Ronitt Rubinfeld was born in 1964. She is a scientist from Israel and the United States who studies computer theory. She works as a professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. At MIT, she leads the Theory of Computation group in the Computer Science and Artificial Intelligence Laboratory.
Biography
Rubinfeld was born in 1964 in Ohio to an American father and an Israeli mother. She grew up in Ann Arbor, Michigan. She graduated from Huron High School in 1981 and earned a BSE in Electrical and Computer Engineering from the University of Michigan in 1985. She completed a PhD at the University of California, Berkeley in 1990, with Manuel Blum as her advisor. From 1990 to 1992, she worked as a postdoctoral researcher at Princeton University and later at the Hebrew University of Jerusalem.
In 1992, Rubinfeld became an assistant professor of computer science at Cornell University. In 1998, she was promoted to associate professor. In 2004, she became a full professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology in Cambridge. In 2008, she was appointed a full professor in the Raymond and Beverly Sackler Faculty of Exact Sciences at Tel Aviv University.
Rubinfeld has also worked in several industrial research laboratories. In 1998, she was a visiting researcher at IBM Research Almaden. From 1999 to 2003, she was a senior researcher at NEC laboratories in Princeton. In 2004, she was a researcher at the Radcliffe Institute for Science Research.
Work
Rubinfeld is known for her work in computational complexity theory and randomized algorithms. One of her major contributions is her research on property testing, which involves creating algorithms to quickly check if an object has a specific quality. This research is useful in areas like data mining, machine learning, computer vision, and network and system security.
She has also contributed to the study of sublinear-time algorithms, which are algorithms that can provide accurate results without examining all the input data. These algorithms are helpful for analyzing large datasets when processing all the data would take too much time or resources.
She has co-authored more than 120 academic articles that have been referenced in thousands of other articles. One of her key achievements, and an important result in the field of model property testing, is a method for testing whether a function is close to being linear. She developed this method with Manuel Blum and Michael Luby in 1993. The method allows researchers to check a small number of a function's values to determine with high confidence whether the function is nearly linear.
Awards and honors
Rubinfeld was an invited lecturer at the International Congress of Mathematicians in 2006. In 2014, she became a fellow of the Association for Computing Machinery because of her work in delegated computation, sublinear time algorithms, and property testing. She was named a fellow of the American Academy of Arts and Sciences in 2020, joined the National Academy of Sciences in 2022, and became a Guggenheim Fellow in 2023.