Daniel S. Brown

836 total citations
38 papers, 252 citations indexed

About

Daniel S. Brown is a scholar working on Artificial Intelligence, Computer Networks and Communications and Control and Systems Engineering. According to data from OpenAlex, Daniel S. Brown has authored 38 papers receiving a total of 252 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 12 papers in Computer Networks and Communications and 9 papers in Control and Systems Engineering. Recurrent topics in Daniel S. Brown's work include Reinforcement Learning in Robotics (8 papers), Distributed Control Multi-Agent Systems (8 papers) and Robot Manipulation and Learning (7 papers). Daniel S. Brown is often cited by papers focused on Reinforcement Learning in Robotics (8 papers), Distributed Control Multi-Agent Systems (8 papers) and Robot Manipulation and Learning (7 papers). Daniel S. Brown collaborates with scholars based in United States, Puerto Rico and Romania. Daniel S. Brown's co-authors include Michael A. Goodrich, Ashwin Balakrishna, Matthew Johnson, Ken Goldberg, Scott Niekum, Anca D. Dragan, Matthew Berger, Gaurav Datta, William R. Cook and Ryan Hoque and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and International Journal of Cancer.

In The Last Decade

Daniel S. Brown

34 papers receiving 224 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel S. Brown United States 10 80 77 60 56 52 38 252
Jiahao Guo China 9 57 0.7× 33 0.4× 45 0.8× 47 0.8× 18 0.3× 31 316
Christopher Reardon United States 11 75 0.9× 45 0.6× 47 0.8× 216 3.9× 67 1.3× 44 403
Hande Çelikkanat Türkiye 7 75 0.9× 214 2.8× 151 2.5× 89 1.6× 45 0.9× 14 332
Keitaro Naruse Japan 12 48 0.6× 126 1.6× 83 1.4× 73 1.3× 117 2.3× 69 464
Florian Vaussard Switzerland 3 81 1.0× 58 0.8× 68 1.1× 48 0.9× 54 1.0× 6 249
Golnaz Habibi United States 9 34 0.4× 112 1.5× 122 2.0× 95 1.7× 72 1.4× 19 283
Daniel Burnier Switzerland 6 84 1.1× 75 1.0× 81 1.4× 137 2.4× 116 2.2× 8 346
Valentin Longchamp Switzerland 5 54 0.7× 119 1.5× 147 2.5× 60 1.1× 55 1.1× 6 279
Chris A. C. Parker Canada 11 70 0.9× 123 1.6× 160 2.7× 51 0.9× 169 3.3× 23 443
Augusto Gómez Eguíluz United Kingdom 12 27 0.3× 27 0.4× 18 0.3× 61 1.1× 45 0.9× 23 305

Countries citing papers authored by Daniel S. Brown

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Daniel S. Brown

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel S. Brown. A scholar is included among the top collaborators of Daniel S. Brown 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 Daniel S. Brown. Daniel S. Brown is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Brown, Daniel S., et al.. (2024). Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks. 1–7. 1 indexed citations
3.
Papadimitriou, Dimitris & Daniel S. Brown. (2024). Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models. 15039–15046.
4.
Zhu, Kevin, Connor Mattson, Ricardo de la Vega Marcos, et al.. (2024). Spiking Neural Networks as a Controller for Emergent Swarm Agents. 319–326.
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6.
Brown, Daniel S., et al.. (2023). The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types. Proceedings of the AAAI Conference on Artificial Intelligence. 37(5). 5983–5992. 9 indexed citations
7.
Mattson, Connor, J CLARK, & Daniel S. Brown. (2023). Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots. 15. 163–169. 1 indexed citations
8.
Li, Zhongyu, et al.. (2022). Teaching Robots to Span the Space of Functional Expressive Motion. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 13406–13413. 4 indexed citations
9.
Brown, Daniel S., et al.. (2020). Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. International Conference on Machine Learning. 1. 1165–1177. 3 indexed citations
10.
Danielczuk, Michael, Ashwin Balakrishna, Daniel S. Brown, & Ken Goldberg. (2020). Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. 377–393. 2 indexed citations
11.
Brown, Daniel S., et al.. (2019). Ranking-Based Reward Extrapolation without Rankings.. arXiv (Cornell University). 1 indexed citations
12.
Brown, Daniel S., Yuchen Cui, & Scott Niekum. (2018). Risk-Aware Active Inverse Reinforcement Learning. 362–372. 6 indexed citations
13.
Brown, Daniel S. & Scott Niekum. (2017). Toward Probabilistic Safety Bounds for Robot Learning from Demonstration.. National Conference on Artificial Intelligence. 10–18. 1 indexed citations
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Berger, Matthew, Lee M. Seversky, & Daniel S. Brown. (2016). Classifying swarm behavior via compressive subspace learning. 5328–5335. 8 indexed citations
16.
Brown, Daniel S., et al.. (2015). Algorithms for Stochastic Physical Search on General Graphs. National Conference on Artificial Intelligence. 1 indexed citations
17.
Brown, Daniel S. & Michael A. Goodrich. (2014). Limited bandwidth recognition of collective behaviors in bio-inspired swarms. Adaptive Agents and Multi-Agents Systems. 405–412. 19 indexed citations
18.
Brown, Daniel S.. (2013). Toward Scalable Human Interaction with Bio-Inspired Robot Teams. International Journal of Cancer. 133(9). 2113–22. 2 indexed citations
19.
Özcan, Alpay, Eftychios G. Christoforou, Daniel S. Brown, & Nikolaos V. Tsekos. (2006). Fast and Efficient Radiological Interventions via a Graphical User Interface Commanded Magnetic Resonance Compatible Robotic Device. PubMed. 2006. 1762–1767. 7 indexed citations
20.
Brown, Daniel S. & Francisco J. Valero‐Cuevas. (2006). Task instability reduces maximum voluntary forces in dynamic multi-fingered grasp. Journal of Biomechanics. 39. S95–S95. 1 indexed citations

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.

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