Carla P. Gomes
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- Constraint Satisfaction and Optimization 36
- Computational Theory and Mathematics top 0.5%
- Artificial Intelligence top 1%
- Bayesian Modeling and Causal Inference 14
- Machine Learning and Algorithms 11
- AI-based Problem Solving and Planning 10
- Logic, Reasoning, and Knowledge 10
- Software top 5%
- Signal Processing top 2%
- Data Management and Algorithms 13
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- Machine Learning in Materials Science 18
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- Wildlife Ecology and Conservation 10
Carla P. Gomes
149 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Computer Networks and Communications 1.0k
- Computational Theory and Mathematics 622
- Artificial Intelligence 1.2k
- Software 124
- Signal Processing 304
Countries citing papers authored by Carla P. Gomes
This map shows the geographic impact of Carla P. Gomes'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 Carla P. Gomes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carla P. Gomes more than expected).
Fields of papers citing papers by Carla P. Gomes
This network shows the impact of papers produced by Carla P. Gomes. 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 Carla P. Gomes. The network helps show where Carla P. Gomes may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Carla P. Gomes, 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 | 2023 | 3 | |
| 2 | 2023 | 6 | |
| 3 | 2023 | 14 | |
| 4 | 2022 | 3 | |
| 5 | 2021 | 54 | |
| 6 | 2019 | 21 | |
| 7 | End-to-End Learning for the Deep Multivariate Probit Model | 2018 | 3 |
| 8 | Variable elimination in the Fourier domain | 2016 | 2 |
| 9 | 2013 | 1 | |
| 10 | Embed and Project: Discrete Sampling with Universal Hashing | 2013 | 34 |
| 11 | 2011 | 1 | |
| 12 | Ranking structured documents: a large margin based approach for patent prior art search | 2009 | 10 |
| 13 | Integrating systematic and local search paradigms: a new strategy for MaxSAT | 2009 | 22 |
| 14 | Learning optimal subsets with implicit user preferences | 2009 | 1 |
| 15 | The impact of network topology on pure Nash equilibria in graphical games | 2007 | 5 |
| 16 | From sampling to model counting | 2007 | 45 |
| 17 | The impact of balancing on problem hardness in a highly structured domain | 2006 | 6 |
| 18 | 2003 | 4 | |
| 19 | The Promise of LP to Boost CSP Techniques for Combinatorial Problems | 2002 | 16 |
| 20 | Generating Satisfiable Problem Instances | 2000 | 84 |
About Carla P. Gomes
Carla P. Gomes is a scholar working on Computer Networks and Communications, Ecological Modeling and Artificial Intelligence, having authored 154 papers that have together received 3.4k indexed citations. Recurring topics across this work include Constraint Satisfaction and Optimization (36 papers), Machine Learning in Materials Science (18 papers), Bayesian Modeling and Causal Inference (14 papers), Data Management and Algorithms (13 papers), Machine Learning and Algorithms (11 papers), AI-based Problem Solving and Planning (10 papers), Logic, Reasoning, and Knowledge (10 papers) and Wildlife Ecology and Conservation (10 papers). The work is most often cited by research in Computer Networks and Communications (1.0k citations), Computational Theory and Mathematics (622 citations) and Artificial Intelligence (1.2k citations). Carla P. Gomes has collaborated with scholars based in United States, China and Spain. Frequent co-authors include Bart Selman, Henry Kautz, Ashish Sabharwal, John M. Gregoire, Nuno Crato, Ryan Williams, David B. Shmoys, Bart Selman, Dan Guevarra and Yexiang Xue. Their work appears in journals such as Nature, Nature Communications and Ecology.
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.