« Back to All Experts page

Paul Werbos

Program Director for Energy, Power and Adaptive Systems
National Science Foundation, retired


Areas of Expertise:

Additional Areas of Expertise:

Quantum Technology

Retired 2/15/2015, but maintains connections with IEEE-USA, the Millennium Project and the National Space Society. In his most recent work at NSF, Dr. Paul Werbos had responsibility for the following Core ECCS areas: Adaptive and Intelligent Systems, Quantum systems and device modeling, and systems-level power grids. Also led the EFRI 2008 topic in Cognitive Optimization and Prediction (engineering-neuroscience collaboration to reverse engineer intelligence). Dr. Werbos has led a variety of other areas, such as fuel cell and electric vehicles, emerging techologies, cyber systems and the sustainability part of IDR since he started at NSF in 1988. He is a Fellow of IEEE and INNS, a winner of the IEEE Neural Networks Pioneer Award and winner of the Hebb Award for 2011 from the International Neural Network Society (INNS). The Hebb Award is INNS's highest award, to honor substantive contributions to the understanding of biological learning systems. The biography going with this award reads:

Paul Werbos began training as a mathematician, taking many university courses culminating in the graduate course in logic from Alonzo Church at Princeton while in middle and high school. Realizing the limits of deductive logic, he began his quest to understand inductive logic and intelligence in the mind back in those days, inspired by the work of John Von Neumann, Donald Hebb and early AI (Feigenbaum and Feldman). He obtained two degrees in economics from Harvard and the London School of Economics, divided equally between using mathematical economics as a model for distributed intelligence and developing some broader understanding. For his Harvard M.S. , he took courses in quantum field theory (QFT) from Julian Schwinger, but did not fully understand the subject until many years later, after he started an activity in quantum technology and modeling at NSF (see his papers at http://arxiv.org/.) For his 1974 Harvard PhD thesis (reprinted in The Roots of Backpropagation, Wiley 1994), he proposed the development of more powerful, more biologically plausible reinforcement learning systems by the then new idea of using neural networks to approximate dynamic programming (ADP), including the value function. In order to implement ADP in a local biologically plausible manner, he translated Freud's theory of "psychic energy" into an algorithm later called backpropagation, and a rigorous general theorem, the chain law for ordered derivatives, which later also became known as the reverse method or adjoint method for automatic or circuit-level differentiation. He has spent many years advancing the fields of ADP and backpropagation and brain-like prediction, aimed at developing and demonstrating the kind of designs which could actually explain the kind of general intelligence we see in the brain and in subjective human experience - collaborating at times with Karl Pribram and Walter Freeman and Pellionisz among others, and proposing biological experiments to test the theory. In looking for applications which are really important to areas like energy, sustainability and space, he has also gotten deep into domain issues and organization, as reflected at http://www.werbos.com/, serving on boards of the National Space Society, the Millennium Project, the Lifeboat Foundation, and the IEEE Energy Policy Committee, and as a Fellow in the Senate in 2009. From 1980-1989, he developed official econometric forecasting models (two based on backpropagation) and was lead analyst for the long-term future at EIA in the Department of Energy.

Recent Comments by Paul Werbos

  • "What kind of investor tries hard to buy high and sell low? What does it say about the ups and downs of our policy competence when we fall into that ki"
    A Pandemic, a Price War, and the Future of America’s Oil Industry
  • "There are two areas of opportunity where the potential benefits are far greater than they are for all the others. There are risks, of course, but most"
    A Grand Challenge to Commercialize Energy Storage
  • "In the end, an optimal policy (to minimize the risk of human extinction at minimum real economic But cost) WOULD include a carbon fee, in my analysis"
    Time for a Price on Carbon
  • "I tend to feel despair when climate proposals are discussed at a kind of astral plane of tneoretical reality. When Ban Ki Moon introduced our session"
    Time for a Price on Carbon
  • "Most economists learn in their first course on microeconomics that a simple tax on a pollution like carbon emission gives the optimal response to redu"
    Time for a Price on Carbon
  • "It sounds good to talk about improving energy efficiency, reducing end use energy services per BTU across the board. But this is a TERRIBLE metric eit"
    But First, Energy Efficiency
  • "It is amazing to me how far the lobbyists of both political parties in the US have gone more and more out of touch with reality, since about when Rosc"
    The Need for a Diverse Approach to Energy
  • "For several years I have heard from a "sea turtle" working with a foreign owned nuclear company in the US, talking about Jiang Zemin's commitment to "
    The Clean Energy Puzzle Needs Nuclear
  • "It is good news, and a bit of a surprise to me, that offshore wind people are offering power at costs down around 7 cents per kwh, a very rapid drop s"
    U.S. Offshore Wind Will Be Bigger Than You Think
  • "During the 2009 debates on the Waxman bill, and its descendants, the DOE/EIA and EPA issued an analysis making some very important points. First, even"
    Carbon Tax: Barriers and Solutions