What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
George Karypis mostly deals with Data mining, Algorithm, Artificial intelligence, Graph partition and Theoretical computer science.
George Karypis has researched Data mining in several fields, including Scalability, Cluster analysis, Document clustering and Graph.
His biological study spans a wide range of topics, including Vertex, Graph theory, Key and Collaborative filtering.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Markov model and Pattern recognition.
In the field of Machine learning, his study on Recommender system overlaps with subjects such as Space.
His research integrates issues of Strength of a graph, Independent set, Graph bandwidth, Sparse matrix and Partition in his study of Graph partition.
His most cited work include:
- Item-based collaborative filtering recommendation algorithms (6199 citations)
- A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs (4174 citations)
- A Comparison of Document Clustering Techniques (2240 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Data mining, Machine learning, Algorithm and Parallel computing.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Natural language processing, Sequence and Pattern recognition.
His studies deal with areas such as Similarity, Feature selection and Cluster analysis as well as Data mining.
His studies in Recommender system and Collaborative filtering are all subfields of Machine learning research.
His Algorithm study incorporates themes from Vertex, Graph partition, Graph, Mathematical optimization and Speedup.
George Karypis combines subjects such as Scalability and Sparse matrix with his study of Parallel computing.
He most often published in these fields:
- Artificial intelligence (29.28%)
- Data mining (23.87%)
- Machine learning (19.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (29.28%)
- Machine learning (19.14%)
- Graph (11.71%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Graph, Graph and Theoretical computer science.
His research in Artificial intelligence intersects with topics in Sequence and Natural language processing.
His Recommender system and Unsupervised learning study, which is part of a larger body of work in Machine learning, is frequently linked to Node, bridging the gap between disciplines.
He interconnects Computation and Speedup in the investigation of issues within Graph.
His Speedup research is multidisciplinary, incorporating elements of Algorithm, Key and Scale.
The concepts of his Theoretical computer science study are interwoven with issues in Scalability, Representation, Mutual information, Cluster analysis and Code.
Between 2018 and 2021, his most popular works were:
- Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks (81 citations)
- RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation (27 citations)
- Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning. (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Operating system
George Karypis spends much of his time researching Artificial intelligence, Graph, Machine learning, Theoretical computer science and Information retrieval.
George Karypis has included themes like Scalability and Graph in his Graph study.
His Recommender system and Relevance study in the realm of Machine learning interacts with subjects such as Personalized learning.
He is interested in Collaborative filtering, which is a field of Recommender system.
His Theoretical computer science study deals with Computation intersecting with Speedup and Deep learning.
His work in the fields of Information retrieval, such as Search engine, intersects with other areas such as Set.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
