D. Mandic

999 total citations · 1 hit paper
9 papers, 713 citations indexed

About

D. Mandic is a scholar working on Cognitive Neuroscience, Computational Mechanics and Law. According to data from OpenAlex, D. Mandic has authored 9 papers receiving a total of 713 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Cognitive Neuroscience, 2 papers in Computational Mechanics and 2 papers in Law. Recurrent topics in D. Mandic's work include EEG and Brain-Computer Interfaces (2 papers), Advanced Adaptive Filtering Techniques (2 papers) and Law in Society and Culture (2 papers). D. Mandic is often cited by papers focused on EEG and Brain-Computer Interfaces (2 papers), Advanced Adaptive Filtering Techniques (2 papers) and Law in Society and Culture (2 papers). D. Mandic collaborates with scholars based in United Kingdom, China and Japan. D. Mandic's co-authors include Naveed ur Rehman, Qibin Zhao, Namgil Lee, Masashi Sugiyama, Phebe Vayanos, Ivan Oseledets, Anh Huy Phan, Andrzej Cichocki, Beth Jelfs and David Looney and has published in prestigious journals such as Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, Nature Machine Intelligence and now publishers, Inc. eBooks.

In The Last Decade

D. Mandic

5 papers receiving 691 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Multivariate empirical mode decomposition

2009 681 citations Naveed ur Rehman, D. Mandic Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
D. Mandic United Kingdom	4	233	211	168	87	81	9	713
Yung-Hung Wang Taiwan	8	150 0.6×	114 0.5×	76 0.5×	52 0.6×	75 0.9×	14	533
Karin Blank United States	7	227 1.0×	64 0.3×	61 0.4×	138 1.6×	41 0.5×	17	682
Jonatan Lerga Croatia	15	140 0.6×	90 0.4×	198 1.2×	31 0.4×	29 0.4×	69	779
S. de Waele Netherlands	13	138 0.6×	86 0.4×	153 0.9×	55 0.6×	165 2.0×	45	709
Corinne Mailhes France	16	86 0.4×	96 0.5×	91 0.5×	40 0.5×	178 2.2×	66	1.0k
Shiou-Gwo Lin Taiwan	5	124 0.5×	118 0.6×	65 0.4×	23 0.3×	114 1.4×	10	574
Eugene Brevdo United States	8	206 0.9×	190 0.9×	94 0.6×	103 1.2×	29 0.4×	11	678
Sayed Mahmoud Sakhaei Iran	7	202 0.9×	43 0.2×	51 0.3×	72 0.8×	37 0.5×	18	523
Jean-Christophe Cexus France	12	417 1.8×	42 0.2×	178 1.1×	206 2.4×	81 1.0×	24	997
Md. Khademul Islam Molla Japan	12	81 0.3×	104 0.5×	184 1.1×	35 0.4×	34 0.4×	34	431

Countries citing papers authored by D. Mandic

Since Specialization

Citations

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

Fields of papers citing papers by D. Mandic

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of D. Mandic

This figure shows the co-authorship network connecting the top 25 collaborators of D. Mandic. A scholar is included among the top collaborators of D. Mandic 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 D. Mandic. D. Mandic is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Scott, Gregory, et al.. (2025). Augmentation of EEG and ECG Time Series for Machine Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates. 1–8.

2.

Wang, Liyuan, et al.. (2025). Versatile cardiovascular signal generation with a unified diffusion transformer. Nature Machine Intelligence. 8(1). 6–19.

3.

Chen, Zehua, et al.. (2025). RespDiff: An End-to-End Multi-scale RNN Diffusion Model for Respiratory Waveform Estimation from PPG Signals. 1–5. 1 indexed citations

4.

Mandic, D., et al.. (2021). LMYE Salon #5: Sara Ramshaw & Danilo Mandic - Law as Sonic Performance.

5.

Mandic, D.. (2020). Law with the Sound of Its Own Making. Law/text/culture. 24(1).

6.

Cichocki, Andrzej, Namgil Lee, Ivan Oseledets, et al.. (2017). Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives. now publishers, Inc. eBooks. 16 indexed citations

7.

Looney, David, Preben Kidmose, Cheolsoo Park, et al.. (2012). The In-the-Ear Recording Concept. 7 indexed citations

8.

Rehman, Naveed ur & D. Mandic. (2009). Multivariate empirical mode decomposition. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 466(2117). 1291–1302. 681 indexed citations breakdown →

9.

Mandic, D., et al.. (2008). Online tracking of the degree of nonlinearity within complex signals. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. 2061–2064. 8 indexed citations

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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