Pattern Matching is one of the oldest fields in computer science. It contributed some of the most venerable algorithms (Knuth-Morris-Pratt) and data structures (tries and suffix trees). Nevertheless, it has also contributed to many applications, from searching in a database, to searching the internet. It gave rise to some areas that have grown to be professional fields of their own, such as Computational Biology. It continues to have an important theoretical and practical role till today. Our research team has been at the forefront of Pattern Matching research for decades. The group produced over 25 Ph.D. students, dozens of Masters students, and hosted many international postdoctoral researchers. Graduates of the group are active in research all over the world. We advanced multidimensional matching, pioneered compressed matching, introduced the recovery model, where the goal is to recover the initial data from corrupt input, developed pattern matching in the streaming model, and are currently active in Pattern Matching on a dynamically changing text, motivated by evolving data sources such as the world web.
Current Ph.D. Students:
Tirza Hirst, Mathieu Raffinot, Tomasz Kocioumaka, Noa Lewenstein, Emanuel Dar, Avivit Levy, Ayelet Butman, Dina Sokol, Rivi Shalom, Reuven Kashi, Yuval Krymolowski, Hagai Aronowitz, Yair Horesh, Oren Kapah, Dana Shapira, Estrella Eisenberg, Igor Nor, Moshe Butman, Haim Pariente, Benny Porat, Oren Sar Shalom, Shay Golan, Isaac Goldstein.