Amir Khosrowshahi
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Cognitive Neuroscience
- Cellular and Molecular Neuroscience
- Co-authors
- Bruno A. OlshausenDmitri E. NikonovDenis KleykoFriedrich T. SommerArjun K. BansalMarcel NassarUrs KösterLuke Hornof
- Topics
- Advanced Memory and Neural Computing (3 papers)Neural dynamics and brain function (2 papers)Ferroelectric and Negative Capacitance Devices (2 papers)
- Journals
- Nature CommunicationsJournal of Neuroscience MethodsIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
- Partner nations
- United StatesSwedenNorway
In The Last Decade
Amir Khosrowshahi
6 papers receiving 140 citations
Peers
Comparison fields: 5 of 28
- Artificial Intelligence 72
- Computer Vision and Pattern Recognition 60
- Electrical and Electronic Engineering 50
- Cognitive Neuroscience 35
- Cellular and Molecular Neuroscience 25
Countries citing papers authored by Amir Khosrowshahi
This map shows the geographic impact of Amir Khosrowshahi'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 Amir Khosrowshahi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir Khosrowshahi more than expected).
Fields of papers citing papers by Amir Khosrowshahi
This network shows the impact of papers produced by Amir Khosrowshahi. 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 Amir Khosrowshahi. The network helps show where Amir Khosrowshahi may publish in the future.
Co-authorship network of co-authors of Amir Khosrowshahi
This figure shows the co-authorship network connecting the top 25 collaborators of Amir Khosrowshahi. A scholar is included among the top collaborators of Amir Khosrowshahi 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 Amir Khosrowshahi. Amir Khosrowshahi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 33 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks | 76 |
| 5 | Deep Learning for Climate Pattern Detection | 0 |
| 6 | 33 | |
| 7 | The laminar organization of V1 neural activity in response to dynamic natural scenes | 1 |
| 8 | Motion Synthesis for 3 Articulated Figures and Mass-Spring Models | 1 |
About Amir Khosrowshahi
Amir Khosrowshahi is a scholar working on Computational Mathematics, Cognitive Neuroscience and Artificial Intelligence, having authored 8 papers that have together received 146 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (3 papers), Neural dynamics and brain function (2 papers) and Ferroelectric and Negative Capacitance Devices (2 papers). The work is most often cited by research in Computational Mathematics (7 citations), Hardware and Architecture (23 citations) and Computer Vision and Pattern Recognition (60 citations). Amir Khosrowshahi has collaborated with scholars based in United States, Sweden and Norway. Frequent co-authors include Bruno A. Olshausen, Dmitri E. Nikonov, Denis Kleyko, Friedrich T. Sommer, Arjun K. Bansal, Marcel Nassar, Urs Köster, Luke Hornof, Xin Wang and Oğuz H. Elibol. Their work appears in journals such as Nature Communications, Journal of Neuroscience Methods and IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.
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.