Dr. Paul John Werbos
Dr. Paul John Werbos is a Program Director of
ECS at the
US National Science Foundation (NSF). He
has core responsibility for the Adaptive and
Intelligent
Systems (AIS) area within the Power, Controls and Adaptive Networks
(PCAN) Program of ECS, and for the new area of Quantum, Molecular and
High-Performance Modeling and Simulation for Devices and Systems. He is
the ECS representative for the
CLEANER initiative, for biocomplexity
(MUSES), and for Collaborative Research in
Computational NeuroScience.
He is one of the two ECS representatives for cyberinfrastructure. 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.
Paul 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
AdCom of the
IEEE Industrial Electronics Society, and 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” and other basic neural network
learning designs.
He also serves on the Planning Committee of the
ACUNU
Millennium Project, 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. In 2003, he
participated on the interagency working group for the Climate Change
Technology Program. At the 2005 Space Development Conference in
Arlington, he was invited to present a new strategy for sustainable
exploration and development of space, drawing in part on previous work
funded by NSF.
In addition to his core interests at NSF, Paul has interest in
larger questions relating to consciousness, the foundations of physics,
and human potential; see his personal web page, www.werbos.com 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. Some of work on high performance
computing is described in
Automatic Differentiation: Applications, Theory and
Implementations.
Paul authored
Using ADP to Understand and Replicate Brain Intelligence: the Next
Level Design,
What is Mind? What is Consciousness? How Can We Build and Understand
Intelligent Systems?,
What is Reality? How can we discover the ultimate
physical/mathematical
laws which govern its evolution in time — or govern its state across
space-time,
What is Life? How does it work?,
Sustainability on Earth:
What Must We Change In Order to Survive?,
Humans in Space:
Goals, Strategy and Technology Options, and
Human potential — growth/learning in brain, soul, integration
(body),
coauthored
Energy Dependence and Co2: Nearest-Term Opportunities for
a
Dramatic Reduction (online PPT) and
The Handbook of Applied Neurocontrols, and coedited
Neural Networks for Control.
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.
Enter Werbos World!