Michael S. Gashler
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Control and Systems Engineering top 10%
- Management Science and Operations Research top 10%
- Co-authors
- Harry A. PiersonDan VenturaTony MartinezCharles XieZhenghui ShaGábor CsiszárВладик КрейновичMichael R. Smith
- Topics
- Neural Networks and Applications (10 papers)Time Series Analysis and Forecasting (4 papers)Stock Market Forecasting Methods (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceManagement Science and Operations Research
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsNeurocomputingJournal of Machine Learning Research
- Partner nations
- United StatesGermanyHungary
In The Last Decade
Michael S. Gashler
16 papers receiving 373 citations
Peers
Comparison fields: 5 of 84
- Artificial Intelligence 161
- Computer Vision and Pattern Recognition 108
- Electrical and Electronic Engineering 73
- Control and Systems Engineering 72
- Management Science and Operations Research 40
Countries citing papers authored by Michael S. Gashler
This map shows the geographic impact of Michael S. Gashler's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael S. Gashler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael S. Gashler more than expected).
Fields of papers citing papers by Michael S. Gashler
This network shows the impact of papers produced by Michael S. Gashler. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael S. Gashler. The network helps show where Michael S. Gashler may publish in the future.
Co-authorship network of co-authors of Michael S. Gashler
This figure shows the co-authorship network connecting the top 25 collaborators of Michael S. Gashler. A scholar is included among the top collaborators of Michael S. Gashler based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael S. Gashler. Michael S. Gashler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 6 | |
| 3 | 10 | |
| 4 | 3 | |
| 5 | 217 | |
| 6 | 2 | |
| 7 | 37 | |
| 8 | 4 | |
| 9 | 2 | |
| 10 | 16 | |
| 11 | 3 | |
| 12 | 28 | |
| 13 | 1 | |
| 14 | Waffles : A Machine Learning Toolkit | 24 |
| 15 | 12 | |
| 16 | Iterative Non-linear Dimensionality Reduction with Manifold Sculpting | 24 |
About Michael S. Gashler
Michael S. Gashler is a scholar working on Signal Processing, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 16 papers that have together received 392 indexed citations. Recurring topics across this work include Neural Networks and Applications (10 papers), Time Series Analysis and Forecasting (4 papers) and Stock Market Forecasting Methods (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (108 citations), Artificial Intelligence (161 citations) and Management Science and Operations Research (40 citations). Michael S. Gashler has collaborated with scholars based in United States, Germany and Hungary. Frequent co-authors include Harry A. Pierson, Dan Ventura, Tony Martinez, Charles Xie, Zhenghui Sha, Gábor Csiszár, Владик Крейнович and Michael R. Smith. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing and Journal of Machine Learning Research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.