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Affiliation: 
National Science Foundation
Title: 
Program Director
Department: 
CNCI Program director
Expertise: 
engineering, energy economics, intelligent systems

Biography

Dr. Paul Werbos has core responsibility for the Adaptive and Intelligent Systems (AIS) Area within the Power, Control and Adaptive Networks (PCAN) Program of ECCS, and for the new area of Quantum, Molecular and High-Performance Modeling and Simulation for Devices and Systems (QMHP). He is the ECCS representative for Collaborative Research in Computational NeuroScience and for the Engineering working group in Adaptive Systems Technology. He initiated and has led the EFRI-2008 topic in Cognitive Optimization and Prediction. He has special interest in efforts to exploit higher levels of true computational intelligence in these areas, and in efforts which can seriously increase the probability that we achieve global sustainability. In 1994, he initiated an SBIR topic on fuel cell and electric cars which he coordinated for several years. He was part of the group which proposed and led NSF's earlier initiative in Learning and Intelligent Systems, and assisted the follow-on in Information Technology Research. He has at times handled the ECCS areas in electric power and wireless communications when there were gaps in those areas. Dr. Werbos is an elected member of the Governing Board of the International Neural Network Society (INNS), for which he was one of the original three two-year Presidents. He has also served as an elected member of the Administrative Committee (AdCom) of the IEEE Computational Intelligence Society (CIS), which he continues to represent on the IEEE-USA Energy Policy Committee. For IEEE-USA and as chair of the CIS Task Force on Alternative Energy, he has given a number of major talks to Congressional staff on energy policy, and helped to organize the IEEE-USA workshop on plug-in hybrid cars. He also serves on the AdCom of the IEEE Industrial Electronics Society. He is a Fellow of the IEEE, and has won its Neural Network Pioneer Award, for the discovery of the "backpropagation algorithm" and other basic neural network learning designs such as Adaptive Dynamic Programming. He also serves on the Planning Committee of the ACUNU Millennium Project (see www.stateofthefuture.org), whose annual report on the future tends to lead global lists of respected reports on the long-term future. In 2002, he and John Mankins of NASA initiated and ran the NASA-NSF-EPRI initiative on enabling technologies for space solar power (search on "JIETSSP" at www.nsf.gov). In 2003, he participated on the interagency working group for the Climate Change Technology Program. He has a paper in press at Futures on a rational strategy for the economic development of space, and has been nominated for the Governing Board of the National Space Society. In addition to his core interests at NSF, Dr. Werbos has interest in larger questions relating to consciousness, the foundations of physics, and human potential; search on "Werbos" at arxiv.org or go to his personal web page for details. 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. Prior to arriving full-time at NSF in 1989, Dr. Werbos worked since 1979 at the Energy Information Administration (EIA) of the Department of Energy. He initially worked in the evaluation of energy models, forecasts and analyses; this required spanning the gamut from decoding undocumented FORTRAN to evaluating the implications for the future of humanity. He later became lead analyst for long-term energy futures, and developed the econometric models used in EIA's Annual Energy Outlook for industrial and transportation energy demand and for oil and gas production. He served on Carters Global 2000 Phase II interagency task force. His model of industrial energy demand played a major role in the Stanford Energy Modeling Forum study of industrial demand, and resulted in several papers, including two in Energy: The International Journal, March/April 1990. Before that he spent a year as an IPA at the Census Use Research center as a mathematical statistician, and taught for 3 and a half years at the University of Maryland in the public policy area. Before teaching, he spent two years at the MIT Cambridge Project adding new capabilities for data mining and modeling to a user-oriented software package written in FORTRAN and PL/1 for the Multics operating system. He holds four degrees from Harvard and the London School of Economics in: (1) economics; (2) international political systems, emphasizing European economic institutions; (3) applied mathematics, with a major in quantum physics and a minor in decision and control; (4) applied mathematics for an interdisciplinary PhD. Prior to that, during high school, he obtained an FCC First Class Commercial Radiotelephone license, and took undergraduate and graduate mathematics courses at Princeton and the University of Pennsylvania.