Justin Dauwels

10.4k citations
267 papers · 7.0k indexed · 1 hit paper · h-index 40

Justin Dauwels

252 papers receiving 6.8k citations

Hit Papers

Steady-state visually evoked potentials: Focus on essenti...7882009202620142020250500750

Peers

Justin Dauwels
Comparison fields: 5 of 181
  • Computational Mathematics 102
  • Cognitive Neuroscience 3.3k
  • Signal Processing 887
  • Transportation 446
  • Psychiatry and Mental health 793
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Bin Hu China
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Tianming Liu United States
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Citations per field
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Citations per year

Countries citing papers authored by Justin Dauwels

Since Specialization
Citations

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

Fields of papers citing papers by Justin Dauwels

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20240
2 20240
3 20231
4 202214
5 202214
6 202230
7 202214
8 202167
9
Fast Bayesian inference of Sparse Networks with automatic sparsity determination
20202
10
Five-institution study of automated classification of pathological slowing from adult scalp electroencephalograms
20201
11 2019135
12
Improved Tabu Search Heuristics for Static Dial-A-Ride Problem: Faster and Better Convergence.
20181
13 20132
14 20127
15
Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals
20091
16
The Factor Graph Approach to Model-Based Signal Processing Factor graphs can model complex systems and help to design effective algorithms for detection and estimation problems.
20071
17
ON THE CONVERGENCE OF ITERATIVE ESTIMATION ALGORITHMS OPERATING ON GRAPHICAL MODELS
20072
18
A General Computation Rule for Lossy Summaries/Messages with Examples from Equalization
200620
19
Computation of the capacity of continuous memoryless channels and the rate distortion function of memoryless continuous sources
20061
20 200519

About Justin Dauwels

Justin Dauwels is a scholar working on Computational Mathematics, Transportation and Cognitive Neuroscience, having authored 267 papers that have together received 7.0k indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (62 papers), Neural dynamics and brain function (37 papers), Traffic Prediction and Management Techniques (31 papers), Blind Source Separation Techniques (26 papers), Transportation Planning and Optimization (26 papers), Functional Brain Connectivity Studies (25 papers), Epilepsy research and treatment (17 papers) and Traffic control and management (17 papers). The work is most often cited by research in Computational Mathematics (102 citations), Cognitive Neuroscience (3.3k citations) and Signal Processing (887 citations). Justin Dauwels has collaborated with scholars based in Singapore, United States and Netherlands. Frequent co-authors include Andrzej Cichocki, François Vialatte, Monique Maurice, Hans‐Andrea Loeliger, Muhammad Tayyab Asif, Nikola Mitrović, Patrick Jaillet, Sydney S. Cash, Toshimitsu Musha and Peter N. Taylor. Their work appears in journals such as Science, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.

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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