Lalit Gupta
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Cognitive Neuroscience top 10%
- Human-Computer Interaction top 2%
- Co-authors
- M SrinathDennis L. MolfeseRavi VaidyanathanPanagiotis G. SimosSrinivas KotaMark P. McAvoyKyle PerkinsSukhendu Das
- Topics
- Neural Networks and Applications (18 papers)EEG and Brain-Computer Interfaces (17 papers)Image Retrieval and Classification Techniques (11 papers)
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
Lalit Gupta
75 papers receiving 1000 citations
Peers
Comparison fields: 5 of 124
- Computer Vision and Pattern Recognition 337
- Artificial Intelligence 280
- Signal Processing 223
- Cognitive Neuroscience 211
- Human-Computer Interaction 181
Countries citing papers authored by Lalit Gupta
This map shows the geographic impact of Lalit Gupta'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 Lalit Gupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lalit Gupta more than expected).
Fields of papers citing papers by Lalit Gupta
This network shows the impact of papers produced by Lalit Gupta. 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 Lalit Gupta. The network helps show where Lalit Gupta may publish in the future.
Co-authorship network of co-authors of Lalit Gupta
This figure shows the co-authorship network connecting the top 25 collaborators of Lalit Gupta. A scholar is included among the top collaborators of Lalit Gupta 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 Lalit Gupta. Lalit Gupta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 6 | |
| 3 | 11 | |
| 4 | 2 | |
| 5 | 15 | |
| 6 | 6 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | PrimerDB: a synthetic database for primer/ oligonucleotide hybridization and efficiency prediction | 0 |
| 10 | 10 | |
| 11 | 4 | |
| 12 | 5 | |
| 13 | 3 | |
| 14 | 38 | |
| 15 | 1 | |
| 16 | 13 | |
| 17 | 78 | |
| 18 | 120 | |
| 19 | 12 | |
| 20 | 24 |
About Lalit Gupta
Lalit Gupta is a scholar working on Architecture, Human-Computer Interaction and Signal Processing, having authored 78 papers that have together received 1.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (18 papers), EEG and Brain-Computer Interfaces (17 papers) and Image Retrieval and Classification Techniques (11 papers). The work is most often cited by research in Human-Computer Interaction (181 citations), Signal Processing (223 citations) and Computer Vision and Pattern Recognition (337 citations). Lalit Gupta has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include M Srinath, Dennis L. Molfese, Ravi Vaidyanathan, Panagiotis G. Simos, Srinivas Kota, Mark P. McAvoy, Kyle Perkins, Sukhendu Das, Jitendra Paliwal and Ruplal Choudhary. Their work appears in journals such as Expert Systems with Applications, IEEE Access and IEEE Transactions on Biomedical Engineering.
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.