Matthew Luciw
- Cognitive Neuroscience top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Control and Systems Engineering top 10%
- Co-authors
- Juyang WengEwa JarockaVarun Raj KompellaBenoni B. EdinJürgen SchmidhuberJuergen SchmidhuberMarijn StollengaAlexander Förster
- Topics
- Neural dynamics and brain function (18 papers)Reinforcement Learning in Robotics (14 papers)Neural Networks and Applications (8 papers)
- Journals
- SHILAP Revista de lepidopterologíaFrontiers in PsychologyArtificial Intelligence
- Partner nations
- SwitzerlandUnited StatesGermany
In The Last Decade
Matthew Luciw
36 papers receiving 468 citations
Peers
Comparison fields: 5 of 60
- Cognitive Neuroscience 256
- Artificial Intelligence 207
- Computer Vision and Pattern Recognition 94
- Electrical and Electronic Engineering 86
- Control and Systems Engineering 73
Countries citing papers authored by Matthew Luciw
This map shows the geographic impact of Matthew Luciw'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 Matthew Luciw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Luciw more than expected).
Fields of papers citing papers by Matthew Luciw
This network shows the impact of papers produced by Matthew Luciw. 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 Matthew Luciw. The network helps show where Matthew Luciw may publish in the future.
Co-authorship network of co-authors of Matthew Luciw
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Luciw. A scholar is included among the top collaborators of Matthew Luciw 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 Matthew Luciw. Matthew Luciw is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 34 | |
| 2 | 6 | |
| 3 | 88 | |
| 4 | 9 | |
| 5 | Teleoperation of a 7 DOF humanoid robot arm using human arm accelerations and EMG signals | 6 |
| 6 | Upper confidence weighted learning for efficient exploration in multiclass prediction with binary feedback | 3 |
| 7 | 20 | |
| 8 | 9 | |
| 9 | 9 | |
| 10 | 2 | |
| 11 | 21 | |
| 12 | 30 | |
| 13 | 8 | |
| 14 | 12 | |
| 15 | 16 | |
| 16 | 10 | |
| 17 | 25 | |
| 18 | 71 | |
| 19 | 0 | |
| 20 | 13 |
About Matthew Luciw
Matthew Luciw is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Signal Processing, having authored 37 papers that have together received 493 indexed citations. Recurring topics across this work include Neural dynamics and brain function (18 papers), Reinforcement Learning in Robotics (14 papers) and Neural Networks and Applications (8 papers). The work is most often cited by research in Cognitive Neuroscience (256 citations), Artificial Intelligence (207 citations) and Computer Vision and Pattern Recognition (94 citations). Matthew Luciw has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Juyang Weng, Ewa Jarocka, Varun Raj Kompella, Benoni B. Edin, Jürgen Schmidhuber, Juergen Schmidhuber, Marijn Stollenga, Alexander Förster, Hung Q. Ngo and Giuseppe Cuccu. Their work appears in journals such as SHILAP Revista de lepidopterología, Frontiers in Psychology and 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.