What Is Lotfi Zadeh Known For?


Lotfi Zadeh was a mathematician, computer scientist, electrical engineer, artificial intelligence researcher, and professor of computer science at the University of California, Berkeley. He was born in Baku Azerbaijan on February 04, 1921and died on 06 September 2017. Lotfi attended elementary school for three in Baku.

What Is Lotfi Zadeh Known For?
Lotfi Zadeh

He moved to Iran when he was 10 years old with his family. Lotfi was enrolled in Alborz High School of which was a missionary school. He was educated for the next eight years at the missionary school and he met his future wife there. Lotfi sat for the Iran national university exams and placed third in the nation. He graduated with a degree in electrical engineering from the University of Tehran in 1942.

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Lotfi moved to the United States to continue his education. He lived in New York and worked for an electronic Company and later entered the Massachusetts Institute of Technology(MIT). Lotfi received an MS degree in electrical engineering from MIT in 1946 and went to work as an engineer at Columbia University. Columbia University admitted him as a doctoral student and he received his Ph.D. in electrical engineering from Columbia in 1949.

Lotfi became an assistant professor and taught for ten years at Columbia and was promoted to full professor in 1957. During his long research career, Lotfi made an important scientific contribution in two distinct areas: (1) Linear system theory and classical control systems, and (2) fuzzy sets, fuzzy logic, and related science and technology.

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Lotfi Zadeh is best known for proposing fuzzy mathematics. Lotfi Zadeh became worldwide popular when he gave fuzzy mathematics to the world that has many fuzzy related concepts: fuzzy sets, fuzzy semantics, fuzzy languages, fuzzy logic, fuzzy algorithms, fuzzy control, fuzzy events, fuzzy system, fuzzy probabilities, and fuzzy information.

Lotfi Zadeh

He published his fuzzy sets on 30th November 1965. His theory gave an alternative to the “black and white” parameters of traditional logic and instead allow for more “fuzzy” boundaries that more closely mimic the way humans see the world.

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