Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Signal Processing



+44 (0)20 7594 6271d.mandic Website




Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267




813Electrical EngineeringSouth Kensington Campus





Dr. Mandic received the Ph.D. degree in nonlinear adaptive signal processing  in 1999 from Imperial College, London, London, U.K. where he is now a Professor. He specialises in Statistical Learning Theory, Machine Intelligence, and Statistical Signal Processing, and their applications especially in Biomedicine and Finance. He is a pioneer of Hearables (in-ear sensing of neural function and vital signs), an unobtrusive, discreet and long-term wearable solution for long-term physiological monitoring based on miniaturised sensors embedded on an earplug, an area where he holds several patents. He also specialises in  Machine Intelligence for Finance, and is a Director of the Financial Signal Processing and Machine Learning Lab a Imperial.

He has written over 500 journal and conference articles, and research monographs on Recurrent Neural Networks (with Wiley, 2001), Complex-valued Adaptive Filters and Neural Networks (Wiley 2009), Tensor Networks for Dimensionality Reduction and Large Scale Optimisation (Now Publishers, 2017) and Data Analytics on Graphs (Now Publishers, 2021).

Prof Mandic is a 2019 recipient of the Dennis Gabor Award for "Outstanding Achievements in Neural Engineering", given by the International Neural Networks Society (INNS). He is also a 2018 winner of the Best Paper Award in IEEE Signal Processing Magazine, for his article on Tensor Decompositions for Signal Processing Application, and the 2021 winner of the Outstanding Paper Award in the IEEE ICASSP conference. He has coauthored 6 more award winning articles.  He is a Core Member of the Machine Learning Initiative at Imperial.

Danilo is a Vice-President of the International Neural Networks Society, and a past Technical Chair of ICASSP 2019, held in Brighton UK. He also received President's Award for Excellence in Research Supevervision at Imperial College in 2014. Danilo is passionate about cross-disciplinary aspects of his work and about bringing research into the curriculum. His current research interests areas are Adaptive Learning Theory, Big Data, Machine Learning on Graphs, Neural Networks, and Complexity Science, and their applications in Biomedicine and Financial Engineering.

Selected Publications

Journal Articles

Scalzo B, Konstantinidis A, Mandic DP, 2021, Analysis of global fixed-income returns using multilinear tensor algebra, The Journal of Fixed Income, Vol:30, ISSN:1059-8596, Pages:32-52

Nakamura T, Alqurashi Y, Morrell M, et al., 2020, Hearables: automatic overnight sleep monitoring with standardised in-ear EEG sensor, IEEE Transactions on Biomedical Engineering, Vol:67, ISSN:0018-9294, Pages:203-212

Stankovic L, Mandic D, Dakovic M, et al., 2020, Data Analytics on Graphs Part I: Graphs and Spectra on Graphs, Foundations and Trends in Machine Learning, Vol:13, ISSN:1935-8237, Pages:1-157

Stankovic L, Mandic D, Dakovic M, et al., 2020, Data Analytics on Graphs Part II: Signals on Graphs, Foundations and Trends in Machine Learning, Vol:13, ISSN:1935-8237, Pages:158-331

Stankovic L, Mandic D, Dakovic M, et al., 2020, Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications, Foundations and Trends in Machine Learning, Vol:13, ISSN:1935-8237, Pages:332-530

Stankovic L, Mandic DP, Dakovic M, et al., 2019, Understanding the Basis of Graph Signal Processing via an Intuitive Example-Driven Approach, IEEE Signal Processing Magazine, Vol:36, ISSN:1053-5888, Pages:133-145

von Rosenberg W, Chanwimalueang T, Goverdovsky V, et al., 2017, Hearables: feasibility of recording cardiac rhythms from head and in-ear locations, Royal Society Open Science, Vol:4, ISSN:2054-5703

Goverdovsky V, von Rosenberg W, Nakamura T, et al., 2017, Hearables: multimodal physiological in-ear sensing, Scientific Reports, Vol:7, ISSN:2045-2322

Cichocki A, Anh-Huy P, Zhao Q, et al., 2017, Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations Part 2 Applications and Future Perspectives, Foundations and Trends in Machine Learning, Vol:9, ISSN:1935-8237, Pages:431-+

Cichocki A, Lee N, Oseledets I, et al., 2016, Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions, Foundations and Trends in Machine Learning, Vol:9, ISSN:1935-8237, Pages:249-429

Cichocki A, Mandic DP, Anh HP, et al., 2015, Tensor decompositions for signal processing applications: from two-way to multiway component analysis, IEEE Signal Processing Magazine, Vol:32, ISSN:1053-5888, Pages:145-163

Zhao Q, Caiafa CF, Mandic D, et al., 2013, Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN:0162-8828, Pages:1-1

Looney D, Kidmose P, Park C, et al., 2012, The In-The-Ear recording concept, Ieee Pulse Magazine, Vol:3, Pages:34-42

Xia Y, Douglas SC, Mandic DP, 2012, Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators, Ieee Signal Processing Magazine

Ahmed MU, Mandic DP, 2011, Multivariate multiscale entropy: A tool for complexity analysis of multichannel data, Physical Review E (statistical, Nonlinear, and Soft Matter Physics), Vol:84, Pages:061918-1-061918-10

Rehman N, Mandic DP, 2010, Multivariate empirical mode decomposition, Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences, Vol:466, ISSN:1364-5021, Pages:1291-1302


Stankovic L, Mandic D, Dakovic M, 2021, Data Analytics on Graphs, Now Publishers, ISBN:9781680839821

Mandic DP, Goh VSL, 2009, Complex Valued Nonlinear Adaptive Filters Noncircularity, Widely Linear and Neural Models, Wiley, ISBN:9780470066355

Mandic DP, Golz M, Kuh A, et al., 2008, Signal Processing Techniques for Knowledge Extraction and Information Fusion, Springer

Mandic, D.P., Chambers, J.A., 2001, Recurrent neural networks for prediction: learning algorithms, architectures and stability, Chichester, John Wiley, ISBN:9780471495178

More Publications