Estevam Hruschka
- Artificial Intelligence top 0.5%
- Information Systems top 1%
- Management Science and Operations Research top 2%
- Computer Vision and Pattern Recognition top 5%
- Molecular Biology
- Co-authors
- Tom M. MitchellJustin BetteridgeJ. Andrew CarlsonBurr SettlesBryan KisielEduardo R. HruschkaNádia Félix Felipe da SilvaRichard C. Wang
- Topics
- Bayesian Modeling and Causal Inference (24 papers)Topic Modeling (19 papers)Data Mining Algorithms and Applications (17 papers)
- Partner nations
- BrazilUnited StatesAustralia
In The Last Decade
Estevam Hruschka
72 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 113
- Artificial Intelligence 2.0k
- Information Systems 538
- Management Science and Operations Research 320
- Computer Vision and Pattern Recognition 259
- Molecular Biology 150
Countries citing papers authored by Estevam Hruschka
This map shows the geographic impact of Estevam Hruschka'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 Estevam Hruschka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Estevam Hruschka more than expected).
Fields of papers citing papers by Estevam Hruschka
This network shows the impact of papers produced by Estevam Hruschka. 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 Estevam Hruschka. The network helps show where Estevam Hruschka may publish in the future.
Co-authorship network of co-authors of Estevam Hruschka
This figure shows the co-authorship network connecting the top 25 collaborators of Estevam Hruschka. A scholar is included among the top collaborators of Estevam Hruschka 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 Estevam Hruschka. Estevam Hruschka is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 23 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 65 | |
| 8 | 3 | |
| 9 | 10 | |
| 10 | Discovering Relations between Noun Categories | 55 |
| 11 | Toward an Architecture for Never-Ending Language Learningbreakdown → | 1179 |
| 12 | Toward Never Ending Language Learning. | 27 |
| 13 | 6 | |
| 14 | 5 | |
| 15 | 1 | |
| 16 | 21 | |
| 17 | 3 | |
| 18 | 0 | |
| 19 | 3 | |
| 20 | A Nearest-Neighbor Method as a Data Preparation Tool for a Clustering Genetic Algorithm. | 1 |
About Estevam Hruschka
Estevam Hruschka is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research, having authored 80 papers that have together received 2.4k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (24 papers), Topic Modeling (19 papers) and Data Mining Algorithms and Applications (17 papers). The work is most often cited by research in Computational Mathematics (52 citations), Artificial Intelligence (2.0k citations) and Management Science and Operations Research (320 citations). Estevam Hruschka has collaborated with scholars based in Brazil, United States and Australia. Frequent co-authors include Tom M. Mitchell, Justin Betteridge, J. Andrew Carlson, Burr Settles, Bryan Kisiel, Eduardo R. Hruschka, Nádia Félix Felipe da Silva, Richard C. Wang, Nelson F. F. Ebecken and Luiz F.S. Coletta. Their work appears in journals such as Information Sciences, Neurocomputing and Decision Support Systems.
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.