Massimo Giordano
- Electrical and Electronic Engineering top 5%
- Cellular and Molecular Neuroscience top 10%
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Materials Chemistry
- Co-authors
- Stefano AmbrogioPritish NarayananGeoffrey W. BurrHsinyu TsaiR. M. ShelbyBenjamin D. KilleenSeverin SidlerCarmelo di Nolfo
- Topics
- Advanced Memory and Neural Computing (10 papers)Ferroelectric and Negative Capacitance Devices (9 papers)Advanced Neural Network Applications (4 papers)
- Cited by
- Electrical and Electronic EngineeringCellular and Molecular NeuroscienceArtificial Intelligence
- Partner nations
- United StatesSwitzerlandTaiwan
In The Last Decade
Massimo Giordano
11 papers receiving 939 citations
Hit Papers
Peers
Comparison fields: 5 of 44
- Electrical and Electronic Engineering 911
- Cellular and Molecular Neuroscience 251
- Artificial Intelligence 237
- Cognitive Neuroscience 125
- Materials Chemistry 116
Countries citing papers authored by Massimo Giordano
This map shows the geographic impact of Massimo Giordano'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 Massimo Giordano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Massimo Giordano more than expected).
Fields of papers citing papers by Massimo Giordano
This network shows the impact of papers produced by Massimo Giordano. 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 Massimo Giordano. The network helps show where Massimo Giordano may publish in the future.
Co-authorship network of co-authors of Massimo Giordano
This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Giordano. A scholar is included among the top collaborators of Massimo Giordano 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 Massimo Giordano. Massimo Giordano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 2 | |
| 4 | 39 | |
| 5 | 35 | |
| 6 | 8 | |
| 7 | 21 | |
| 8 | 39 | |
| 9 | Equivalent-accuracy accelerated neural-network training using analogue memorybreakdown → | 770 |
| 10 | 28 | |
| 11 | 13 |
About Massimo Giordano
Massimo Giordano is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Hardware and Architecture, having authored 11 papers that have together received 959 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (10 papers), Ferroelectric and Negative Capacitance Devices (9 papers) and Advanced Neural Network Applications (4 papers). The work is most often cited by research in Electrical and Electronic Engineering (911 citations), Cellular and Molecular Neuroscience (251 citations) and Artificial Intelligence (237 citations). Massimo Giordano has collaborated with scholars based in United States, Switzerland and Taiwan. Frequent co-authors include Stefano Ambrogio, Pritish Narayanan, Geoffrey W. Burr, Hsinyu Tsai, R. M. Shelby, Benjamin D. Killeen, Severin Sidler, Carmelo di Nolfo, Irem Boybat and Robert M. Radway. Their work appears in journals such as Nature, Journal of Applied Physics and IEEE Journal of Solid-State 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.