David Wolpert is a professor at the Santa Fe Institute, external professor at the Complexity Science Hub in Vienna, adjunct professor at ASU, and research associate at the ICTP in Trieste. He is the author of three books (and co-editor of several more), over 200 papers, has three patents, is an associate editor at over half a dozen journals, has received numerous awards, and is a fellow of the IEEE.
He has over 30,000 citations, with most of his papers in thermodynamics of computation, foundations of physics, dynamics of social organizations, machine learning, game theory, and distributed optimization / control. In particular, his machine learning technique of stacking was instrumental in both winning entries for the Netflix competition, and his papers on the no-free lunch theorems have over 10,000 citations. (Details at http://davidwolpert.weebly.com).
Most of his current research involves two topics:
- Combining recent revolutionary breakthroughs in nonequilibrium statistical physics with computer science theory to lay the foundations of a modern theory of the thermodynamics of computation.
- Using modern machine learning tools to investigate social systems, ranging from models of social organization (command and communication networks within social groups) to estimating Langevin dynamics of states evolving in time to investigating reinforcement learning AIs interacting via smart contracts.
Before his current position, he was the Ulam Scholar at the Center for Nonlinear Studies, and before that he was at NASA Ames Research Center and a consulting professor at Stanford University, where he formed the Collective Intelligence group. He has worked at IBM and a data mining startup and is external faculty at numerous international institutions.
His degrees in Physics are from Princeton and the University of California.