Dr. Paul Werbos, Ph.D.

Program Director of National Science Foundation (NSF)
For Power, Controls and Adaptive Networks

He holds four degrees from Harvard and London in: (1) Applied mathematics for an interdisciplinary PhD, (2) Applied mathematics, with a major in physics and a minor in decision and control, (3) Economics; (4) International political systems. He took undergraduate and graduate mathematics at Princeton and the University of Pennsylvania.

He serves on the Governing Board of the International Neural Network Society (INNS). He was one of the three original two-year Presidents of INNS. He is a Fellow of the IEEE, and has won its Neural Network Pioneer Award, for the discovery of the "backpropagation algorithm".

Dr. Werbos has core responsibility at the NSF for the Adaptive and Intelligent Systems (AIS) area within the Controls, Networks and Computational Intelligence (CNCI) Program of ECS. He is also leading the development of a new Cybersystems thrust within the Integrative Hybrid and Complex Systems (IHCS) program of ECS. He is the ECS representative for the CLEANER initiative, for biocomplexity (MUSES), and for Collaborative Research in Computational NeuroScience.

Dr. Werbos is an elected member of the Administrative Committee (AdCom) of the IEEE Computational Intelligence Society, which he represents on the IEEE-USA Energy Policy Committee. He also serves on the Planning Committee of the ACUNU Millennium Project

In addition to his core interests at NSF, Dr. Werbos has interest in larger questions relating to life, and mind, consciousness, intelligent systems, the foundations of physics and human potential. See also selected papers on complex intelligent neural network systems. His 1974 Harvard Ph.D. thesis has been reprinted in its entirety, along with related papers, in his book The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting, Wiley, 1994. Some of work on high performance computing is described in P. Werbos, Backwards differentiation in AD and Neural Nets: Past Links and New Opportunities. In Martin Bucker, George Corliss, Paul Hovland, Uwe Naumann & Boyana Norris (eds), Automatic Differentiation: Applications, Theory and Implementations, Springer (LNCS), New York, 2005.