Leslie Gabriel Valiant FRS^{[4]}^{[5]} (born 28 March 1949) is a British American^{[6]} computer scientist and computational theorist.^{[7]}^{[8]} He is currently the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University.^{[9]}^{[10]}^{[11]}^{[12]} Valiant was awarded the Turing Award in 2010, having been described by the A.C.M. as a heroic figure in theoretical computer science and a role model for his courage and creativity in addressing some of the deepest unsolved problems in science; in particular for his "striking combination of depth and breadth".^{[6]}
Leslie Valiant  

Born  Leslie Gabriel Valiant 28 March 1949 
Nationality  British 
Alma mater 

Known for  
Awards 

Scientific career  
Fields  Mathematics Theoretical computer science Computational learning theory Theoretical neuroscience 
Institutions  
Thesis  Decision Procedures for Families of Deterministic Pushdown Automata (1974) 
Doctoral advisor  Mike Paterson^{[3]} 
Doctoral students  
Website  people 
Valiant was educated at King's College, Cambridge,^{[13]}^{[6]} Imperial College London,^{[13]}^{[6]} and the University of Warwick where he received a PhD in computer science in 1974.^{[14]}^{[3]}
Valiant is worldrenowned for his work in theoretical computer science. Among his many contributions to complexity theory, he introduced the notion of #Pcompleteness ("sharpP completeness") to explain why enumeration and reliability problems are intractable. He also introduced the "probably approximately correct" (PAC) model of machine learning that has helped the field of computational learning theory grow, and the concept of holographic algorithms. In computer systems, he is most wellknown for introducing the bulk synchronous parallel processing model. His earlier work in automata theory includes an algorithm for contextfree parsing, which is (as of 2010) still the asymptotically fastest known. He also works in computational neuroscience focusing on understanding memory and learning.
Valiant's 2013 book is Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World.^{[15]} In it he argues, among other things, that evolutionary biology does not explain the rate at which evolution occurs, writing, for example, "The evidence for Darwin's general schema for evolution being essentially correct is convincing to the great majority of biologists. This author has been to enough natural history museums to be convinced himself. All this, however, does not mean the current theory of evolution is adequately explanatory. At present the theory of evolution can offer no account of the rate at which evolution progresses to develop complex mechanisms or to maintain them in changing environments."
Valiant started teaching at Harvard University in 1982 and is currently the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the Harvard School of Engineering and Applied Sciences. Prior to 1982 he taught at Carnegie Mellon University, the University of Leeds, and the University of Edinburgh.
Valiant received the Nevanlinna Prize in 1986, the Knuth Prize in 1997, the EATCS Award in 2008,^{[16]} and the Turing Award in 2010.^{[17]}^{[18]} He was elected a Fellow of the Royal Society (FRS) in 1991,^{[4]} a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 1992,^{[19]} and a member of the United States National Academy of Sciences in 2001.^{[20]} Valiant's nomination for the Royal Society reads:
Valiant has contributed in a decisive way to the growth of almost every branch of theoretical computer science. His work is concerned mainly with quantifying mathematically the resource costs of solving problems on a computer. In early work (1975) he found the asymptotically fastest algorithm known for recognising contextfree languages. At the same time, he pioneered the use of communication properties of graphs for analysing computations. In 1977 he defined the notion of #Pcompleteness ("sharpP") and established its utility in classifying counting or enumeration problems according to computational tractability. The first application was to counting matchings (the matrix permanent function). In 1984 Valiant introduced a definition of inductive learning that for the first time reconciles computational feasibility with the applicability to nontrivial classes of logical rules to be learned.* More recently he has devised a scheme for efficient routing of communications in a multiprocessor system. He showed that the overheads involved even in a sparse network need not grow with the size of the system. This establishes, from a theoretical viewpoint, the possibility of efficient general purpose parallel computers.^{[5]}
The citation for his A.M. Turing Award reads:
For transformative contributions to the theory of computation, including the theory of probably approximately correct (PAC) learning, the complexity of enumeration and of algebraic computation, and the theory of parallel and distributed computing.^{[6]}
His two sons Gregory Valiant^{[21]} and Paul Valiant^{[22]} are both also theoretical computer scientists.^{[8]}
This article incorporates text available under the CC BY 4.0 license.