David Graff
Impact in
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- Computational Drug Discovery Methods
- Signal Processing top 5%
- Speech and Audio Processing
Papers in
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- Speech Recognition and Synthesis 12
- Natural Language Processing Techniques 9
- Topic Modeling 5
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- Computational Drug Discovery Methods 8
- Co-authors
- Robert R. Knowles (2 shared papers)Connor W. Coley (8 shared papers)Eugene I. Shakhnovich (2 shared papers)Qilei Zhu (1 shared paper)Christopher Cieri (9 shared papers)William H. Green (2 shared papers)Charles J. McGill (1 shared paper)Esther Heid (1 shared paper)
- Journals
- Language Resources and Evaluation (10 papers)Journal of Chemical Information and Modeling (4 papers)Journal of the American Chemical Society (2 papers)The Journal of the Acoustical Society of America (1 paper)Chemical Science (1 paper)
- Partner nations
- United StatesIrelandAustria
In The Last Decade
David Graff
33 papers receiving 1.1k citations
David Graff's Hit Papers
Peers
Comparison fields: 5 of 110
- Computational Theory and Mathematics 370
- Signal Processing 153
- Organic Chemistry 294
- Artificial Intelligence 329
- Materials Chemistry 311
Countries citing papers authored by David Graff
This map shows the geographic impact of David Graff'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 David Graff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Graff more than expected).
Fields of papers citing papers by David Graff
This network shows the impact of papers produced by David Graff. 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 David Graff. The network helps show where David Graff may publish in the future.
Co-authors
The 25 scholars most cited alongside David Graff, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Chemprop: A Machine Learning Package for Chemical Property Prediction Hit paper breakdown → | 2023 | 300 |
| 2 | 2021 | 198 | |
| 3 | 2017 | 164 | |
| 4 | 2020 | 119 | |
| 5 | 2006 | 78 | |
| 6 | 2002 | 36 | |
| 7 | 2007 | 33 | |
| 8 | 2022 | 32 | |
| 9 | THE TDT-3 TEXT AND SPEECH CORPUS | 2007 | 27 |
| 10 | 2002 | 26 | |
| 11 | 2022 | 20 | |
| 12 | 2024 | 17 | |
| 13 | 2000 | 16 | |
| 14 | 1994 | 16 | |
| 15 | 2000 | 14 | |
| 16 | The TDT-2 Text And Speech Corpus | 1999 | 12 |
| 17 | 2006 | 11 | |
| 18 | 2010 | 11 | |
| 19 | 2017 | 11 | |
| 20 | 2012 | 10 |
About David Graff
David Graff is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Materials Chemistry, Signal Processing and Molecular Biology, having authored 34 papers that have together received 1.2k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (12 papers), Natural Language Processing Techniques (9 papers), Machine Learning in Materials Science (9 papers), Computational Drug Discovery Methods (8 papers), Topic Modeling (5 papers), Speech and Audio Processing (5 papers), Protein Structure and Dynamics (3 papers) and Catalytic C–H Functionalization Methods (2 papers). The work is most often cited by research in Computational Theory and Mathematics (370 citations), Signal Processing (153 citations), Organic Chemistry (294 citations), Artificial Intelligence (329 citations) and Materials Chemistry (311 citations). David Graff has collaborated with scholars based in United States, Ireland and Austria. Frequent co-authors include Robert R. Knowles, Connor W. Coley, Eugene I. Shakhnovich, Qilei Zhu, Christopher Cieri, William H. Green, Charles J. McGill, Esther Heid, Kevin P. Greenman and Shih‐Cheng Li. Their work appears in journals such as Language Resources and Evaluation, Journal of Chemical Information and Modeling, Journal of the American Chemical Society, The Journal of the Acoustical Society of America and Chemical Science.
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.