David Wolpert is an IEEE fellow, is the author of three books and more than 200 papers, has three patents, is an associate editor at more than half a dozen journals, and has received numerous awards. He has more than 17,000 citations in a wide range of fields, including physics, machine learning, game theory, information theory, the therodynamics of computation, and distributed optimization. In particular, his machine learning technique of stacking was instrumental in both winning entries for the Netflix competiton, and his papers on the no free lunch theorems jointly have more than 7,000 citations (details).
He is a world expert on using nonequilibrium statistical physics to analyze the thermodynamics of computing systems; extending game theory to model humans operating in complex engineered systems; exploiting machine learning to improve optimization; and Monte Carlo methods.
He is currently a member of the resident faculty at the Santa Fe Institute. Previously he was the Ulam Scholar at the Center for Nonlinear Studies at Los Alamos National Laboratory, and before that he was at the NASA Ames Research Center and was a consulting professor at Stanford University, where he formed the Collective Intelligence Group. He has worked at IBM and at a data mining startup, and he is external faculty at numerous international institutions.
His degrees in physics are from Princeton University and the University of California.