World's Best Scientists 2026 revealed!
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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
79
Citations
28412
World Ranking
1132
National Ranking
62

Electronics and Electrical Engineering

D-Index
77
Citations
26666
World Ranking
603
National Ranking
28

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award
  • 2013 - IEEE Fellow For contributions to multivariate and nonlinear learning systems

Overview

Danilo P. Mandic is affiliated with Imperial College London in the United Kingdom. Their research spans multiple areas within computer science, particularly focusing on artificial intelligence and signal processing. Their work extends into cardiology and cardiovascular medicine, cognitive neuroscience, and biomedical engineering.

The scientist's publication record includes significant contributions to several main topics, such as tensor decomposition and its applications, EEG and brain-computer interfaces, advanced graph neural networks, ECG monitoring and analysis, blind source separation techniques, speech and audio processing, and non-invasive vital sign monitoring.

Notable recent papers by Danilo P. Mandic include:

  • "Convergence of the RMSProp deep learning method with penalty for nonconvex optimization" (2021), Neural Networks
  • "The 2023 wearable photoplethysmography roadmap" (2023), Physiological Measurement
  • "In-Ear SpO2: A Tool for Wearable, Unobtrusive Monitoring of Core Blood Oxygen Saturation" (2020), Sensors
  • "AudioLDM: Text-to-Audio Generation with Latent Diffusion Models" (2023), arXiv (Cornell University)
  • "Exploring Convolutional Neural Network Architectures for EEG Feature Extraction" (2024), Sensors

Frequent collaborators include Harry J. Davies, Ljubiša Stanković, Fu Siong Ng, Arunashis Sau, and Yao Xu, each contributing to multiple joint publications with Mandic.

Mandic's work is published in a variety of venues, with a notable number of papers appearing in:

  • arXiv (Cornell University)
  • IEEE Signal Processing Magazine
  • Signal Processing
  • Neural Networks
  • Sensors

Danilo P. Mandic has been recognized as an IEEE Fellow since 2013 for their contributions to multivariate and nonlinear learning systems.

Best Publications

  • Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis

    Andrzej Cichocki;Danilo Mandic;Lieven De Lathauwer;Guoxu Zhou

  • Multivariate empirical mode decomposition

    N. Rehman;D. P. Mandic

  • Recurrent Neural Networks for Prediction

    Danilo P. Mandic;Jonathon A. Chambers

  • Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability

    Danilo P. Mandic;Jonathon Chambers

  • Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models

    Danilo Mandic;Vanessa Su Lee Goh

  • Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis

    A. Cichocki;D. Mandic;A-H. Phan;C. Caiafa

  • Complex Valued Nonlinear Adaptive Filters

    Danilo P. Mandic;Vanessa Su Lee Goh

  • Filter Bank Property of Multivariate Empirical Mode Decomposition

    Naveed ur Rehman;D P Mandic

  • Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis

    Danilo P. Mandic;Naveed Ur Rehman;Zhaohua Wu;Norden E. Huang

  • Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1.

    Andrzej Cichocki;Namgil Lee;Ivan V. Oseledets;Anh Huy Phan

  • Multivariate multiscale entropy: a tool for complexity analysis of multichannel data.

    Mosabber Uddin Ahmed;Danilo P. Mandic

  • Complex Empirical Mode Decomposition

    T. Tanaka;D.P. Mandic

  • The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes

    C.C. Took;D.P. Mandic

  • Biometrics from Brain Electrical Activity: A Machine Learning Approach

    R. Palaniappan;D.P. Mandic

  • A generalized normalized gradient descent algorithm

    D.P. Mandic

  • Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions

    Andrzej Cichocki;Namgil Lee;Ivan Oseledets;Anh-Huy Phan

  • The In-the-Ear Recording Concept: User-Centered and Wearable Brain Monitoring

    D. Looney;P. Kidmose;Cheolsoo Park;M. Ungstrup

  • Augmented second-order statistics of quaternion random signals

    Clive Cheong Took;Danilo P. Mandic

  • Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition

    Cheolsoo Park;D. Looney;Naveed ur Rehman;A. Ahrabian

  • Empirical Mode Decomposition for Trivariate Signals

    N. ur Rehman;D.P. Mandic

  • Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

    A. Cichocki;A-H. Phan;Q. Zhao;N. Lee

Frequent Co-Authors

Andrzej Cichocki
Andrzej Cichocki Systems Research Institute
Scott C. Douglas
Scott C. Douglas Southern Methodist University
Ljubisa Stankovic
Ljubisa Stankovic University of Montenegro
Jonathon A. Chambers
Jonathon A. Chambers Harbin Engineering University
Luxi Yang
Luxi Yang Southeast University
Kazuyuki Aihara
Kazuyuki Aihara University of Tokyo
Ervin Sejdic
Ervin Sejdic University of Pittsburgh
Yongming Huang
Yongming Huang Southeast University

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