Igor Klebanov

Igor Romanovich Klebanov (Russian: И́горь Ромáнович Клеба́нов; 29 March 1962) is a theoretical physicist whose research is centered on relations between string theory and quantum gauge field theory. Since 1989, he has been a faculty member at Princeton University where he is currently a Eugene Higgins Professor of Physics and the Director of the Princeton Center for Theoretical Science.[1] In 2016, he was elected to the National Academy of Sciences.[2]

Born in the Soviet Union, he emigrated to the U.S. as a teenager. He received his undergraduate education at MIT (class of 1982), and his Ph.D. degree at Princeton University as a student of Curtis Callan in 1986. In his thesis he made advances in the Skyrme model of hadrons. Klebanov worked as a post-doc at SLAC. His main contributions to string theory are in Matrix model approaches to two-dimensional strings, in brane dynamics, and in the gauge theory-gravity duality. His work in 1996-97 on relations between branes in supergravity and their gauge theory description anticipated the gauge theory-gravity correspondence.

Klebanov's 1998 paper Gauge Theory Correlators from Non-Critical String Theory with his graduate student Gubser, and Polyakov, which made a precise statement of the AdS/CFT duality, is among the all-time top cited papers in high-energy physics (it has over 10,000 citations according to Google Scholar). A series of papers by Klebanov and collaborators on D-branes on the conifold has led to discovery of cascading gauge theory. Its dual warped throat provides a geometric description of color confinement and chiral symmetry breaking; it has been used in model building for cosmology and particle physics. The relation between 3-dimensional critical O(N) model and bosonic higher-spin gauge theory in 4-dimensional AdS space has been called the Klebanov-Polyakov correspondence.

Klebanov's more recent notable work includes the Entanglement Entropy in confining gauge theories, the F-theorem for 3-dimensional Renormalization Group flows, and large N tensor models.