Samuel C. Hoffman
- Artificial Intelligence top 5%
- Safety Research top 1%
- Health Informatics top 1%
- Information Systems top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Prasanna SattigeriKush R. VarshneyAleksandra MojsilovićVijil ChenthamarakshanStephanie HoudeMichael HindRachel BellamyJohn T. Richards
- Topics
- Explainable Artificial Intelligence (XAI) (5 papers)Computational Drug Discovery Methods (4 papers)Artificial Intelligence in Healthcare and Education (3 papers)
- Journals
- Science AdvancesJournal of Machine Learning ResearchIBM Journal of Research and Development
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Samuel C. Hoffman
14 papers receiving 697 citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 369
- Safety Research 296
- Health Informatics 111
- Information Systems 62
- Computer Vision and Pattern Recognition 61
Countries citing papers authored by Samuel C. Hoffman
This map shows the geographic impact of Samuel C. Hoffman'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 Samuel C. Hoffman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel C. Hoffman more than expected).
Fields of papers citing papers by Samuel C. Hoffman
This network shows the impact of papers produced by Samuel C. Hoffman. 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 Samuel C. Hoffman. The network helps show where Samuel C. Hoffman may publish in the future.
Co-authorship network of co-authors of Samuel C. Hoffman
This figure shows the co-authorship network connecting the top 25 collaborators of Samuel C. Hoffman. A scholar is included among the top collaborators of Samuel C. Hoffman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Samuel C. Hoffman. Samuel C. Hoffman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistancebreakdown → | 33 |
| 2 | 3 | |
| 3 | 22 | |
| 4 | 9 | |
| 5 | 4 | |
| 6 | 31 | |
| 7 | AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models | 37 |
| 8 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models | 30 |
| 9 | 19 | |
| 10 | 30 | |
| 11 | 16 | |
| 12 | 422 | |
| 13 | 74 | |
| 14 | Application of Active Instability Control to a Heavy Duty Gas Turbine | 1 |
| 15 | 2 |
About Samuel C. Hoffman
Samuel C. Hoffman is a scholar working on Health Informatics, Applied Microbiology and Biotechnology and Computational Theory and Mathematics, having authored 15 papers that have together received 733 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (5 papers), Computational Drug Discovery Methods (4 papers) and Artificial Intelligence in Healthcare and Education (3 papers). The work is most often cited by research in Health Informatics (111 citations), Safety Research (296 citations) and Artificial Intelligence (369 citations). Samuel C. Hoffman has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Prasanna Sattigeri, Kush R. Varshney, Aleksandra Mojsilović, Vijil Chenthamarakshan, Stephanie Houde, Michael Hind, Rachel Bellamy, John T. Richards, Karthikeyan Natesan Ramamurthy and Kuntal Dey. Their work appears in journals such as Science Advances, Journal of Machine Learning Research and IBM Journal of Research and Development.
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.