Paul Werbos

Quantum Physicist





Program Director (retired) of National Science Foundation

(Control, Networks and Computational Intelligence)

Fellow, IEEE and INNS; Recipient of the IEEE Neural Networks Pioneer Award and

Hebb Award (2011) from the International Neural Network Society (INNS).

Served on the boards of the, National Space Society, the Millennium Project, the Lifeboat Foundation, IEEE Energy Policy

Committee and as a Brookings Fellow for Senator Specter (2009)

Paul Werbos trained as a mathematician and received two Master's degrees in economics from Harvard University (1969)

and the London School of Economics (1968). He received is PhD in 1974 from Harvard University.

He is best known as the inventor of backpropagation and was also a pioneer of recurrent neural networks.

His Harvard PhD thesis first described the process of training artificial neural networks through 'backpropagation' of errors.

which was reprinted in full in his book the Roots of Backpropagation, Wiley 1994, along with his classic 1990 tutorial on backpropagation through time for Proc IEEE.

In his thesis 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,

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.


From 1988 to 2015, he led the neural network research funded by the Engineering Directorate of NSF, and other research areas

including Climate Change Technology, Space Solar Power, Quantum Technology


dblp computer science bibliography