Said Sanei

Saeid Sanei

Professor

School of Science & Technology

Staff Group(s)
Computing and Technology

Role

Professor Sanei is a professor of signal processing and machine learning. He teaches on the Wireless Communication and Networking and Digital Control modules. He is the TILT Chair in Machine Learning and the Department Research Coordinator for Computing and Technology.

Career overview

Saeid received his PhD from the Department of Electrical & Electronic Engineering, Imperial College London.

Since then he has been a member of academic staff in Iran, Singapore, and the United Kingdom (King’s College London, Cardiff University, and University of Surrey, where he served as the Deputy Head of Computer Science Department).

He has also been a visitor to RIKEN Brain Institute in Japan, a Distinguished Speaker in Nanyang Singapore, an External Examiner to Glasgow University, London Southbank University, and University of Mauritius. He is currently a Technical Committee Member of the IEEE Signal Processing Theory and Methods (SPTM) and an Editor and Associate Editor of a number of Journals.

Research areas

Saeid's research covers a wide area of signal processing and machine learning with major applications to computer networking, communications, speech and biomedical engineering, automation, brain computer interfacing (BCI), and big data. His contributions to tensor factorisation and cooperative networking are also the two main pillars of big data analytics. He has three books, a number of book chapters and over 350 peer reviewed papers mostly with IEEE.

His other areas of research includes:

  • deep neural networks
  • assistive technology
  • brain computer interfacing
  • biomedical engineering
  • nature-inspired modelling
  • compressive sensing
  • modelling complex systems
  • machine learning
  • pattern recognition
  • complex & quaternion – valued signals and systems, and the related applications to medical signals and images
  • healthcare technology
  • body sensor networking
  • biometrics
  • speech, and communication
  • analysis of sleep, seizure, dementia, stroke, and other brain-related diseases from EEG signals.

External activity

He has been a Technical Committee Membe of the IEEE Machine Learning for Signal Processing Committee from 2013-2018. Since 2018 he has been the Technical Committee Member of the IEEE Signal Processing Theory and Methods. He has often been invited as a Keynote Speaker for prestigious international conferences, and to present in workshops in conferences, academia and industry.

Meeting, Workshop and Conference Organisation

  • General Chair of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, UK.
  • General Chair and Organiser of the 22nd International Conference on Digital Signal Processing, DSP 2017, London, UK
  • Honorary Chair, 1st International Conference on Emerging Trends in Electrical, Electronic and Communication Engineering (ELECOM 2016), Mauritius
  • Technical Co-Chair of European Signal Processing Conference, EUSIPCO 2016, Budapest, Hungary
  • General Chair, IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013), UK
  • Honorary Chair, Third international Conference on Bio-inspired Systems and Signal Processing, 2010, Valencia, Spain
  • General Chair of IEEE Statistical Signal Processing Workshop, SSP 2009, Cardiff, UK
  • Organising Chair of 17th International Conference on Digital Signal Processing, DSP 2007, Cardiff, UK.

Editorial Activities

  • Associate Editor of Scientia Iranica Transactions on Electrical Engineering (since August 2017)
  • Editorial Board Member of Journal of Signals (since March 2017)
  • Editorial Board Member of Journal of Neurodevelopment Cognition (since November 2016)
  • Guest Editor of the Journal of Bioengineering, Special Issue in Biomedical Signal Processing (since 2016)
  • Guest Editor of the Sixth Special Issue on Advances in Biomedical Signal and Image Processing, and Biometrics, Elsevier Journal of Computers and Electrical Engineering (2015-2016)

Sponsors and collaborators

Professor Sanei collaborates with many national and international institutions, below are some examples:

  • Department of Electrical & Electronic engineering, Imperial College London
  • Department of Clinical Neuroscience, King’s College London
  • Department of Computer Science, University of Surrey
  • A-star and Department of Electrical & Electronic Engineering, National University of Singapore
  • RIKEN Brain Institute, Japan
  • Department of Electrical Engineering, UCLA, CA, USA

Publications

Books (monograms):

Sanei S, Jarchi D, Constantinides A G, Body Sensor Networks, John Wiley & Sons, 2019, ISBN-10: 1119390028 (under print)

Sanei S and Hassani H, Singular Spectrum Analysis of Biomedical Signals, CRC Press, 2015, ISBN-10: 1466589272

Sanei S, Adaptive Processing of Brain Signals, John Wiley & Sons, 2013, ISBN- 10: 0470686138

Sanei S and Chambers J, EEG Signal Processing, John Wiley & Sons, 2007, ISBN-10: 0470025816

Books (editorial)

Fleming P, Vyas N, Sanei S and Deb K, Emerging Trends in Electrical, Electronic and Communications Engineering, Springer 2016

Sanei S, Smaragdis P, Nandi A, Ho ATS and Larsen J: Proceedings of the International Workshop on Machine Learning for Signal Processing (MLSP)2013, IEEE Press, 2013

Babiloni F, Cichocki A, Sanei S, Astolfi L, Cincotti F and Gonzalez Andino S, Computational Intelligence & Neuroscience, 4th International Conference on Bioinspired Systems and Cognitive Signal Processing, 2011

Book Chapters

S. Sanei, S. Monajemi, A. Rastegarnia, O. Geman, and S.-H. Ong, “Multitask Cooperative Networks and their Diverse Applications”, Chapters 15-05, Learning Approaches in Signal Processing" Pan Stanford DSP Book Series, 2018 (Editors: Wan-Chi Siu, Lap-Pui Chau, Liang Wang, Tieniu Tan).

S. Monajemi, S. Ensafi, S. Lu, A. A. Kassim, C. L. Tan, S. Sanei and S-H Ong, “Adaptive Distributed Dictionary Learning for HEp-2 Cell Classification”, In Biomedical Signal Processing in Big Data. Florida, USA: Taylor & Francis Group, LLC, a State of Delaware limited liability company, 2017.

A. Khalili, A. Rastegarnia, W. M. Bazzi, and S. Sanei, Maximum Correntropy based Distributed Estimation of Adaptive Networks, Advances in Computer Communications and Networks - from Green, Mobile, Pervasive Networking to Big Data Computing, River Publisher, Eds. Aaron Striegel, Min Song, and Kewei Sha, 2016.

S. Sanei and B. Makkiabadi, Tensor Factorization with Application to Convolutive Blind Source Separation of Speech, Machine Audition: Principles, Algorithms and Systems, IGI-Global Pub., Edited by W. Wang, 2009.

M. Jing and S. Sanei, Simultaneous EEG-fMRI Analysis with Application to Detection and Localizaion of Seizure Signal Sources, Recent Advances in Signal Processing, IN-TECH Pub., ISBN 978-953-307-002-5, Edited by A. A. Zaher, 2009.

Recent Journal Papers

M. Latifi, A. Khalili, A. Rastegarnia, W. M. Bazzi, and S. Sanei, “A self-governed online energy management and trading for smart micro/nano-grids,” To appear in IEEE Transactions on Industrial Electronics, 2019 (Accepted on 20/09/2019)

H. Azami, S. E. Arnold, S. Sanei, Z. Chang, G. Sapiro, J. Escudero, and A. S. Gupta, “Multiscale fluctuation-based dispersion entropy and its applications to neurological disease,” IEEE Access, vol. 7, no. 1, pp. 68718-68733, 2019, Print ISSN: 2169-3536, DoI: 10.1109/ACCESS.2019.2918560

A. Akbari, M. Trocan, S. Sanei, and B. Granado, “Joint sparse learning with nonlocal and local image priors for image error concealment," IEEE Transactions on Circuits and Systems for Video Technology, (Accepted on June 26, 2019), 10.1109/TCSVT.2019.2927912

V. Vahidpour, M. Latifi, A. Khalili, A. Rastegarnia, and S. Sanei, “Performance analysis of distributed kalman filtering with partial diffusion over noisy network,” To appear in IEEE Transactions on Aerospace and Electronic Systems, 2019, DoI: 10.1109/TAES.2019.2933961

A. Rastegarnia, P. Malekian, A. Khalili, W. M. Bazzi, and S. Sanei, “Tracking Analysis of Minimum Kernel Risk-Sensitive Loss Algorithm Under General Non-Gaussian Noise,” IEEE Transactions on Circuits and Systems II, Vol 66, no. 7, pp. 1262-1266, 2019. DoI: 10.1109/TCSII.2018.2874969

M. Latifi, A. Khalili, A. Rastegarnia, and S. Sanei, “A Distributed game-theoretic demand response with multi-class appliance control in smart grid" Elsevier Journal of Applied Energy, 2019

M. Latifi, A. Khalili, A. Rastegarnia, and S. Sanei, “A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations, IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 2555-2568, DoI. 10.1109/TII.2018.2866267, May 2019.

A. Prochazka, T. Dostálová, M. Kašparová, O. Vyšata, H. Charvátová, and S. Sanei, “Augmented reality implementations in stomatology,” MDPI Journal of Applied Sciences, Special Issue on Augmented Reality: Current Trends, Challenges and Prospects, Invited review paper, MDPI 2019 (Accepted June 19, 2019).

D. Mandic, P. Djuric, A. Cichocki, C. C. Took, S. Sanei, and L. Hanzo, “Quo Vadis ICASSP – Echoes of the 2019 International Conference on Acoustics, Speech, and Signal Processing, Brighton, UK, 2019; Signal Processing Meets Needs of Modern Humankind.” To appear in IEEE signal Processing Magazine, 2019 (Accepted on June 14, 2019).

V. Vahidpour, A. Rastegarnia, A. Khalili, and S. Sanei, “Partial diffusion Kalman filtering for distributed state estimation in multi-agent networks,” to appear in the IEEE Transactions on Neural Networks and Learning Systems, 2019 (arXiv preprint arXiv:1705.08920).

A. Rastegarnia, P. Malekian, A. Khalili, W. M. Bazzi, and S. Sanei, “Tracking Analysis of Minimum Kernel Risk-Sensitive Loss Algorithm Under General Non-Gaussian Noise,” IEEE Transactions on Circuits and Systems II, Vol 66, no. 7, pp. 1262-1266, 2019. DoI: 10.1109/TCSII.2018.2874969.

A. Antoniades, L. Spyrou, D. Martin-Lopez, A. Valentin, G. Alarcon, S. Sanei, and C. Cheong Took, “Deep neural architectures for mapping scalp to intracranial EEG,” International Journal of Neural Systems, 2018, DOI. 10.1142/S0129065718500090, online.

S. Monajemi, S. Sanei, and S.-H. Ong, “Information credibility over multitask distributed networks,” Elsevier Journal of Future Generation Computer Systems, Special Issue on Measurements and Security of Complex Networks and Systems, vol. 83, pp. 485-495, 2018. https://doi.org/10.1016/j.future. 2017.07.023.

D. Jarchi, J. Pope, T. K. M. Lee, L. Tamjidi, and S. Sanei, “A review on accelerometry based gait analysis and emerging clinical applications,” IEEE Reviews in Biomedical Engineering, vol. 11, issue 1, pp. 177-194, 2018, DOI. 10.1109/RBME.2018.2807182

Z. Yang, W.-K. Ling, R. Tao, L. K. Woo, and S. Sanei “Optimal design of orders of DFrFTs for sparse representations,” IET Signal Processing, Vol. 12, no. 8, pp. 1023-1033, 2018, DOI.  10.1049/iet-spr.2017.0283.

M. Latifi, A. Rastegarnia, A. Khalili, and S. Sanei, “Agent-based decentralized optimal charging strategy for plug-in electric vehicles” IEEE Transactions on Industrial Electronics, pp. 1-13, 2018 DoI. 10.1109/TIE.2018.2853609.

E. Eghlimi, B. Makkiabadi, N. Samadzadehaghdam, H. Khajehpour, F. Mohagheian,  and S. Sanei, “A novel underdetermined source recovery algorithm based on k-sparse component analysis, Springer Journal of Circuits, Systems, and Signal Processing, Part of Springer Nature, pp. 1-23, 2018 (https://doi.org/10.1007/s00034-018-0910-9).

A. Prochazka, J. Kuchynka, O. Vysata, M. Schatz, M. Yadollahi, S. Sanei, and M. Valis, “Sleep Scoring using polysomnography data features,” Springer Journal of Signal, Image and Video Processing (SIVP), vol. 12, no. 6, pp. 1-9, 2018, DoI 10.1107/s11760-018-1252-6.

Antoniades A, Spyrou L, Martin-Lopez D, Valentin A, Alarcon G, Sanei S and Cheong Took C, Detection of interictal discharges using convolutional neural networks from multichannel intracranial EEG, IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2017, DOI: 10.1109/TNSRE.2017.2755770

Latifi M, Khalili A, Rastegarnia A and Sanei S, Fully Distributed Demand Response Using Adaptive Diffusion Stackelberg Algorithm, IEEE Transactions on Industrial Informatics, 2017, 13(5), 2291-2301, DOI: 10.1109/TII.2017.2703132

Monajemi S, Jarchi D, Ong SH and Sanei S, Cooperative Particle Filtering for Detection and Tracking of ERP Subcomponents from Multichannel EEG,  Journal of Entropy, 2017, 19(5), 199, DOI:10.3390/e19050199

Khalili A, Rastegarnia A and Sanei S, Performance analysis of incremental LMS over flat fading channels, IEEE Transactions on Control of Network Systems, 2017, 4(3), 489-498, DOI 10.1109/TCNS.2016.2516826

Khalili A, Rastegarnia A, Bazzi WM and Sanei S, Analysis of incremental augmented affine projection algorithm for distributed estimation of complex-valued signals, Journal of Circuits, Systems & Signal Processing, 2017, 36 (1), 119-136

Monajemi S, Eftaxias K, Ong SH and Sanei S, An informed multitask diffusion adaptation approach to study tremor in Parkinson’s disease, IEEE Journal of Selected Topics in Signal Processing,  2016, 10(7) , 1306-1314

Spyrou L, Lopez DM, Alarcon G, Valentin A and Sanei S, Detection of intracranial signatures of interictal epileptiform discharges from concurrent scalp EEG, International Journal of Neural Systems, 2016, 26(4), DOI: 10.1142/S0129065716500167

Enshaeifar S, Kouchaki S, Cheong Took C and Sanei S, Quaternion singular spectrum analysis of electroencephalogram with application to sleep analysis, IEEE Trans. on Neural Systems & Rehabilitation Engineering, 2016, 24(1), 57 - 67

Wang S, Tang HL, Al Turk LI, Hu Y, Sanei S, Saleh GM and Peto T, Localising microaneurysms in fundus images through singular spectrum analysis, IEEE Trans. on Biomedical Engineering, 2016, 10.1109/TBME.2016.2585344

Khalili A, Rastegarnia A and Sanei S, Tracking performance of incremental augmented complex least mean square adaptive network in the presence of model non-stationarity, IET Signal Processing, 2016, 10 (7), 798-804

See all of Saeid Sanei's publications...

Press expertise

  • Brain Computer Interfacing
  • EEG-assisted Speech Rehabilitation
  • Healthcare Technology
  • Rehabilitation Engineering
  • EEG-fMRI for Seizure Detection, Localisation and Prediction
  • Body Sensor Networking
  • Machine Learning and Pattern Recognition
  • Fall prediction
  • Smart Grid and Smart City

Course(s) I teach on