Adel Javanmard

2.1k citations
33 papers · 1.0k · h-index 13

Impact in

Papers in

Adel Javanmard

30 papers receiving 999 citations

Peers

Adel Javanmard
Comparison fields: 5 of 109
  • Statistics and Probability 341
  • Computer Networks and Communications 347
  • Computational Mathematics 7
  • Signal Processing 119
  • Computational Mechanics 219
Replace Shuheng Zhou with:
Shuheng Zhou United States
Romain Couillet France
Aarti Singh United States
Don Hush United States
Daniel M. Kane United States
Ding Zhou China
Peter Richtárik United Kingdom
Colin McDiarmid United Kingdom
Tim van Erven Netherlands
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Citations per field
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Citations per year

Countries citing papers authored by Adel Javanmard

Since Specialization
Citations

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

Fields of papers citing papers by Adel Javanmard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013355
2 2013122
3 201198
4 200892
5 201257
6 201654
7 201237
8
Learning Linear Bayesian Networks with Latent Variables
201231
9 201917
10 201215
11 201514
12 201012
13 201812
14 201112
15 200912
16 202210
17 201110
18 201210
19 20239
20
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
20129

About Adel Javanmard

Adel Javanmard is a scholar working on Computational Mechanics, Computer Networks and Communications, Artificial Intelligence, Statistics and Probability and Signal Processing, having authored 33 papers that have together received 1.0k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (10 papers), Advanced Bandit Algorithms Research (4 papers), Statistical Methods and Inference (4 papers), Distributed Sensor Networks and Detection Algorithms (3 papers), Machine Learning and Algorithms (3 papers), Bayesian Modeling and Causal Inference (3 papers), Blind Source Separation Techniques (3 papers) and Robotics and Sensor-Based Localization (2 papers). The work is most often cited by research in Statistics and Probability (341 citations), Computer Networks and Communications (347 citations), Computational Mathematics (7 citations), Signal Processing (119 citations) and Computational Mechanics (219 citations). Adel Javanmard has collaborated with scholars based in United States, Iran and Canada. Frequent co-authors include Andrea Montanari, David L. Donoho, Mohammad Alizadeh, Balaji Prabhakar, Farid Ashtiani, Mohamed Hamdi, Federico Ricci‐Tersenghi, Sham M. Kakade, Daniel Hsu and Animashree Anandkumar. Their work appears in journals such as ACM SIGMETRICS Performance Evaluation Review, Bioinformatics, The Annals of Statistics, Foundations of Computational Mathematics and Computer Communications.

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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