Leslie Valiant facts for kids
Quick facts for kids
Leslie Valiant
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Valiant in 2012
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Leslie Gabriel Valiant
28 March 1949 Budapest, Hungarian Republic
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| Nationality | British |
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| Fields | Mathematics Theoretical computer science Computational learning theory Theoretical neuroscience |
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| Thesis | Decision Procedures for Families of Deterministic Pushdown Automata (1974) |
| Doctoral advisor | Mike Paterson |
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Leslie Gabriel Valiant (born March 28, 1949) is a famous British-American computer scientist. He is a professor at Harvard University. In 2010, he won the Turing Award. This award is like the Nobel Prize for computer science. The A.C.M. (Association for Computing Machinery) called him a "heroic figure" in computer science. They praised his bravery and new ideas in solving tough science problems.
Contents
Education and Early Career
Leslie Valiant studied at several top universities. He went to King's College, Cambridge. He also studied at Imperial College London. He earned his PhD in computer science in 1974 from the University of Warwick.
Before joining Harvard, he taught at other universities. These included Carnegie Mellon University, the University of Leeds, and the University of Edinburgh. Since 1982, he has been teaching at Harvard University. He is now the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics.
Key Contributions to Computer Science
Valiant is well-known for his important work in Theoretical Computer Science. This field looks at the basic ideas behind computing. He has made many big contributions to how we understand computers.
Understanding Complex Problems
Valiant helped us understand why some computer problems are very hard to solve. He introduced the idea of #P-completeness. This helps explain why counting and reliability problems can be so difficult.
How Computers Learn
He created the Probably Approximately Correct (PAC) model of learning. This model was a big step for the field of Computational Learning Theory. It also became a key idea for how Machine Learning developed. Machine learning is how computers learn from data without being directly programmed.
New Ways to Compute
Valiant also came up with Holographic Algorithms. These are new ways to solve problems. They were inspired by how quantum computers might work.
Parallel Computing Models
In computer systems, he is known for the Bulk Synchronous Parallel (BSP) model. This model helps computers work together on many tasks at once. It's like a plan for how many computers can share work. Big companies like Google and Facebook use ideas from BSP. For example, Google uses it for tools like MapReduce and Dataflow. Facebook uses it for analyzing huge amounts of data. Many open-source projects also use BSP ideas. These include Hadoop, Spark, and Giraph.
Early Work and Memory Research
His earlier work looked at Automata Theory. This is about how simple machines can process information. He created a very fast way to understand context-free languages. He also studies Computational Neuroscience. In this area, he tries to understand how our brains remember and learn things.
His Book on Learning
In 2013, Valiant wrote a book called Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World. In his book, he talks about how evolution works. He suggests that current theories don't fully explain how fast evolution happens. He believes there's more to learn about how complex life forms develop.
Awards and Recognition
Leslie Valiant has received many important awards for his work.
- In 1986, he won the Nevanlinna Prize.
- He received the Knuth Prize in 1997.
- The EATCS Award was given to him in 2008.
- His most famous award is the Turing Award in 2010.
He was also chosen as a Fellow of the Royal Society (FRS) in 1991. This is a very high honor for scientists in the UK. In 1992, he became a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). In 2001, he joined the United States National Academy of Sciences.
His nomination for the Royal Society praised his work.
Leslie Valiant has greatly helped the field of theoretical computer science. His work focuses on measuring how much computer power is needed to solve problems. He found the fastest known way to recognize context-free languages. He also showed how to classify counting problems based on how hard they are to solve. In 1984, he created a new way to define how computers learn. This idea, called 'probably approximately correct learning,' became a key part of machine learning. In 1989, he developed the idea of bulk synchronous computation for parallel computing. Leslie received the Nevanlinna Prize in 1986, and the Turing Award in 2010.
The Turing Award citation also highlighted his impact.
For important contributions to the theory of computation, including the theory of probably approximately correct (PAC) learning, the complexity of counting and algebraic computation, and the theory of parallel and distributed computing.
Family Life
Leslie Valiant has two sons, Gregory Valiant and Paul Valiant. Both of his sons have also become theoretical computer scientists, following in their father's footsteps.
See also
In Spanish: Leslie Valiant para niños
