Marc Maier
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
Papers in
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- Bayesian Modeling and Causal Inference 5
- Data Stream Mining Techniques 1
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- Complex Network Analysis Techniques 3
- Co-authors
- David D. Jensen (8 shared papers)Matthew Hale Rattigan (4 shared papers)Brian John Taylor (3 shared papers)Panayotis Kevrekidis (1 shared paper)Heather A. Harrington (1 shared paper)N. Whitaker (1 shared paper)Christopher M. Danforth (1 shared paper)Joshua R. Minot (1 shared paper)
- Journals
- AI Magazine (1 paper)Mathematical and Computer Modelling (1 paper)ACM Transactions on Computing for Healthcare (1 paper)Scholarworks (University of Massachusetts Amherst) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (3 papers)
- Partner nations
- United StatesGermany
In The Last Decade
Marc Maier
13 papers receiving 286 citations
Peers
Comparison fields: 5 of 62
- Statistical and Nonlinear Physics 119
- Health Informatics 8
- Modeling and Simulation 19
- Artificial Intelligence 145
- Signal Processing 41
Countries citing papers authored by Marc Maier
This map shows the geographic impact of Marc Maier'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 Marc Maier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc Maier more than expected).
Fields of papers citing papers by Marc Maier
This network shows the impact of papers produced by Marc Maier. 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 Marc Maier. The network helps show where Marc Maier may publish in the future.
Co-authors
The 21 scholars most cited alongside Marc Maier, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 108 | |
| 2 | 2006 | 46 | |
| 3 | 2007 | 22 | |
| 4 | 2010 | 20 | |
| 5 | 2020 | 19 | |
| 6 | 2008 | 17 | |
| 7 | 2007 | 16 | |
| 8 | 2022 | 14 | |
| 9 | 2007 | 11 | |
| 10 | Learning the structure of causal models with relational and temporal dependence | 2015 | 9 |
| 11 | 2002 | 8 | |
| 12 | 2011 | 8 | |
| 13 | 2019 | 6 |
About Marc Maier
Marc Maier is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Demography, Health Informatics and Modeling and Simulation, having authored 13 papers that have together received 304 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (5 papers), Complex Network Analysis Techniques (3 papers), Insurance and Financial Risk Management (2 papers), Bioinformatics and Genomic Networks (2 papers), Insurance, Mortality, Demography, Risk Management (2 papers), Data Quality and Management (1 paper), Rough Sets and Fuzzy Logic (1 paper) and Data Stream Mining Techniques (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (119 citations), Health Informatics (8 citations), Modeling and Simulation (19 citations), Artificial Intelligence (145 citations) and Signal Processing (41 citations). Marc Maier has collaborated with scholars based in United States and Germany. Frequent co-authors include David D. Jensen, Matthew Hale Rattigan, Brian John Taylor, Panayotis Kevrekidis, Heather A. Harrington, N. Whitaker, Christopher M. Danforth, Joshua R. Minot, Peter Sheridan Dodds and Nicholas C Cheney. Their work appears in journals such as AI Magazine, Mathematical and Computer Modelling, ACM Transactions on Computing for Healthcare, Scholarworks (University of Massachusetts Amherst) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.