Search results
Cleve Barry Moler (born August 17, 1939) is an American mathematician and computer programmer specializing in numerical analysis. In the mid to late 1970s, he was one of the authors of LINPACK and EISPACK, Fortran libraries for numerical computing.
Cleve Moler is chief mathematician, chairman, and cofounder of MathWorks. Moler was a professor of math and computer science for almost 20 years at the University of Michigan, Stanford University, and the University of New Mexico.
People also ask
Who is Cleve Moler?
What is the Cleve Moler link?
Why did Jack Little and Cleve Moler create MATLAB?
Who is John Moler?
Apr 18, 2024 · Moler serves as founder and chief mathematician at the company. Moler is a Member of the National Academy of Engineering and has won numerous awards including the IEEE John von Neumann medal, the IEEE-Computer Society Computer Pioneer Award and Sidney Fernbach Award and two awards from SIAM, the main professional society for numerical analysis.
Cleve Moler is chairman and chief scientist at MathWorks. Moler was a professor of math and computer science for almost 20 years at the University of Michigan, Stanford University, and the University of New Mexico.
The company's key product, MATLAB, was created in the 1970s by Cleve Moler, who was chairman of the computer science department at the University of New Mexico at the time. It was a free tool for academics.
- 6,000
- Natick, Massachusetts, USA
- December 7, 1984; 38 years ago in Portola Valley, California, U.S.
- US$1.25 billion (2022)
Cleve Moler was a professor of mathematics and computer science for almost 20 years at the University of Michigan, Stanford University and the University of New Mexico. At New Mexico, he was a professor in the Mathematics department in the late 1970’s and then Chair of the Computer Science department in the early 1980’s.
May 4, 2024 · Posted by Cleve Moler, May 4, 2024. The coefficient of determination, R-squared or R^2, is a popular statistic that describes how well a regression model fits data. It measures the proportion of variation in data that is predicted by a model. However, that is all that R^2 measures.
