What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- World Wide Web
His primary scientific interests are in Information retrieval, Artificial intelligence, Web query classification, Machine learning and Web page.
His specific area of interest is Information retrieval, where Jian-Tao Sun studies Topic model.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Pattern recognition, that overlap with his field of interest in Active learning.
Web search query and Search engine are the subject areas of his Web query classification study.
His research in Web search query intersects with topics in Query language and Query expansion.
As a member of one scientific family, Jian-Tao Sun mostly works in the field of Web page, focusing on Automatic summarization and, on occasion, Lexicon, Latent semantic analysis and Ranking.
His most cited work include:
- Cross-domain sentiment classification via spectral feature alignment (549 citations)
- CubeSVD: a novel approach to personalized Web search (329 citations)
- Document summarization using conditional random fields (273 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Information retrieval, Artificial intelligence, Web search query, Web query classification and Data mining.
His research integrates issues of Web page and World Wide Web in his study of Information retrieval.
Jian-Tao Sun interconnects Machine learning, Pattern recognition and Natural language processing in the investigation of issues within Artificial intelligence.
His studies deal with areas such as Database search engine and Image as well as Web search query.
His work is dedicated to discovering how Data mining, Support vector machine are connected with Feature vector and other disciplines.
In the subject of general Search engine, his work in Semantic search and Metasearch engine is often linked to Set and Context, thereby combining diverse domains of study.
He most often published in these fields:
- Information retrieval (55.00%)
- Artificial intelligence (38.75%)
- Web search query (28.75%)
What were the highlights of his more recent work (between 2011-2020)?
- Information retrieval (55.00%)
- Artificial intelligence (38.75%)
- Search engine (20.00%)
In recent papers he was focusing on the following fields of study:
Information retrieval, Artificial intelligence, Search engine, Web search query and Machine learning are his primary areas of study.
Many of his studies on Information retrieval involve topics that are commonly interrelated, such as Data mining.
His Artificial intelligence study integrates concerns from other disciplines, such as Theoretical computer science and Natural language processing.
His Search engine research is multidisciplinary, incorporating perspectives in Data science and Click-through rate.
His Web search query research is multidisciplinary, relying on both Database search engine and Image, Associated image, Image selection.
His work on Transfer of learning and Subgradient method as part of general Machine learning study is frequently connected to Supervised learning, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Between 2011 and 2020, his most popular works were:
- Context-based search query formation (59 citations)
- Multi-domain active learning for text classification (43 citations)
- Sentiment Topic Model with Decomposed Prior. (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- World Wide Web
Jian-Tao Sun spends much of his time researching Information retrieval, Artificial intelligence, Full text search, Query language and Query expansion.
He has researched Information retrieval in several fields, including Probabilistic logic, Nearest neighbor search and Dimension.
Many of his studies on Artificial intelligence apply to Machine learning as well.
His work deals with themes such as Web search query, Web query classification, RDF query language, Sargable and Query optimization, which intersect with Full text search.
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