Daniel S. Brown
Impact in
- Computer Science Applications top 10%
- Mobile Crowdsensing and Crowdsourcing
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
Papers in
-
- Reinforcement Learning in Robotics 10
- Adversarial Robustness in Machine Learning 4
- Machine Learning and Algorithms 4
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- Distributed Control Multi-Agent Systems 8
- Optimization and Search Problems 3
- Co-authors
- Michael A. Goodrich (6 shared papers)Scott Niekum (6 shared papers)Ashwin Balakrishna (5 shared papers)Ken Goldberg (5 shared papers)Matthew Johnson (1 shared paper)Anca D. Dragan (2 shared papers)Lee M. Seversky (1 shared paper)Gaurav Datta (1 shared paper)
- Journals
- Journal of Biomechanics (1 paper)Nature (1 paper)International Journal of Cancer (1 paper)Computational Intelligence (1 paper)2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (1 paper)
- Partner nations
- United StatesRomaniaGermany
In The Last Decade
Daniel S. Brown
37 papers receiving 272 citations
Peers
Comparison fields: 5 of 64
- Computer Science Applications 24
- Artificial Intelligence 115
- Computer Networks and Communications 82
- Computer Vision and Pattern Recognition 63
- Control and Systems Engineering 69
Countries citing papers authored by Daniel S. Brown
This map shows the geographic impact of Daniel S. Brown'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 S. Brown with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel S. Brown more than expected).
Fields of papers citing papers by Daniel S. Brown
This network shows the impact of papers produced by Daniel S. Brown. 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 S. Brown. The network helps show where Daniel S. Brown may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel S. Brown, 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 41 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 26 | |
| 2 | 2014 | 26 | |
| 3 | 2012 | 25 | |
| 4 | 2015 | 23 | |
| 5 | 2021 | 20 | |
| 6 | 2014 | 19 | |
| 7 | 1962 | 18 | |
| 8 | 2018 | 15 | |
| 9 | 2013 | 11 | |
| 10 | 2023 | 10 | |
| 11 | 2016 | 10 | |
| 12 | 2023 | 10 | |
| 13 | 2024 | 8 | |
| 14 | 2016 | 8 | |
| 15 | 2014 | 8 | |
| 16 | 2006 | 7 | |
| 17 | 2021 | 7 | |
| 18 | Risk-Aware Active Inverse Reinforcement Learning | 2018 | 6 |
| 19 | Monadic Memoization Mixins | 2006 | 6 |
| 20 | 2022 | 6 |
About Daniel S. Brown
Daniel S. Brown is a scholar working on Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering, Biomedical Engineering and Computer Vision and Pattern Recognition, having authored 41 papers that have together received 302 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Distributed Control Multi-Agent Systems (8 papers), Robot Manipulation and Learning (7 papers), Slime Mold and Myxomycetes Research (5 papers), Modular Robots and Swarm Intelligence (5 papers), Adversarial Robustness in Machine Learning (4 papers), Machine Learning and Algorithms (4 papers) and Optimization and Search Problems (3 papers). The work is most often cited by research in Computer Science Applications (24 citations), Artificial Intelligence (115 citations), Computer Networks and Communications (82 citations), Computer Vision and Pattern Recognition (63 citations) and Control and Systems Engineering (69 citations). Daniel S. Brown has collaborated with scholars based in United States, Romania and Germany. Frequent co-authors include Michael A. Goodrich, Scott Niekum, Ashwin Balakrishna, Ken Goldberg, Matthew Johnson, Anca D. Dragan, Lee M. Seversky, Gaurav Datta, Ryan Hoque and William R. Cook. Their work appears in journals such as Journal of Biomechanics, Nature, International Journal of Cancer, Computational Intelligence and 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE).
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.