David R. So
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning in Healthcare
Papers in
-
- Topic Modeling 3
- Evolutionary Algorithms and Applications 2
- Natural Language Processing Techniques 2
- Reinforcement Learning in Robotics 1
- Metaheuristic Optimization Algorithms Research 1
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- Bone Metabolism and Diseases 1
- Co-authors
- Quoc V. Le (7 shared papers)Liang Chen (1 shared paper)Liang Chen (3 shared papers)Lluís-Miquel Munguía (1 shared paper)Maud Texier (1 shared paper)David S. Patterson (1 shared paper)Urs Hölzle (1 shared paper)Jeff Dean (1 shared paper)
- Journals
- Computer (1 paper)PLoS ONE (1 paper)Proceedings of the Genetic and Evolutionary Computation Conference Companion (1 paper)Cureus (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
David R. So
10 papers receiving 377 citations
David R. So's Hit Papers
Peers
Comparison fields: 5 of 89
- Health Informatics 13
- Artificial Intelligence 169
- Computer Vision and Pattern Recognition 70
- Computer Science Applications 14
- Biophysics 13
Countries citing papers authored by David R. So
This map shows the geographic impact of David R. So'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 R. So with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David R. So more than expected).
Fields of papers citing papers by David R. So
This network shows the impact of papers produced by David R. So. 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 R. So. The network helps show where David R. So may publish in the future.
Co-authors
The 25 scholars most cited alongside David R. So, 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 | The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink Hit paper breakdown → | 2022 | 181 |
| 2 | 2019 | 65 | |
| 3 | 2018 | 64 | |
| 4 | 2021 | 30 | |
| 5 | Searching for Efficient Transformers for Language Modeling | 2021 | 20 |
| 6 | 2023 | 18 | |
| 7 | 2022 | 7 | |
| 8 | AutoML-Zero: Evolving Machine Learning Algorithms From Scratch | 2020 | 4 |
| 9 | 2018 | 1 | |
| 10 | Evolving Machine Learning Algorithms From Scratch | 2020 | 1 |
About David R. So
David R. So is a scholar working on Artificial Intelligence, Molecular Biology, Oncology, Complementary and alternative medicine and Genetics, having authored 10 papers that have together received 391 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Evolutionary Algorithms and Applications (2 papers), Natural Language Processing Techniques (2 papers), Enzyme Structure and Function (1 paper), Bone Metabolism and Diseases (1 paper), Dermatological and Skeletal Disorders (1 paper), Reinforcement Learning in Robotics (1 paper) and Metaheuristic Optimization Algorithms Research (1 paper). The work is most often cited by research in Health Informatics (13 citations), Artificial Intelligence (169 citations), Computer Vision and Pattern Recognition (70 citations), Computer Science Applications (14 citations) and Biophysics (13 citations). David R. So has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Quoc V. Le, Liang Chen, Liang Chen, Lluís-Miquel Munguía, Maud Texier, David S. Patterson, Urs Hölzle, Jeff Dean, Joseph E. Gonzalez and Daniel Rothchild. Their work appears in journals such as Computer, PLoS ONE, Proceedings of the Genetic and Evolutionary Computation Conference Companion, Cureus and Proceedings of the AAAI Conference on Artificial Intelligence.
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.