Nicholas R. Waytowich

1.1k total citations
44 papers, 710 citations indexed

About

Nicholas R. Waytowich is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nicholas R. Waytowich has authored 44 papers receiving a total of 710 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 15 papers in Cognitive Neuroscience and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nicholas R. Waytowich's work include Reinforcement Learning in Robotics (17 papers), EEG and Brain-Computer Interfaces (14 papers) and Neural dynamics and brain function (11 papers). Nicholas R. Waytowich is often cited by papers focused on Reinforcement Learning in Robotics (17 papers), EEG and Brain-Computer Interfaces (14 papers) and Neural dynamics and brain function (11 papers). Nicholas R. Waytowich collaborates with scholars based in United States, Russia and China. Nicholas R. Waytowich's co-authors include Dean J. Krusienski, Vernon J. Lawhern, Paul Sajda, Josef Faller, Javier O. Garcia, Jean M. Vettel, Jennifer Cummings, Tinoosh Mohsenin, Xingyu Wang and Haiqiang Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Signal Processing and Computer.

In The Last Decade

Nicholas R. Waytowich

43 papers receiving 696 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nicholas R. Waytowich United States 14 457 218 194 151 122 44 710
Young-Eun Lee South Korea 12 496 1.1× 197 0.9× 149 0.8× 73 0.5× 118 1.0× 28 683
Yonghao Song China 14 566 1.2× 155 0.7× 130 0.7× 186 1.2× 122 1.0× 29 797
Dong-Ok Won South Korea 12 487 1.1× 190 0.9× 97 0.5× 140 0.9× 82 0.7× 46 751
Shiu Kumar Fiji 14 462 1.0× 211 1.0× 233 1.2× 63 0.4× 164 1.3× 27 691
Vernon J. Lawhern United States 17 811 1.8× 231 1.1× 205 1.1× 134 0.9× 219 1.8× 40 999
Saugat Bhattacharyya India 16 637 1.4× 310 1.4× 211 1.1× 125 0.8× 126 1.0× 58 1.0k
Bingchuan Liu China 9 694 1.5× 258 1.2× 198 1.0× 98 0.6× 162 1.3× 15 805
Pramod Gaur India 13 729 1.6× 295 1.4× 183 0.9× 111 0.7× 244 2.0× 16 900
Yifan Xu China 13 342 0.7× 79 0.4× 128 0.7× 207 1.4× 61 0.5× 42 748

Countries citing papers authored by Nicholas R. Waytowich

Since Specialization
Citations

This map shows the geographic impact of Nicholas R. Waytowich'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 Nicholas R. Waytowich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas R. Waytowich more than expected).

Fields of papers citing papers by Nicholas R. Waytowich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicholas R. Waytowich. 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 Nicholas R. Waytowich. The network helps show where Nicholas R. Waytowich may publish in the future.

Co-authorship network of co-authors of Nicholas R. Waytowich

This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas R. Waytowich. A scholar is included among the top collaborators of Nicholas R. Waytowich 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 Nicholas R. Waytowich. Nicholas R. Waytowich 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.
Jayarajah, Kasthuri, et al.. (2025). Augmenting Personalized Memory via Practical Multimodal Wearable Sensing in Visual Search and Wayfinding Navigation. Maryland Shared Open Access Repository (USMAI Consortium). 11–21. 1 indexed citations
2.
Wu, MingKang, et al.. (2024). Rating-Based Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(9). 10207–10215. 3 indexed citations
3.
Waytowich, Nicholas R., et al.. (2022). On games and simulators as a platform for development of artificial intelligence for command and control. The Journal of Defense Modeling and Simulation Applications Methodology Technology. 20(4). 495–508. 17 indexed citations
4.
Ramamurthy, Sreenivasan Ramasamy, et al.. (2022). PerMTL: A Multi-Task Learning Framework for Skilled Human Performance Assessment. Maryland Shared Open Access Repository (USMAI Consortium). 37–44. 2 indexed citations
5.
Corder, J. Kevin, et al.. (2022). Utility of doctrine with multi-agent RL for military engagements. 81–81. 2 indexed citations
6.
7.
Allen, Peter K., et al.. (2022). Mobile Manipulation Leveraging Multiple Views. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4585–4592. 3 indexed citations
8.
Warnell, Garrett, et al.. (2021). Improving Autonomous Robotic Navigation Using Imitation Learning. Frontiers in Robotics and AI. 8. 627730–627730. 11 indexed citations
9.
Mazumder, Arnab Neelim, et al.. (2021). A Hardware Accelerator for Language-Guided Reinforcement Learning. IEEE Design and Test. 39(3). 37–44. 9 indexed citations
10.
Hosseini, Morteza, et al.. (2021). An Energy Efficient EdgeAI Autoencoder Accelerator for Reinforcement Learning. SHILAP Revista de lepidopterología. 2. 182–195. 10 indexed citations
11.
Gremillion, Gregory M., et al.. (2020). Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments. Adaptive Agents and Multi-Agents Systems. 465–473. 22 indexed citations
12.
Waytowich, Nicholas R., et al.. (2020). Guiding Safe Reinforcement Learning Policies Using Structured Language Constraints. Maryland Shared Open Access Repository (USMAI Consortium). 153–161. 10 indexed citations
13.
Gandhi, Sunil, Tim Oates, Tinoosh Mohsenin, & Nicholas R. Waytowich. (2019). Learning Behaviors from a Single Video Demonstration Using Human Feedback. Adaptive Agents and Multi-Agents Systems. 1970–1972. 4 indexed citations
14.
Faller, Josef, et al.. (2019). Investigating Evoked EEG Responses to Targets Presented in Virtual Reality. PubMed. 118. 5536–5539. 3 indexed citations
15.
Hosseini, Morteza, et al.. (2018). Energy Efficient Convolutional Neural Networks for EEG Artifact Detection. Maryland Shared Open Access Repository (USMAI Consortium). 1–4. 19 indexed citations
16.
Waytowich, Nicholas R., Vernon J. Lawhern, Javier O. Garcia, et al.. (2018). Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials. Journal of Neural Engineering. 15(6). 66031–66031. 167 indexed citations
17.
Waytowich, Nicholas R., Yusuke Yamani, & Dean J. Krusienski. (2016). Optimization of Checkerboard Spatial Frequencies for Steady-State Visual Evoked Potential Brain–Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25(6). 557–565. 36 indexed citations
18.
Waytowich, Nicholas R. & Dean J. Krusienski. (2015). Spatial decoupling of targets and flashing stimuli for visual brain–computer interfaces. Journal of Neural Engineering. 12(3). 36006–36006. 16 indexed citations
19.
Waytowich, Nicholas R., et al.. (2010). Robot application of a brain computer interface to staubli TX40 robots - early stages. World Automation Congress. 1–6. 14 indexed citations
20.
Waytowich, Nicholas R., et al.. (2010). Extending the discrete selection capabilities of the P300 speller to goal-oriented robotic arm control. 572–575. 6 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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