Colby Banbury
- Automotive Engineering top 10%
- Artificial Intelligence
- Mechanical Engineering
- Biomedical Engineering
- Electrical and Electronic Engineering
- Co-authors
- Michael E. MackayDavid D. PhanDavid A. EdwardsVijay Janapa ReddiPete WardenGuido de CroonSrivatsan KrishnanAleksandra Faust
- Topics
- Neural Networks and Applications (3 papers)Advanced Memory and Neural Computing (3 papers)CCD and CMOS Imaging Sensors (2 papers)
- Partner nations
- United StatesNetherlandsUnited Kingdom
In The Last Decade
Colby Banbury
10 papers receiving 172 citations
Peers
Comparison fields: 5 of 49
- Automotive Engineering 73
- Artificial Intelligence 46
- Mechanical Engineering 45
- Biomedical Engineering 42
- Electrical and Electronic Engineering 25
Countries citing papers authored by Colby Banbury
This map shows the geographic impact of Colby Banbury'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 Colby Banbury with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Colby Banbury more than expected).
Fields of papers citing papers by Colby Banbury
This network shows the impact of papers produced by Colby Banbury. 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 Colby Banbury. The network helps show where Colby Banbury may publish in the future.
Co-authorship network of co-authors of Colby Banbury
This figure shows the co-authorship network connecting the top 25 collaborators of Colby Banbury. A scholar is included among the top collaborators of Colby Banbury 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 Colby Banbury. Colby Banbury 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 | 14 | |
| 4 | 2 | |
| 5 | 23 | |
| 6 | Multilingual Spoken Words Corpus | 7 |
| 7 | 20 | |
| 8 | 26 | |
| 9 | MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers | 6 |
| 10 | Learning to Seek: Deep Reinforcement Learning for Phototaxis of a Nano Drone in an Obstacle Field | 4 |
| 11 | 4 | |
| 12 | 74 |
About Colby Banbury
Colby Banbury is a scholar working on Signal Processing, Automotive Engineering and Artificial Intelligence, having authored 12 papers that have together received 180 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Advanced Memory and Neural Computing (3 papers) and CCD and CMOS Imaging Sensors (2 papers). The work is most often cited by research in Automotive Engineering (73 citations), Industrial and Manufacturing Engineering (24 citations) and Artificial Intelligence (46 citations). Colby Banbury has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Michael E. Mackay, David D. Phan, David A. Edwards, Vijay Janapa Reddi, Pete Warden, Guido de Croon, Srivatsan Krishnan, Aleksandra Faust, Brian Plancher and Jonathan Cruz. Their work appears in journals such as Communications of the ACM, Journal of Rheology and Internet of Things.
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.