Daniel Selsam
Impact in
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- Topic Modeling
- Machine Learning and Data Classification
- Machine Learning in Healthcare
- Natural Language Processing Techniques
- Machine Learning and Algorithms
Papers in
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- Machine Learning and Data Classification 2
- Adversarial Robustness in Machine Learning 1
- Explainable Artificial Intelligence (XAI) 1
- Advanced Software Engineering Methodologies 1
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- Software Engineering Research 1
- Co-authors
- Sen Wu (1 shared paper)Christopher De (1 shared paper)Cristina Re (1 shared paper)Alexander Ratner (1 shared paper)Nikolaj Bjørner (1 shared paper)Percy Liang (1 shared paper)David L. Dill (1 shared paper)
- Journals
- PubMed (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United States
In The Last Decade
Daniel Selsam
3 papers receiving 61 citations
Peers
Comparison fields: 5 of 39
- Health Informatics 2
- Artificial Intelligence 40
- Software 4
- Health Information Management 2
- Computer Vision and Pattern Recognition 8
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 7 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 | 56 |
| 2 | NeuroCore: Guiding High-Performance SAT Solvers with Unsat-Core Predictions. | 2019 | 5 |
| 3 | Developing Bug-Free Machine Learning Systems With Formal Mathematics. | 2017 | 4 |
About Daniel Selsam
Daniel Selsam is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 65 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (2 papers), Adversarial Robustness in Machine Learning (1 paper), Explainable Artificial Intelligence (XAI) (1 paper), Software Engineering Research (1 paper), Advanced Software Engineering Methodologies (1 paper) and Data Visualization and Analytics (1 paper). The work is most often cited by research in Health Informatics (2 citations), Artificial Intelligence (40 citations), Software (4 citations), Health Information Management (2 citations) and Computer Vision and Pattern Recognition (8 citations). Daniel Selsam has collaborated with scholars based in United States. Frequent co-authors include Sen Wu, Christopher De, Cristina Re, Alexander Ratner, Nikolaj Bjørner, Percy Liang and David L. Dill. Their work appears in journals such as PubMed and International Conference on Machine Learning.
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.