Nikhil Garg
- Artificial Intelligence top 10%
- Neural Networks and Reservoir Computing 4
- Topic Modeling 3
- Natural Language Processing Techniques 3
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- Neural dynamics and brain function 5
- EEG and Brain-Computer Interfaces 4
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- Advanced Memory and Neural Computing 8
- Advanced Optical Network Technologies 3
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- Neuroscience and Neural Engineering 5
- Co-authors
- Benoît FavreJames HendersonYasha HasijaVeeky BathsFabien AlibartDominique DrouinYuan ZhangLoris Bazzani
- Journals
- Nature Communications (1 paper)SHILAP Revista de lepidopterología (1 paper)Advanced Functional Materials (1 paper)
- Partner nations
- FranceCanadaUnited States
In The Last Decade
Nikhil Garg
22 papers receiving 192 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 100
- Cognitive Neuroscience 37
- Health Information Management 8
- Computer Vision and Pattern Recognition 34
- Human-Computer Interaction 6
Countries citing papers authored by Nikhil Garg
This map shows the geographic impact of Nikhil Garg'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 Nikhil Garg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nikhil Garg more than expected).
Fields of papers citing papers by Nikhil Garg
This network shows the impact of papers produced by Nikhil Garg. 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 Nikhil Garg. The network helps show where Nikhil Garg may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Nikhil Garg, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 3 | |
| 2 | 2024 | 6 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 7 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 13 | |
| 7 | 2022 | 12 | |
| 8 | 2022 | 8 | |
| 9 | 2022 | 5 | |
| 10 | 2021 | 21 | |
| 11 | 2021 | 1 | |
| 12 | 2021 | 5 | |
| 13 | 2020 | 13 | |
| 14 | 2019 | 7 | |
| 15 | 2019 | 1 | |
| 16 | 2017 | 1 | |
| 17 | Unsupervised Semantic Role Induction with Global Role Ordering | 2012 | 8 |
| 18 | Temporal Restricted Boltzmann Machines for Dependency Parsing | 2011 | 9 |
| 19 | 2003 | 3 | |
| 20 | 2003 | 1 |
About Nikhil Garg
Nikhil Garg is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Cellular and Molecular Neuroscience, having authored 24 papers that have together received 206 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (8 papers), Neural dynamics and brain function (5 papers), Neuroscience and Neural Engineering (5 papers), Neural Networks and Reservoir Computing (4 papers), EEG and Brain-Computer Interfaces (4 papers), Topic Modeling (3 papers), Advanced Optical Network Technologies (3 papers) and Natural Language Processing Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (100 citations), Cognitive Neuroscience (37 citations) and Health Information Management (8 citations). Nikhil Garg has collaborated with scholars based in France, Canada and United States. Frequent co-authors include Benoît Favre, James Henderson, Yasha Hasija, Veeky Baths, Fabien Alibart, Dominique Drouin, Yuan Zhang, Loris Bazzani, Michael Donoser and Yann Beilliard. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Advanced Functional Materials.
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.