Michael Haft
- Environmental Chemistry top 10%
- Soil and Water Nutrient Dynamics 2
- Water Science and Technology top 10%
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- Bayesian Modeling and Causal Inference 3
- Neural Networks and Applications 2
- Bayesian Methods and Mixture Models 1
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- Neural dynamics and brain function 4
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- Statistical Methods and Bayesian Inference 2
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- Retinal Development and Disorders 2
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- Photoreceptor and optogenetics research 1
- Co-authors
- Volker TrespJaakko HollménM. K. WeigelH. LenskeNorman K. GlendenningJ. Leo van HemmenMark HedgesRichard Gärtner
- Journals
- Network Computation in Neural Systems (3 papers)Pattern Analysis and Applications (1 paper)Environmental Science Processes & Impacts (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Michael Haft
11 papers receiving 275 citations
Peers
Comparison fields: 5 of 71
- Environmental Chemistry 95
- Water Science and Technology 76
- Nuclear and High Energy Physics 44
- Soil Science 27
- Artificial Intelligence 72
Countries citing papers authored by Michael Haft
This map shows the geographic impact of Michael Haft'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 Haft with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Haft more than expected).
Fields of papers citing papers by Michael Haft
This network shows the impact of papers produced by Michael Haft. 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 Haft. The network helps show where Michael Haft may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Michael Haft, 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 | 2014 | 63 | |
| 2 | 2013 | 0 | |
| 3 | FISHNet: encouraging data sharing and reuse in the freshwater science community | 2012 | 0 |
| 4 | 2011 | 59 | |
| 5 | 2004 | 9 | |
| 6 | 2002 | 62 | |
| 7 | Mean field inference in a general probabilistic setting. | 1999 | 1 |
| 8 | 1999 | 13 | |
| 9 | 1998 | 5 | |
| 10 | 1998 | 16 | |
| 11 | 1998 | 8 | |
| 12 | 1995 | 2 | |
| 13 | 1993 | 48 |
About Michael Haft
Michael Haft is a scholar working on Statistics and Probability, Artificial Intelligence, Cognitive Neuroscience, Environmental Chemistry and Geology, having authored 13 papers that have together received 286 indexed citations. Recurring topics across this work include Neural dynamics and brain function (4 papers), Bayesian Modeling and Causal Inference (3 papers), Statistical Methods and Bayesian Inference (2 papers), Neural Networks and Applications (2 papers), Soil and Water Nutrient Dynamics (2 papers), Retinal Development and Disorders (2 papers), Photoreceptor and optogenetics research (1 paper) and Bayesian Methods and Mixture Models (1 paper). The work is most often cited by research in Environmental Chemistry (95 citations), Water Science and Technology (76 citations), Nuclear and High Energy Physics (44 citations), Soil Science (27 citations) and Artificial Intelligence (72 citations). Michael Haft has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Volker Tresp, Jaakko Hollmén, M. K. Weigel, H. Lenske, Norman K. Glendenning, J. Leo van Hemmen, Mark Hedges, Richard Gärtner, Sean Burke and Robert C. Harris. Their work appears in journals such as Network Computation in Neural Systems, Pattern Analysis and Applications, Environmental Science Processes & Impacts, Physical Review Letters and The Science of The Total Environment.
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.