Joshua L. Phillips
- Molecular Biology
- Health, Toxicology and Mutagenesis
- Pollution
- Artificial Intelligence
- Materials Chemistry
- Co-authors
- Michael E. ColvinShawn NewsamS. GnanakaranEdmond Y. LauVladimir N. UverskyHans HuangJustin AchesonV. V. Krishnan
- Topics
- Protein Structure and Dynamics (10 papers)HIV Research and Treatment (5 papers)Reinforcement Learning in Robotics (3 papers)
- Partner nations
- United StatesUnited KingdomGhana
In The Last Decade
Joshua L. Phillips
26 papers receiving 546 citations
Peers
Comparison fields: 5 of 106
- Molecular Biology 341
- Health, Toxicology and Mutagenesis 53
- Pollution 44
- Artificial Intelligence 42
- Materials Chemistry 38
Countries citing papers authored by Joshua L. Phillips
This map shows the geographic impact of Joshua L. Phillips'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 Joshua L. Phillips with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua L. Phillips more than expected).
Fields of papers citing papers by Joshua L. Phillips
This network shows the impact of papers produced by Joshua L. Phillips. 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 Joshua L. Phillips. The network helps show where Joshua L. Phillips may publish in the future.
Co-authorship network of co-authors of Joshua L. Phillips
This figure shows the co-authorship network connecting the top 25 collaborators of Joshua L. Phillips. A scholar is included among the top collaborators of Joshua L. Phillips 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 Joshua L. Phillips. Joshua L. Phillips is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | 3 | |
| 7 | 78 | |
| 8 | n-task Learning: Solving Multiple or Unknown Numbers of Reinforcement Learning Problems. | 2 |
| 9 | Multilayer Context Reasoning in a Neurobiologically Inspired Working Memory Model for Cognitive Robots. | 0 |
| 10 | 1 | |
| 11 | Working Memory Concept Encoding Using Holographic Reduced Representations. | 2 |
| 12 | 30 | |
| 13 | 1 | |
| 14 | 20 | |
| 15 | 10 | |
| 16 | 10 | |
| 17 | Validation of computational approaches for studying disordered and unfolded protein dynamics using polymer models | 1 |
| 18 | 31 | |
| 19 | 268 | |
| 20 | A computational neuroscience model of working memory with application to robot perceptual learning | 6 |
About Joshua L. Phillips
Joshua L. Phillips is a scholar working on Virology, Cognitive Neuroscience and Artificial Intelligence, having authored 31 papers that have together received 556 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (10 papers), HIV Research and Treatment (5 papers) and Reinforcement Learning in Robotics (3 papers). The work is most often cited by research in Structural Biology (10 citations), Molecular Medicine (31 citations) and Molecular Biology (341 citations). Joshua L. Phillips has collaborated with scholars based in United States, United Kingdom and Ghana. Frequent co-authors include Michael E. Colvin, Shawn Newsam, S. Gnanakaran, Edmond Y. Lau, Vladimir N. Uversky, Hans Huang, Justin Acheson, V. V. Krishnan, Michael Rexach and Justin Yamada. Their work appears in journals such as Journal of Molecular Biology, IEEE Access and BMC Bioinformatics.
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.