What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Programming language
Steven Skiena mostly deals with Artificial intelligence, Natural language processing, Combinatorics, Sentiment analysis and Language model.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Literal and Pattern recognition.
The Machine learning study combines topics in areas such as Class, Anomaly detection and Training set.
His work carried out in the field of Natural language processing brings together such families of science as Polyglot, Word and Knowledge graph.
His Combinatorics research focuses on subjects like Discrete mathematics, which are linked to Flexibility.
His Language model research includes themes of Feature learning and Point.
His most cited work include:
- DeepWalk: online learning of social representations (3514 citations)
- The Algorithm Design Manual (840 citations)
- DeepWalk: Online Learning of Social Representations (657 citations)
What are the main themes of his work throughout his whole career to date?
Steven Skiena mainly investigates Artificial intelligence, Combinatorics, Algorithm, Natural language processing and Theoretical computer science.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning.
His Combinatorics research is multidisciplinary, incorporating perspectives in Discrete mathematics and Upper and lower bounds.
Steven Skiena combines subjects such as Word and Key with his study of Natural language processing.
He most often published in these fields:
- Artificial intelligence (16.67%)
- Combinatorics (15.77%)
- Algorithm (12.50%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (16.67%)
- Natural language processing (12.50%)
- Theoretical computer science (10.42%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Theoretical computer science, Embedding and Graph.
His research integrates issues of Machine learning and Task in his study of Artificial intelligence.
Particularly relevant to Deep learning is his body of work in Machine learning.
His studies in Deep learning integrate themes in fields like Class, Anomaly detection and Statistical model.
His Natural language processing study incorporates themes from Word, Structure, Social media, Key and Data set.
His Graph study combines topics from a wide range of disciplines, such as Matrix, Graph, Algorithm and Transitive relation.
Between 2013 and 2021, his most popular works were:
- DeepWalk: online learning of social representations (3514 citations)
- DeepWalk: Online Learning of Social Representations (657 citations)
- Statistically Significant Detection of Linguistic Change (230 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Programming language
Steven Skiena spends much of his time researching Artificial intelligence, Natural language processing, Theoretical computer science, Feature learning and Language model.
His Artificial intelligence study combines topics in areas such as Social network, Machine learning, ENCODE and Personalized search.
His Machine learning research integrates issues from Class and Anomaly detection, Data mining.
His biological study spans a wide range of topics, including Word, Reading, The Internet, Homophily and Social media.
His research integrates issues of Hierarchy, Maxima and minima, Embedding, Set and Unsupervised learning in his study of Feature learning.
His work deals with themes such as Word usage, Language change, Null model, Variation and Semantics, which intersect with Language model.
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