What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Data mining, Cluster analysis, Information retrieval, World Wide Web and Graph.
His work on Association rule learning as part of his general Data mining study is frequently connected to Simple, Obstacle and Randomization, thereby bridging the divide between different branches of science.
His study on Cluster analysis is covered under Artificial intelligence.
Aristides Gionis interconnects Shortest path problem and Pattern recognition in the investigation of issues within Artificial intelligence.
Aristides Gionis has included themes like Nearest neighbor search, Real-time web and Personalization in his Information retrieval study.
His Machine learning study combines topics from a wide range of disciplines, such as Linear search, Hopscotch hashing and Tree decomposition.
His most cited work include:
- Similarity Search in High Dimensions via Hashing (2912 citations)
- Finding high-quality content in social media (1007 citations)
- Maintaining Stream Statistics over Sliding Windows (680 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Theoretical computer science, Graph, Data mining, Artificial intelligence and Social media.
His Theoretical computer science research focuses on subjects like Approximation algorithm, which are linked to Submodular set function.
His Graph course of study focuses on Algorithm and Sequence.
His Data mining study frequently links to other fields, such as Cluster analysis.
His Artificial intelligence study integrates concerns from other disciplines, such as Structure, Machine learning and Pattern recognition.
The concepts of his Social media study are interwoven with issues in Information retrieval, Maximization and Data science.
He most often published in these fields:
- Theoretical computer science (25.08%)
- Graph (21.82%)
- Data mining (17.92%)
What were the highlights of his more recent work (between 2018-2021)?
- Graph (21.82%)
- Theoretical computer science (25.08%)
- Mathematical optimization (6.51%)
In recent papers he was focusing on the following fields of study:
Aristides Gionis mainly investigates Graph, Theoretical computer science, Mathematical optimization, Scalability and Discrete mathematics.
Aristides Gionis interconnects Multiset, Data mining and Automatic summarization in the investigation of issues within Graph.
His studies in Data mining integrate themes in fields like Evolving networks and Centrality.
His Theoretical computer science study combines topics in areas such as Optimization problem, Representation, Laplacian matrix and Computational sociology.
His work focuses on many connections between Mathematical optimization and other disciplines, such as Measure, that overlap with his field of interest in Dynamic network analysis and Similarity.
In Contrast, Aristides Gionis works on issues like Artificial intelligence, which are connected to Structure.
Between 2018 and 2021, his most popular works were:
- Discovering Polarized Communities in Signed Networks (16 citations)
- So) Big Data and the transformation of the city (10 citations)
- So) Big Data and the transformation of the city (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Graph, Social media, Theoretical computer science, Greedy algorithm and Heuristics.
His research integrates issues of Data mining and Pattern recognition in his study of Graph.
His work on Anomaly detection is typically connected to Correlation as part of general Data mining study, connecting several disciplines of science.
His Social media research includes elements of Data science, Knowledge extraction, Data analysis and Virtual research environment.
His Theoretical computer science research integrates issues from Cluster analysis, Representation and Computational sociology.
His Greedy algorithm research incorporates elements of Information Dissemination, Computational mathematics, Scalability and Maximization.
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