Michael Wick
Impact in
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- Particle physics theoretical and experimental studies
- Quantum Chromodynamics and Particle Interactions
- High-Energy Particle Collisions Research
- Black Holes and Theoretical Physics
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Semantic Web and Ontologies
Papers in
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- Topic Modeling 17
- Natural Language Processing Techniques 10
- Bayesian Modeling and Causal Inference 8
- Semantic Web and Ontologies 8
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- Data Quality and Management 11
- Co-authors
- Andrew McCallum (17 shared papers)Aoife Bharucha (2 shared papers)Aron Culotta (5 shared papers)Wolfgang Altmannshofer (3 shared papers)Andrzej J. Buras (2 shared papers)Patricia Ball (1 shared paper)David M. Straub (1 shared paper)Thorsten Feldmann (1 shared paper)
- Journals
- Journal of High Energy Physics (3 papers)Language Resources and Evaluation (1 paper)Proceedings of the VLDB Endowment (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)Neural Information Processing Systems (3 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Michael Wick
34 papers receiving 897 citations
Peers
Comparison fields: 5 of 60
- Nuclear and High Energy Physics 402
- Artificial Intelligence 490
- Management Science and Operations Research 154
- Signal Processing 63
- Information Systems 113
Countries citing papers authored by Michael Wick
This map shows the geographic impact of Michael Wick'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 Wick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Wick more than expected).
Fields of papers citing papers by Michael Wick
This network shows the impact of papers produced by Michael Wick. 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 Wick. The network helps show where Michael Wick may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael Wick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 317 | |
| 2 | First-Order Probabilistic Models for Coreference Resolution | 2007 | 118 |
| 3 | 2010 | 77 | |
| 4 | Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function | 2007 | 56 |
| 5 | 2010 | 52 | |
| 6 | A Discriminative Hierarchical Model for Fast Coreference at Large Scale | 2012 | 37 |
| 7 | SampleRank: Training Factor Graphs with Atomic Gradients | 2011 | 36 |
| 8 | 2008 | 33 | |
| 9 | 2006 | 26 | |
| 10 | Unlocking Fairness: a Trade-off Revisited | 2019 | 23 |
| 11 | 2011 | 23 | |
| 12 | 2016 | 22 | |
| 13 | 2007 | 17 | |
| 14 | Exponential Stochastic Cellular Automata for Massively Parallel Inference | 2016 | 15 |
| 15 | Query-Aware MCMC | 2011 | 14 |
| 16 | 2013 | 13 | |
| 17 | FACTORIE: Efficient Probabilistic Programming for Relational Factor Graphs via Imperative Declarations of Structure, Inference and Learning | 2008 | 12 |
| 18 | 2021 | 12 | |
| 19 | Monte Carlo MCMC: Efficient Inference by Approximate Sampling | 2012 | 10 |
| 20 | 2019 | 10 |
About Michael Wick
Michael Wick is a scholar working on Artificial Intelligence, Management Science and Operations Research, Nuclear and High Energy Physics, Computer Networks and Communications and Signal Processing, having authored 34 papers that have together received 973 indexed citations. Recurring topics across this work include Topic Modeling (17 papers), Data Quality and Management (11 papers), Natural Language Processing Techniques (10 papers), Bayesian Modeling and Causal Inference (8 papers), Semantic Web and Ontologies (8 papers), Quantum Chromodynamics and Particle Interactions (4 papers), Particle physics theoretical and experimental studies (4 papers) and Advanced Database Systems and Queries (3 papers). The work is most often cited by research in Nuclear and High Energy Physics (402 citations), Artificial Intelligence (490 citations), Management Science and Operations Research (154 citations), Signal Processing (63 citations) and Information Systems (113 citations). Michael Wick has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Andrew McCallum, Aoife Bharucha, Aron Culotta, Wolfgang Altmannshofer, Andrzej J. Buras, Patricia Ball, David M. Straub, Thorsten Feldmann, Sameer Singh and Khashayar Rohanimanesh. Their work appears in journals such as Journal of High Energy Physics, Language Resources and Evaluation, Proceedings of the VLDB Endowment, International Conference on Artificial Intelligence and Statistics and Neural Information Processing 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.