served on organizing committees of many scientific conferences. He was the General Co-Chair of the INNS Conference on Big Data in San Francisco in 2015 and the Technical Program Co-Chair of IJCNN 2015 in Ireland. He is the Program Co-Chair of INNS Big Data Conference 2016 in Thessaloniki, Greece. He was the Program Chair for the ORSA/TIMS (Operations Research Society of America / The Institute of Management Sciences) National meeting in Las Vegas and the General Chair of the ORSA/TIMS National meeting in Phoenix. Asim is listed in Who’s Who in America.
His research interests are in theories of the brain, brain-like learning, artificial neural networks, automated machine learning, data mining, pattern recognition, prediction and forecasting, intelligent systems and nonlinear multiple objective optimization. His research has been published in Management Science, Decision Sciences, Mathematical Programming, Financial Management, Neural Networks, Neural Computation, Naval Research Logistics, ORSA Journal on Computing, IEEE Transactions on Neural Networks, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man and Cybernetics, Frontiers in Cognitive Science, and other journals.
Designed and developed IFPS/OPTIMUM
Asim designed and developed the software system IFPS/OPTIMUM that pioneered the idea of incorporating optimization tools in financial and planning languages for managerial use. It has been used by hundreds of corporations worldwide for financial, corporate, and production planning. This system has saved many companies hundreds of millions of dollars. Following in its footsteps, such optimization systems are now widely available with spreadsheet systems such as Excel Solver within Excel.
Asim’s brain theories
Asim has published three theories of the brain. The first theory postulates that localist representation, as opposed to distributed representation, is used widely in the brain: A theory of the brain: localist representation is used widely in the brain. That implies that firings of neurons in the brain have “meaning and interpretation” on a stand-alone basis. The second theory postulates that grandmother cells are used widely in the brain: An extension of the localist representation theory: grandmother cells are also widely used in the brain. Grandmother cells are a special type of localist cells and represent complex concepts that are multimodal invariant. In 2008, Asim published a theory of the brain that postulates that there are parts of the brain that control other parts and thus control theoretic principles that can be used to design and construct systems similar to the brain: Connectionism, controllers and a brain theory. These three theories invalidate many ideas of the current dominant theory of the brain called “Connectionism”. reported on Asim’s brain theories did the following reports on Asim’s brain theories:

  1. On grandmother cells: If you can’t beat them, join them: Grandmother cells revisited
  2. On localist representation: Do brain cells need to be connected to have meaning?
  3. On the controller theory of the brain: Professor Finally Publishes Controversial Brain Theory
Here’s the response to the criticism of the localist representation theory by James McClelland of Stanford University and David Plaut of Carnegie Mellon University: Response to Plaut and McClelland in the story.
Asim’s work has been described as pioneering by distinguished scholars in the field. He has been invited to many national and international conferences for plenary talks and for tutorials, workshops, and short courses on his new learning theory and methods.
Asim authored A theory of the brain: localist representation is used widely in the brain, An extension of the localist representation theory: grandmother cells are also widely used in the brain, Connectionism, controllers and a brain theory, The hardest test for a theory of cognition: The Input Test, On Connectionism, Rule Extraction and Brain-like Learning, and Artificial Neural Networks – A Science in Trouble, and coauthored An Interactive Weight Space Reduction Procedure for Nonlinear Multiple Objective Mathematical Programming, A Multi-Tasking Learning Model for Online Pattern Recognition, An Interactive Search Method Based on User Preferences, A Neural Network Learning Theory and a Polynomial Time RBF Algorithm, An algorithm to generate radial basis function (RBF)-like nets for classification problems, A polynomial time algorithm for the construction and training of a class of multilayer perceptrons, A Polynomial Time Algorithm for Generating Neural Networks for Pattern Classification: Its Stability Properties and Some Test Results, Extending planning languages to include optimization capabilities, and End-user optimization with spreadsheet models.