Justin Zobel
From Wikipedia, the free encyclopedia
Justin Zobel | |
|---|---|
| Born | 1963 (age 62–63) Australia |
| Education | University of Melbourne |
| Known for | Information retrieval, search engines, data structures, text compression |
| Awards | Fellow of the ACM |
| Scientific career | |
| Fields | Computer science |
| Institutions | University of Melbourne |
| Thesis | (1991) |
Justin Zobel is an Australian computer scientist working in information retrieval, search engine technology, and research evaluation. He is a Redmond Barry Distinguished Professor in the School of Computing and Information Systems at the University of Melbourne and serves as Pro Vice-Chancellor for Graduate & International Research.[1][2]
Zobel received his Ph.D. in computer science from the University of Melbourne in 1991. His doctoral research was in type theory for logic programming.[3] Following his Ph.D., he held academic appointments at RMIT University, where he was part of the team that developed an open-source search engines, MG. His work at RMIT contributed to the development of scalable methods for indexing and searching large text collections.[4]
Career
Zobel later returned to the University of Melbourne, where he was appointed professor and subsequently Redmond Barry Distinguished Professor.[5] He has served in administrative roles including Head of the School of Computing and Information Systems and currently as Pro Vice-Chancellor for Graduate & International Research.[6] His has published books Writing for Computer Science[7] and How to Write a Better Thesis, co-authored with David Evans and Paul Gruba.[8][9]
Research
Zobel's research has primarily focused on information retrieval, text indexing, search engine architecture, and data compression. He has contributed to the development of efficient, scalable algorithms for managing and searching large-scale textual data. He has collaborated with Alistair Moffat and others on inverted indexes, similarity measures, and compression techniques.[10][11] He has worked on MG and later Zettair, text retrieval systems designed for high performance on modest hardware.[12] He has also explored self-indexing, dynamic tries, and burst tries as innovative approaches to managing large string datasets.[13]