Rubinfeld was born in 1964 in Ohio and grew up in Ann Arbor, Michigan. As a child, she attended Huron High School (class of 1981) and went on to graduate from the University of Michigan with a BSE in Electrical and Computer Engineering (1985). Following that, she received her PhD from the University of California, Berkeley (1990), under the supervision of Manuel Blum.^[1] In the years 1990–1992 she did a post-doctorate at Princeton University in New Jersey and then at the Hebrew University in Jerusalem.

Career edit

In 1992, Rubinfeld joined the faculty of computer science at Cornell University in New York as an associate professor and in 1998 was appointed associate professor. In 2004, she joined as a full professor in the Faculty of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology in Cambridge. In 2008, she received an appointment as a full professor at Tel Aviv University .

Rubinfeld's research interests lie in the fields of computational complexity theory and randomized algorithms, which focus on understanding the limits of computational power and developing efficient algorithms for solving computational problems.

One of her major contributions to theoretical computer science is her work on property testing, which involves designing algorithms to quickly test whether a given object satisfies a certain property. This research has practical applications in fields such as data mining, machine learning, and computer vision, as well as in network and system security.

Rubinfeld has also made important contributions to the study of sublinear algorithms, which are algorithms that do not need to process the entire input in order to produce an accurate result. These algorithms are particularly useful for large-scale data analysis, where processing the entire input may be prohibitively expensive in terms of time and resources.

She has co-authored more than 120 academic articles that have been cited in thousands of different articles. One of her main results, and in the field of model property testing in general, is a method for testing the linearity of a function, which she developed in her work with Manuel Blum and Michael Luby in 1993. The method allows, by sampling a small number of values of a given function, to determine with high probability whether the function is close to a linear function or not.

Rubinfeld also held positions in several research laboratories at various companies in the industry. In 1998, she served as a visiting researcher at the IBM Almaden research laboratories in San Jose (California). Between 1999 and 2003 she served as a senior researcher at the NEC laboratories in Princeton and in 2004 she served as a researcher at the Radcliffe Institute for Science Research.^[2]

• She gave an invited lecture at the International Congress of Mathematicians in 2006.^[3]
• She became a fellow of the Association for Computing Machinery in 2014 for contributions to delegated computation, sublinear time algorithms and property testing.^[4]
• She was elected a fellow of the American Academy of Arts and Sciences (AAAS) in 2020,^[5] a member of the National Academy of Sciences in 2022,^[6] and a 2023 Guggenheim Fellow.^[7]

• Rubinfeld's MIT page