Michael Hund
- Computer Vision and Pattern Recognition top 10%
- Cell Biology
- Artificial Intelligence
- Pathology and Forensic Medicine
- Molecular Biology
- Co-authors
- Daniel A. KeimA. RehaChristian RohrdantzDédée F. MurrellNita PatelTobias SchreckAnna L. BrucknerHjalmar Lagast
- Topics
- Data Visualization and Analytics (9 papers)Complex Network Analysis Techniques (3 papers)Data Analysis with R (2 papers)
- Journals
- IEEE Transactions on Visualization and Computer GraphicsComputer Graphics ForumOrphanet Journal of Rare Diseases
- Partner nations
- GermanyAustriaUnited States
In The Last Decade
Michael Hund
14 papers receiving 216 citations
Peers
Comparison fields: 5 of 91
- Computer Vision and Pattern Recognition 72
- Cell Biology 59
- Artificial Intelligence 47
- Pathology and Forensic Medicine 35
- Molecular Biology 27
Countries citing papers authored by Michael Hund
This map shows the geographic impact of Michael Hund'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 Hund with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Hund more than expected).
Fields of papers citing papers by Michael Hund
This network shows the impact of papers produced by Michael Hund. 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 Hund. The network helps show where Michael Hund may publish in the future.
Co-authorship network of co-authors of Michael Hund
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Hund. A scholar is included among the top collaborators of Michael Hund 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 Hund. Michael Hund is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 93 | |
| 3 | HistoBankVis : Detecting Language Change via Data Visualization | 5 |
| 4 | 1 | |
| 5 | 32 | |
| 6 | 4 | |
| 7 | 22 | |
| 8 | 19 | |
| 9 | Visual Quality Assessment of Subspace Clusterings | 3 |
| 10 | Visual Analytics for the Prediction of Movie Rating and Box Office Performance | 2 |
| 11 | 2 | |
| 12 | MooVis -- A Visual Analytics Tool for the Prediction of Movie Viewer Ratings and Boxoffice | 1 |
| 13 | 10 | |
| 14 | Getting there first : real-time detection of real-world incidents on Twitter | 21 |
About Michael Hund
Michael Hund is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Statistical and Nonlinear Physics, having authored 14 papers that have together received 221 indexed citations. Recurring topics across this work include Data Visualization and Analytics (9 papers), Complex Network Analysis Techniques (3 papers) and Data Analysis with R (2 papers). The work is most often cited by research in Cell Biology (59 citations), Computer Vision and Pattern Recognition (72 citations) and Urology (14 citations). Michael Hund has collaborated with scholars based in Germany, Austria and United States. Frequent co-authors include Daniel A. Keim, A. Reha, Christian Rohrdantz, Dédée F. Murrell, Nita Patel, Tobias Schreck, Anna L. Bruckner, Hjalmar Lagast, Andreas Weiler and Miloš Krstajić. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum and Orphanet Journal of Rare Diseases.
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.