Daniel J. Fremont
- Software top 10%
- Software Testing and Debugging Techniques 2
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- Machine Learning and Algorithms 4
- Bayesian Modeling and Causal Inference 3
- Adversarial Robustness in Machine Learning 3
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- Formal Methods in Verification 5
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- Software Engineering Research 2
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- Systems Engineering Methodologies and Applications 1
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- Manufacturing Process and Optimization 1
- Co-authors
- Sanjit A. SeshiaKuldeep S. MeelMoshe Y. VardiSupratik ChakrabortyAlberto Sangiovanni‐VincentelliShromona GhoshTommaso DreossiXiangyu Yue
- Journals
- Machine Learning (1 paper)Computers in entertainment (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Daniel J. Fremont
9 papers receiving 115 citations
Peers
Comparison fields: 5 of 26
- Software 31
- Automotive Engineering 26
- Artificial Intelligence 68
- Computational Theory and Mathematics 22
- Signal Processing 14
Countries citing papers authored by Daniel J. Fremont
This map shows the geographic impact of Daniel J. Fremont'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 Daniel J. Fremont with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel J. Fremont more than expected).
Fields of papers citing papers by Daniel J. Fremont
This network shows the impact of papers produced by Daniel J. Fremont. 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 Daniel J. Fremont. The network helps show where Daniel J. Fremont may publish in the future.
Co-authorship network
The 21 scholars most cited alongside Daniel J. Fremont, 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 | 1 | |
| 2 | 2022 | 40 | |
| 3 | 2022 | 0 | |
| 4 | 2021 | 6 | |
| 5 | Scenic: Language-Based Scene Generation. | 2018 | 7 |
| 6 | 2017 | 5 | |
| 7 | 2016 | 4 | |
| 8 | Constrained Sampling and Counting: Universal Hashing Meets SAT Solving | 2015 | 15 |
| 9 | Speeding Up SMT-Based Quantitative Program Analysis. | 2014 | 1 |
| 10 | 2014 | 39 |
About Daniel J. Fremont
Daniel J. Fremont is a scholar working on Software, Computational Theory and Mathematics and Artificial Intelligence, having authored 10 papers that have together received 118 indexed citations. Recurring topics across this work include Formal Methods in Verification (5 papers), Machine Learning and Algorithms (4 papers), Bayesian Modeling and Causal Inference (3 papers), Adversarial Robustness in Machine Learning (3 papers), Software Engineering Research (2 papers), Software Testing and Debugging Techniques (2 papers), Systems Engineering Methodologies and Applications (1 paper) and Manufacturing Process and Optimization (1 paper). The work is most often cited by research in Software (31 citations), Automotive Engineering (26 citations) and Artificial Intelligence (68 citations). Daniel J. Fremont has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Sanjit A. Seshia, Kuldeep S. Meel, Moshe Y. Vardi, Supratik Chakraborty, Alberto Sangiovanni‐Vincentelli, Shromona Ghosh, Tommaso Dreossi, Xiangyu Yue, Edward Kim and Dror Fried. Their work appears in journals such as Machine Learning, Computers in entertainment and arXiv (Cornell University).
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.