Mark Silberstein
- Hardware and Architecture top 0.5%
- Parallel Computing and Optimization Techniques 33
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- Advanced Data Storage Technologies 28
- Distributed and Parallel Computing Systems 18
- Caching and Content Delivery 9
- Software-Defined Networks and 5G 9
- Information Systems top 1%
- Cloud Computing and Resource Management 16
- Signal Processing top 2%
- Advanced Malware Detection Techniques 9
- Artificial Intelligence top 1%
- Security and Verification in Computing 13
Mark Silberstein
62 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Hardware and Architecture 797
- Computer Networks and Communications 1.1k
- Information Systems 775
- Signal Processing 359
- Artificial Intelligence 860
Countries citing papers authored by Mark Silberstein
This map shows the geographic impact of Mark Silberstein'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 Mark Silberstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Silberstein more than expected).
Fields of papers citing papers by Mark Silberstein
This network shows the impact of papers produced by Mark Silberstein. 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 Mark Silberstein. The network helps show where Mark Silberstein may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mark Silberstein, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 0 | |
| 5 | 2023 | 0 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 16 | |
| 9 | 2023 | 6 | |
| 10 | 2022 | 9 | |
| 11 | 2022 | 2 | |
| 12 | Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism. | 2021 | 5 |
| 13 | 2021 | 4 | |
| 14 | {NICA}: An Infrastructure for Inline Acceleration of Network Applications | 2019 | 23 |
| 15 | 2018 | 4 | |
| 16 | 2016 | 25 | |
| 17 | 2014 | 63 | |
| 18 | 2013 | 1 | |
| 19 | Materializing Highly Available Grids. | 2006 | 1 |
| 20 | 2006 | 66 |
About Mark Silberstein
Mark Silberstein is a scholar working on Hardware and Architecture, Computer Networks and Communications, Information Systems, Signal Processing and Artificial Intelligence, having authored 69 papers that have together received 2.0k indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (33 papers), Advanced Data Storage Technologies (28 papers), Distributed and Parallel Computing Systems (18 papers), Cloud Computing and Resource Management (16 papers), Security and Verification in Computing (13 papers), Caching and Content Delivery (9 papers), Software-Defined Networks and 5G (9 papers) and Advanced Malware Detection Techniques (9 papers). The work is most often cited by research in Hardware and Architecture (797 citations), Computer Networks and Communications (1.1k citations), Information Systems (775 citations), Signal Processing (359 citations) and Artificial Intelligence (860 citations). Mark Silberstein has collaborated with scholars based in Israel, United States and United Kingdom. Frequent co-authors include Emmett Witchel, Marina Minkin, Assaf Schuster, Raoul Strackx, Baris Kasikci, Thomas F. Wenisch, Frank Piessens, Jo Van Bulck, Ofir Weisse and Daniel Genkin. Their work appears in journals such as ACM Transactions on Computer Systems, Bioinformatics, IEEE Micro, IEEE/ACM Transactions on Networking and ACM Transactions on Storage.
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.