|Fields||Randomized and probabilistic algorithms, Communication networks, Natural language processing|
|Alma mater||Hebrew University|
|Thesis||Hilbert Transforms On a Half Line and Mixed Elliptic Boundary Problems in the Plane (1963)|
|Doctoral students||Vardy Amdursky, Mira Balaban, Catriel Beeri, Danny Dolev, Jakob Gonczarowski, Dan Gordon, Craig Gotsman, Daniel Lehmann, Moshe Morgenstern, Joseph Naor, Ainat Rogel, Dmitry Ryabogin, Jeanette Schmidt-Pruzan, Assaf Schuster, Clara Shwartzman, Marc Snir, Eli Upfal|
|Known for||pumping lemma|
Eliahu (Eli) Shamir (Hebrew: אליהו שמיר) is an Israeli mathematician and computer scientist, the Jean and Helene Alfassa Professor Emeritus of Computer Science at the Hebrew University of Jerusalem.
Shamir earned his Ph.D. from the Hebrew University in 1963, under the supervision of Shmuel Agmon. After briefly holding faculty positions at the University of California, Berkeley and Northwestern University, he returned to the Hebrew University in 1966, and was promoted to full professor in 1972.
Shamir was one of the discoverers of the pumping lemma for context-free languages. He did research in partial differential equations, automata theory, random graphs, computational learning theory, and computational linguistics. He was (with Michael O. Rabin) one of the founders of the computer science program at the Hebrew University.
Awards and honors
He was given his named chair in 1987, and in 2002 a workshop on learning and formal verification was held in his honor at Neve Ilan, Israel.
- Bar-Hillel, Y.; Perles, M.; Shamir, E. (1961), "On formal properties of simple phrase structure grammars", Zeitschrift für Phonetik, Sprachwissenschaft und Kommunikationsforschung 14 (2): 143–172.
- Shamir, E.; Spencer, J. (1987), "Sharp concentration of the chromatic number on random graphs Gn,p", Combinatorica 7 (1): 121–129, doi:10.1007/BF02579208, MR 905159.
- Freund, Yoav; Seung, H. Sebastian; Shamir, Eli; Tishby, Naftali (1997), "Selective sampling using the query by committee algorithm", Machine Learning 28 (2–3): 133–168, doi:10.1023/A:1007330508534.