What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Database
Philippe Cudré-Mauroux focuses on Crowdsourcing, Linked data, Information retrieval, Semantic interoperability and World Wide Web.
His work carried out in the field of Crowdsourcing brings together such families of science as Data science, Machine learning and Human intelligence, Artificial intelligence.
He focuses mostly in the field of Human intelligence, narrowing it down to topics relating to Categorization and, in certain cases, Scalability.
His Linked data research includes themes of Learning to rank, Feature, Data mining, Probabilistic logic and Web application.
His Semantic interoperability study combines topics in areas such as Semantic computing, Semantic Web, Data independence and Distributed computing.
His work on World Wide Web is being expanded to include thematically relevant topics such as Semantics.
His most cited work include:
- P-Grid: a self-organizing structured P2P system (341 citations)
- ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking (319 citations)
- HYRISE: a main memory hybrid storage engine (218 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Information retrieval, World Wide Web, Artificial intelligence, Linked data and Data mining.
His Information retrieval research includes elements of Semantics, Graph and Data management.
His biological study spans a wide range of topics, including Distributed computing, Scalability and Data integration.
As a part of the same scientific study, Philippe Cudré-Mauroux usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Crowdsourcing.
Philippe Cudré-Mauroux interconnects Entity linking, Human intelligence and Data science in the investigation of issues within Crowdsourcing.
As a part of the same scientific family, Philippe Cudré-Mauroux mostly works in the field of Linked data, focusing on RDF and, on occasion, Database and RDF query language.
He most often published in these fields:
- Information retrieval (21.20%)
- World Wide Web (20.28%)
- Artificial intelligence (14.75%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (14.75%)
- Theoretical computer science (7.83%)
- Embedding (5.53%)
In recent papers he was focusing on the following fields of study:
Philippe Cudré-Mauroux mainly investigates Artificial intelligence, Theoretical computer science, Embedding, Data mining and Knowledge graph.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Pattern recognition.
His Data mining research is multidisciplinary, incorporating perspectives in Missing data and Cluster analysis.
His Knowledge graph research is multidisciplinary, relying on both End-to-end principle, Programming language and Database.
His research investigates the connection between Training set and topics such as Feature vector that intersect with problems in Crowdsourcing.
The Information retrieval study combines topics in areas such as Annotation and Leverage.
Between 2018 and 2021, his most popular works were:
- Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (52 citations)
- Playing Atari with Six Neurons (24 citations)
- Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Database
Artificial intelligence, Data mining, Crowdsourcing, Theoretical computer science and Set are his primary areas of study.
His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence.
His research in Data mining intersects with topics in Missing data and Imputation.
In his works, Philippe Cudré-Mauroux conducts interdisciplinary research on Crowdsourcing and Fair scheduling.
His studies deal with areas such as Embedding, Graph embedding and Knowledge base as well as Theoretical computer science.
He interconnects Server, Search engine indexing, Cluster analysis and Implementation in the investigation of issues within Set.
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