astronomy

Leslie Valiant

Leslie Valiant is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Leslie Valiant rather than just read about it. In short: Leslie Gabriel Valiant (born 28 March 1949) is a British American computer scientist and computational theorist. He was born to a chemical engineer father and a translator mother.

Key takeaways

Leslie Valiant belongs to astronomy; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Leslie Valiant to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Leslie Valiant from memory before moving on to harder problems.

Reference excerpt

Leslie Gabriel Valiant (born 28 March 1949) is a British American computer scientist and computational theorist. He was born to a chemical engineer father and a translator mother. He is currently the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University. 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".

Education Valiant was educated at King's College, Cambridge, Imperial College London, and the University of Warwick where he received a PhD in computer science in 1974.

Research and career Valiant is world-renowned for his work in Theoretical Computer Science. Among his many contributions to Complexity Theory, he introduced the notion of #P-completeness ("Sharp-P completeness") to explain why enumeration and reliability problems are intractable. He created the Probably Approximately Correct or PAC model of learning that introduced the field of Computational Learning Theory and became a theoretical basis for the development of Machine Learning. He also introduced the concept of Holographic Algorithms inspired by the Quantum Computation model. In computer systems, he is most well-known for introducing the Bulk Synchronous Parallel processing model. Analogous to the von Neumann model for a single computer architecture, BSP has been an influential model for parallel and distributed computing architectures. Recent examples are Google adopting it for computation at large scale via MapReduce, MillWheel, Pregel and Dataflow, and Facebook creating a graph analytics system capable of processing over 1 trillion edges. There have also been active open-source projects to add explicit BSP programming as well as other high-performance parallel programming models derived from BSP. Popular examples are Hadoop, Spark, Giraph, Hama, Beam and Dask. His earlier work in Automata Theory includes an algorithm for context-free parsing, which is 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. 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.

Awards and honors Valiant received the Nevanlinna Prize in 1986, the Knuth Prize in 1997, the EATCS Award in 2008, and the Turing Award in 2010. He was elected a Fellow of the Royal Society (FRS) in 1991, a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 1992, and a member of the United States National Academy of Sciences in 2001. Valiant's nomination for the Royal Society reads:

Leslie Valiant has contributed in a decisive way to the growth 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 context-free languages. At the same time, he pioneered the use of communication properties of graphs for analysing computations. In 1977, he defined the notion of ‘sharp-P’ (#P)-completeness 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, Leslie 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. This notion, later called ‘probably approximately correct learning’, became a theoretical basis for the development of machine learning. In 1989, he formulated the concept of bulk synchronous computation as a unifying principle for parallel computation. Leslie received the Nevanlinna Prize in 1986, and the Turing Award in 2010. 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.

Personal life His two sons Gregory Valiant and Paul Valiant are both also theoretical computer scientists.

References

External links This article incorporates text available under the CC BY 4.0 license.

Illustrations

Worked examples

Example 1 — a first encounter with Leslie Valiant

Start with the simplest possible case. Write down what Leslie Valiant claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Leslie Valiant before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Leslie Valiant ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Leslie Valiant

In research: Leslie Valiant appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Leslie Valiant in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Leslie Valiant is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1949 births, Academics of the University of Edinburgh, Alumni of the Department of Computing, Imperial College London, so understanding it makes those chapters shorter.
In everyday life: Look for Leslie Valiant outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Leslie Valiant” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Leslie Valiant in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Leslie Valiant means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Leslie Valiant out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Leslie Valiant in simple terms?

Leslie Gabriel Valiant (born 28 March 1949) is a British American computer scientist and computational theorist. He was born to a chemical engineer father and a translator mother.

Why does Leslie Valiant matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Leslie Valiant?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Leslie Valiant.