Farshid Ashtiani

1.1k citations
25 papers · 641 · 1 hit paper · h-index 6

Impact in

Papers in

Farshid Ashtiani

19 papers receiving 581 citations

Farshid Ashtiani's Hit Papers

An on-chip photonic deep neural network for image classification 2022 · 447 citations
4470+1+2Years since publication100200300400

Peers

Farshid Ashtiani
Comparison fields: 5 of 50
  • Acoustics and Ultrasonics 17
  • Artificial Intelligence 406
  • Electrical and Electronic Engineering 503
  • Instrumentation 15
  • Atomic and Molecular Physics, and Optics 122
Replace Alexander J. Geers with:
Alexander J. Geers United States
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Bohan Li United States
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Ying Zuo China
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Farshid Ashtiani relative to Alexander J. Geers United States Alexander J. Geers's profile →
Citations per field
00.5×1.5×2.5×
Alexander J. Geers · 1×
Citations per year

Countries citing papers authored by Farshid Ashtiani

Since Specialization
Citations

This map shows the geographic impact of Farshid Ashtiani'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 Farshid Ashtiani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Farshid Ashtiani more than expected).

Fields of papers citing papers by Farshid Ashtiani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Farshid Ashtiani. 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 Farshid Ashtiani. The network helps show where Farshid Ashtiani may publish in the future.

Co-authors

The 25 scholars most cited alongside Farshid Ashtiani, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Farshid Ashtiani Line = papers co-authored together Farshid Ashtiani links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1
An on-chip photonic deep neural network for image classification
Hit paper breakdown →
2022447
2 201964
3 202452
4 202427
5 201716
6 20188
7 20195
8 20254
9 20234
10 20182
11 20232
12 20241
13 20231
14 20211
15 20201
16 20241
17 20251
18 20231
19 20241
20 20211

About Farshid Ashtiani

Farshid Ashtiani is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Atomic and Molecular Physics, and Optics, Biomedical Engineering and Instrumentation, having authored 25 papers that have together received 641 indexed citations. Recurring topics across this work include Photonic and Optical Devices (19 papers), Neural Networks and Reservoir Computing (13 papers), Optical Network Technologies (9 papers), Advanced Fiber Laser Technologies (7 papers), Advanced Photonic Communication Systems (5 papers), Optical Coherence Tomography Applications (2 papers), Topological Materials and Phenomena (2 papers) and Photonic Crystals and Applications (2 papers). The work is most often cited by research in Acoustics and Ultrasonics (17 citations), Artificial Intelligence (406 citations), Electrical and Electronic Engineering (503 citations), Instrumentation (15 citations) and Atomic and Molecular Physics, and Optics (122 citations). Farshid Ashtiani has collaborated with scholars based in United States, Finland and Canada. Frequent co-authors include Firooz Aflatouni, Alexander J. Geers, Brian Edwards, Nader Engheta, Vahid Nikkhah, S. J. Ben Yoo, Andrea Blanco‐Redondo, Daniel Pérez, Kwangwoong Kim and John E. Bowers. Their work appears in journals such as Optics Express, Nature, ACS Photonics, Nature Communications and Optica.

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.

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