Akshay Vashist
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 10%
- Radiology, Nuclear Medicine and Imaging
- Media Technology top 10%
- Co-authors
- Vladimir VapnikRauf IzmailovDmitry PechyonyIlya MuchnikCasimir A. KulikowskiRitu ChadhaRoberto PagliariYitzchak M. Gottlieb
- Topics
- Anomaly Detection Techniques and Applications (4 papers)Machine Learning and Data Classification (3 papers)Network Security and Intrusion Detection (3 papers)
- Journals
- Neural NetworksInvestigative RadiologyIEEE/ACM Transactions on Computational Biology and Bioinformatics
- Partner nations
- United StatesJapan
In The Last Decade
Akshay Vashist
13 papers receiving 534 citations
Hit Papers
Peers
Comparison fields: 5 of 90
- Artificial Intelligence 323
- Computer Vision and Pattern Recognition 263
- Signal Processing 48
- Radiology, Nuclear Medicine and Imaging 36
- Media Technology 34
Countries citing papers authored by Akshay Vashist
This map shows the geographic impact of Akshay Vashist'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 Akshay Vashist with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akshay Vashist more than expected).
Fields of papers citing papers by Akshay Vashist
This network shows the impact of papers produced by Akshay Vashist. 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 Akshay Vashist. The network helps show where Akshay Vashist may publish in the future.
Co-authorship network of co-authors of Akshay Vashist
This figure shows the co-authorship network connecting the top 25 collaborators of Akshay Vashist. A scholar is included among the top collaborators of Akshay Vashist 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 Akshay Vashist. Akshay Vashist 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 | 6 | |
| 3 | 15 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 37 | |
| 9 | 2 | |
| 10 | A new learning paradigm: Learning using privileged informationbreakdown → | 436 |
| 11 | 29 | |
| 12 | 11 | |
| 13 | 2 | |
| 14 | Automatic screening for groups of orthologous genes in comparative genomics using multiple-component clustering | 1 |
About Akshay Vashist
Akshay Vashist is a scholar working on Medical Laboratory Technology, Artificial Intelligence and Computer Networks and Communications, having authored 14 papers that have together received 549 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (4 papers), Machine Learning and Data Classification (3 papers) and Network Security and Intrusion Detection (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (263 citations), Artificial Intelligence (323 citations) and Signal Processing (48 citations). Akshay Vashist has collaborated with scholars based in United States and Japan. Frequent co-authors include Vladimir Vapnik, Rauf Izmailov, Dmitry Pechyony, Ilya Muchnik, Casimir A. Kulikowski, Ritu Chadha, Casimir A. Kulikowski, Roberto Pagliari, Yitzchak M. Gottlieb and Konstantine Arkoudas. Their work appears in journals such as Neural Networks, Investigative Radiology and IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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.