Sherdil Niyaz
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Biomedical Engineering
- Mechanical Engineering
- Aerospace Engineering
- Co-authors
- Ken GoldbergFlorian T. PokornyJeffrey MahlerOren SalzmanRon AlterovitzSiddhartha S SrinivasaAlan KuntzZiang Liu
- Topics
- Topological and Geometric Data Analysis (2 papers)Robotics and Sensor-Based Localization (2 papers)Cell Image Analysis Techniques (2 papers)
- Cited by
- Control and Systems EngineeringComputer Vision and Pattern RecognitionAerospace Engineering
- Journals
- IEEE Transactions on Automation Science and EngineeringPubMed2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Partner nations
- United StatesSweden
In The Last Decade
Sherdil Niyaz
5 papers receiving 36 citations
Peers
Comparison fields: 5 of 23
- Control and Systems Engineering 22
- Computer Vision and Pattern Recognition 14
- Biomedical Engineering 12
- Mechanical Engineering 8
- Aerospace Engineering 7
Countries citing papers authored by Sherdil Niyaz
This map shows the geographic impact of Sherdil Niyaz'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 Sherdil Niyaz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sherdil Niyaz more than expected).
Fields of papers citing papers by Sherdil Niyaz
This network shows the impact of papers produced by Sherdil Niyaz. 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 Sherdil Niyaz. The network helps show where Sherdil Niyaz may publish in the future.
Co-authorship network of co-authors of Sherdil Niyaz
This figure shows the co-authorship network connecting the top 25 collaborators of Sherdil Niyaz. A scholar is included among the top collaborators of Sherdil Niyaz 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 Sherdil Niyaz. Sherdil Niyaz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 1 | |
| 3 | 8 | |
| 4 | 13 | |
| 5 | 7 |
About Sherdil Niyaz
Sherdil Niyaz is a scholar working on Biophysics, Human-Computer Interaction and Computer Vision and Pattern Recognition, having authored 5 papers that have together received 36 indexed citations. Recurring topics across this work include Topological and Geometric Data Analysis (2 papers), Robotics and Sensor-Based Localization (2 papers) and Cell Image Analysis Techniques (2 papers). The work is most often cited by research in Control and Systems Engineering (22 citations), Computer Vision and Pattern Recognition (14 citations) and Aerospace Engineering (7 citations). Sherdil Niyaz has collaborated with scholars based in United States and Sweden. Frequent co-authors include Ken Goldberg, Florian T. Pokorny, Jeffrey Mahler, Oren Salzman, Ron Alterovitz, Siddhartha S Srinivasa, Alan Kuntz, Ziang Liu, Barath Raghavan and Stefanos Nikolaidis. Their work appears in journals such as IEEE Transactions on Automation Science and Engineering, PubMed and 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.