Michael Haft

416 citations
15 papers · 323 · h-index 9

Impact in

Papers in

Michael Haft

12 papers receiving 308 citations

Peers

Michael Haft
Comparison fields: 5 of 80
  • Environmental Chemistry 95
  • Water Science and Technology 76
  • Nuclear and High Energy Physics 46
  • Soil Science 28
  • Artificial Intelligence 91
Replace Chihiro Yoshimura with:
Chihiro Yoshimura Japan
Peter M. Higgins United Kingdom
Toby Lewis United Kingdom
Md Ashad Alam United States
Jiashuo Liu China
John Snell United States
Ian Anderson United States
A. Maier Austria
Michael Haft relative to Chihiro Yoshimura Japan Chihiro Yoshimura's profile →
Citations per field
00.5×2×2.6×
Chihiro Yoshimura · 1×
Citations per year

Countries citing papers authored by Michael Haft

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

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.

Border = papers with Michael Haft Line = papers co-authored together Michael Haft links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 200278
2 201464
3 201160
4 199350
5 199817
6 199916
7 200412
8
FISHNet: encouraging data sharing and reuse in the freshwater science community
20128
9 19988
10 19985
11 19952
12
Mean field inference in a general probabilistic setting.
19991
13 20021
14 19941
15 20130

About Michael Haft

Michael Haft is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Signal Processing, Molecular Biology and Environmental Chemistry, having authored 15 papers that have together received 323 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (4 papers), Neural dynamics and brain function (4 papers), Neural Networks and Applications (3 papers), Soil and Water Nutrient Dynamics (2 papers), Statistical Methods and Bayesian Inference (2 papers), Data Management and Algorithms (2 papers), Retinal Development and Disorders (2 papers) and Quantum Chromodynamics and Particle Interactions (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 (46 citations), Soil Science (28 citations) and Artificial Intelligence (91 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, Sean Burke, Andrew Lovett and Adrian L. Collins. Their work appears in journals such as Network Computation in Neural Systems, Environmental Science Processes & Impacts, The Science of The Total Environment, Physical Review Letters and Pattern Analysis and Applications.

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.

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