Daniel Selsam
Impact in
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- Software Testing and Debugging Techniques
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- Topic Modeling
- Machine Learning and Data Classification
- Machine Learning and Algorithms
- Logic, programming, and type systems
Papers in
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- Machine Learning and Data Classification 2
- Logic, programming, and type systems 2
- Adversarial Robustness in Machine Learning 1
- Logic, Reasoning, and Knowledge 1
- Software 1
- Co-authors
- Nikolaj Bjørner (2 shared papers)Sen Wu (1 shared paper)Cristina Re (1 shared paper)Alexander Ratner (1 shared paper)Christopher De (1 shared paper)Leonardo de Moura (1 shared paper)Percy Liang (1 shared paper)David L. Dill (1 shared paper)
- Journals
- Lecture notes in computer science (3 papers)International Conference on Machine Learning (1 paper)PubMed (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Daniel Selsam
6 papers receiving 135 citations
Peers
Comparison fields: 5 of 40
- Software 21
- Artificial Intelligence 85
- Health Informatics 3
- Computational Theory and Mathematics 35
- Information Systems 23
Countries citing papers authored by Daniel Selsam
This map shows the geographic impact of Daniel Selsam'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 Selsam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Selsam more than expected).
Fields of papers citing papers by Daniel Selsam
This network shows the impact of papers produced by Daniel Selsam. 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 Selsam. The network helps show where Daniel Selsam may publish in the future.
Co-authors
The 8 scholars most cited alongside Daniel Selsam, 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 | Data Programming: Creating Large Training Sets, Quickly. | 2016 | 67 |
| 2 | 2019 | 46 | |
| 3 | 2016 | 10 | |
| 4 | NeuroCore: Guiding High-Performance SAT Solvers with Unsat-Core Predictions. | 2019 | 9 |
| 5 | Developing Bug-Free Machine Learning Systems With Formal Mathematics. | 2017 | 8 |
| 6 | 2021 | 1 |
About Daniel Selsam
Daniel Selsam is a scholar working on Artificial Intelligence, Software, Information Systems, Computer Networks and Communications and Computational Theory and Mathematics, having authored 6 papers that have together received 141 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (2 papers), Software Engineering Research (2 papers), Constraint Satisfaction and Optimization (2 papers), Logic, programming, and type systems (2 papers), Adversarial Robustness in Machine Learning (1 paper), Data Visualization and Analytics (1 paper), Logic, Reasoning, and Knowledge (1 paper) and Formal Methods in Verification (1 paper). The work is most often cited by research in Software (21 citations), Artificial Intelligence (85 citations), Health Informatics (3 citations), Computational Theory and Mathematics (35 citations) and Information Systems (23 citations). Daniel Selsam has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Nikolaj Bjørner, Sen Wu, Cristina Re, Alexander Ratner, Christopher De, Leonardo de Moura, Percy Liang and David L. Dill. Their work appears in journals such as Lecture notes in computer science, International Conference on Machine Learning and PubMed.
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.