Marcel Nassar

1.5k citations
29 papers · 881 · h-index 15

Impact in

Papers in

Marcel Nassar

28 papers receiving 852 citations

Peers

Marcel Nassar
Comparison fields: 5 of 64
  • Computational Mathematics 8
  • Electrical and Electronic Engineering 697
  • Signal Processing 75
  • Computer Networks and Communications 123
  • Hardware and Architecture 36
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Wenbo Xu China
M. Speth Germany
André Noll Barreto Germany
Atílio Gameiro Portugal
Hideichi Sasaoka Japan
Joëlle Skaf United States
Victor B. Lawrence United States
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Thomas W. Rondeau United States
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Citations per year

Countries citing papers authored by Marcel Nassar

Since Specialization
Citations

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

Fields of papers citing papers by Marcel Nassar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Marcel Nassar, 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 Marcel Nassar Line = papers co-authored together Marcel Nassar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013188
2 2012144
3 201286
4
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
201776
5 201454
6 201145
7 200839
8 201333
9 200933
10 202024
11 202224
12 201118
13 201118
14 201816
15 201316
16 201112
17 202011
18 20239
19 20088
20 20256

About Marcel Nassar

Marcel Nassar is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Computer Networks and Communications, Computational Theory and Mathematics and Molecular Biology, having authored 29 papers that have together received 881 indexed citations. Recurring topics across this work include Power Line Communications and Noise (13 papers), Electromagnetic Compatibility and Noise Suppression (10 papers), Advanced Wireless Communication Techniques (5 papers), Advanced Graph Neural Networks (4 papers), Millimeter-Wave Propagation and Modeling (4 papers), Computational Drug Discovery Methods (3 papers), Machine Learning in Materials Science (3 papers) and Wireless Communication Networks Research (3 papers). The work is most often cited by research in Computational Mathematics (8 citations), Electrical and Electronic Engineering (697 citations), Signal Processing (75 citations), Computer Networks and Communications (123 citations) and Hardware and Architecture (36 citations). Marcel Nassar has collaborated with scholars based in United States, Lebanon and Egypt. Frequent co-authors include Brian L. Evans, Jing Lin, Anand G. Dabak, Il Kim, Kapil Gulati, Philip Schniter, Il Han Kim, Yousef Mortazavi, Oğuz H. Elibol and Karl Nieman. Their work appears in journals such as IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, npj Computational Materials, IEEE Journal on Selected Areas in Communications, IEEE Signal Processing Magazine and IEEE Transactions on Biomedical Engineering.

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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