What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Mathematical analysis
His primary areas of study are Community structure, Structure, Data science, Complex network and Mechanics.
Peter J. Mucha applies his multidisciplinary studies on Community structure and Node in his research.
His Structure study incorporates themes from Local scale, Core and Politics.
Peter J. Mucha combines subjects such as Social structure, Field and Social network with his study of Data science.
His studies in Complex network integrate themes in fields like Modularity, Theoretical computer science, Cluster analysis and Statistical noise.
His Mechanics research is multidisciplinary, incorporating perspectives in Scaling and Classical mechanics.
His most cited work include:
- Community Structure in Time-Dependent, Multiscale, and Multiplex Networks (1360 citations)
- Dynamic reconfiguration of human brain networks during learning. (1061 citations)
- Communities in networks (772 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Community structure, Theoretical computer science, Network science, Structure and Mechanics.
His work carried out in the field of Community structure brings together such families of science as Distributed computing, Modularity, Data mining and Probability and statistics.
His research integrates issues of Null model and Complex network in his study of Modularity.
His research investigates the link between Theoretical computer science and topics such as Cluster analysis that cross with problems in Voter model and Statistical physics.
His Network science research incorporates themes from Machine learning, Centrality and Artificial intelligence.
His Structure study combines topics from a wide range of disciplines, such as House of Representatives, Politics, Data science and Identification.
He most often published in these fields:
- Community structure (16.75%)
- Theoretical computer science (13.20%)
- Network science (11.68%)
What were the highlights of his more recent work (between 2018-2021)?
- Community structure (16.75%)
- Theoretical computer science (13.20%)
- Topology (5.08%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Community structure, Theoretical computer science, Topology, Network science and Artificial intelligence.
His research in Community structure intersects with topics in Probability and statistics, Heuristics and Social network.
His study on Reachability is often connected to Node as part of broader study in Theoretical computer science.
While the research belongs to areas of Topology, Peter J. Mucha spends his time largely on the problem of Eigenvalues and eigenvectors, intersecting his research to questions surrounding Core, Data integration and Multiplex.
His Artificial intelligence research integrates issues from Machine learning and Pattern recognition.
His Machine learning research is multidisciplinary, relying on both Social media, Information cascade and Complex network.
Between 2018 and 2021, his most popular works were:
- Emotion semantics show both cultural variation and universal structure (47 citations)
- Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections (22 citations)
- Stochastic block models with multiple continuous attributes (14 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Social science
His primary areas of investigation include Centrality, Theoretical computer science, Eigenvalues and eigenvectors, Adjacency matrix and Node.
The various areas that Peter J. Mucha examines in his Centrality study include Social network analysis, Administrative claims and Family medicine, Specialty.
His Theoretical computer science study combines topics in areas such as Enhanced Data Rates for GSM Evolution, Cluster analysis and Transitive relation.
His Eigenvalues and eigenvectors research includes elements of Probability theory, Matrix, Interval, PageRank and Coupling.
He has researched Coupling in several fields, including Discrete mathematics, Singular perturbation, Network science and Scaling.
The concepts of his Adjacency matrix study are interwoven with issues in Stochastic block model, Data mining, Biological data and Topology.
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.
