What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Statistics
Justin Zobel mostly deals with Information retrieval, Data mining, Search engine indexing, Ranking and Ranking.
His studies deal with areas such as Space and Information system as well as Information retrieval.
His Data mining study combines topics from a wide range of disciplines, such as Inverted index, Context and Rating scale.
His research integrates issues of Lexicon, Pattern matching, Plagiarism detection and Code in his study of Search engine indexing.
His Ranking research includes themes of Rank and Identification.
His study looks at the relationship between Ranking and topics such as Document retrieval, which overlap with Concept search and Query expansion.
His most cited work include:
- Inverted files for text search engines (940 citations)
- Bandage: interactive visualization of de novo genome assemblies (646 citations)
- SRST2: Rapid genomic surveillance for public health and hospital microbiology labs (574 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Information retrieval, Data mining, Artificial intelligence, Database and Search engine indexing.
His Information retrieval study typically links adjacent topics like Information system.
In his research, Document retrieval is intimately related to Inverted index, which falls under the overarching field of Data mining.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, Computer vision and Natural language processing.
Search engine indexing and Index are frequently intertwined in his study.
Justin Zobel combines subjects such as Web search query and Query optimization with his study of Query expansion.
He most often published in these fields:
- Information retrieval (43.03%)
- Data mining (18.99%)
- Artificial intelligence (14.54%)
What were the highlights of his more recent work (between 2013-2020)?
- Information retrieval (43.03%)
- Database (11.87%)
- Artificial intelligence (14.54%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Information retrieval, Database, Artificial intelligence, Natural language processing and Biological database.
His Information retrieval study integrates concerns from other disciplines, such as Interactive visualization and Contig.
His Database study incorporates themes from Data mining, Duplicate detection, Annotation, Solid compression and Data quality.
His Data quality research is multidisciplinary, relying on both Consistency, UniProt and Identification.
His work is dedicated to discovering how Natural language processing, Coreference are connected with Event and Domain and other disciplines.
In his study, which falls under the umbrella issue of Biological database, European Nucleotide Archive is strongly linked to GenBank.
Between 2013 and 2020, his most popular works were:
- Bandage: interactive visualization of de novo genome assemblies (646 citations)
- SRST2: Rapid genomic surveillance for public health and hospital microbiology labs (574 citations)
- Can machine translation systems be evaluated by the crowd alone (90 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Statistics
His primary scientific interests are in Artificial intelligence, Information retrieval, Natural language processing, Database and Data mining.
His Artificial intelligence research incorporates elements of Predictive modelling, Machine learning, Recommender system and Performance prediction.
His study in Information retrieval is interdisciplinary in nature, drawing from both Field, String searching algorithm and Taxonomy.
His studies in Natural language processing integrate themes in fields like Event, Isolation, Translation and Coreference.
His work deals with themes such as Annotation, Biological database, Data quality and GenBank, which intersect with Database.
His Data mining research is multidisciplinary, incorporating elements of Supercomputer, Genome-wide association study, Computational complexity theory, Multivariate analysis and SNP array.
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