Professor Sanei, a Fellow of British Computer Society, 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.
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). Currently, he is an Academic Visitor in Digital Health to the Department of Electrical & Electronic Engineering of Imperial College London.
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. In the past he also served as the Technical Committee Member of the IEEE Machine Learning for Signal Processing (MLSP) and an Associate Editor for the IEEE Signal Processing Letters and IEEE Signal Processing Magazine.
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
- cooperative networks
- speech, and communications
- analysis of sleep, seizure, dementia, stroke, and other brain-related diseases from EEG signals.
Professor Sanei is a Fellow of British Computer Society (FBCS) and since 2018 he has been the Technical Committee Member of the IEEE Signal Processing Theory and Methods (SPTM). He has been a Technical Committee Membe of the IEEE Machine Learning for Signal Processing (MLSP) Committee from 2013-2018. 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
- International Chair of The International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), 2020, Mauritius
- 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.
- Associate Editor of Journal of Computational Intelligence and Neuroscience
- 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 seven Special Issue on Advances in Biomedical Signal and Image Processing, and Biometrics, Elsevier Journal of Computers and Electrical Engineering (2015-2016)
- Guest Editor of three Sensor Journal Special Issues mainly in AI, Machine Learning, and Sensor Networks
- Guest Editor of Biosensors Journal; Special Issue Intelligent on Biosignal Processing in Wearable and Implantable Sensors
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 Electronic Engineering, Royal Holloway University of London
- School of Computer Science and Electronic Engineering (CSEE)
- A-star and Department of Electrical & Electronic Engineering, National University of Singapore
- Skolkovo Institute of Science and Technology, Russia
- Research Center for Human Development (CEDH), Faculdade de Educação e Psicologia, , Porto, Portugal
Sanei S and Chambers J, EEG Signal Processing and Machine Learning, John Wiley & Sons, May 2021, ISBN 1119386942
Sanei S, Jarchi D, Constantinides A G, Body Sensor Networking, Design and Algorithms, John Wiley & Sons, 2020, ISBN-10: 1119390028
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
- S. Sanei, J. Chambers, J. McWhriter, Y. Hicks, and A. G. Constantinides, Proceedings of the 15th Int. Conf. on Digital Signal Processing (Eds.), 2007.
- S. Sanei, J. Chambers, and J. McWhriter, Proceedings of the 2009 IEEE Int. Workshop on Statistical Signal Processing (Eds.), 2009.
- F. Babiloni, A. Cichocki, S. Sanei, L. Astolfi, F. Cincotti, and S. Gonzalez Andino, Computational Intelligence & Neuroscience; Selected papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing, 2011
- S. Sanei, P. Smaragdis, A. Nandi, A. T. S. Ho, and J. Larsen: Proceedings of the International Workshop on Machine Learning for Signal Processing (MLSP)2013, IEEE Press, (Eds.) Sept. 2013.
- P. Fleming, N. Vyas, S. Sanei, and K. Deb, Emerging Trends in Electrical, Electronic and Communications Engineering, Springer 2016.
- P. Fleming, N. Vyas, S. Sanei, K. Deb, and A. Jackobsson, Smart and Sustainable Engineering for Next Generation Applications: Proceeding of the Second International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM 2018), November 28-30, 2018, Mauritius.
- Islam M.K., Rastegarnia A., Sanei S. (2021) Signal Artifacts and Techniques for Artifacts and Noise Removal. In: Ahad M.A.R., Ahmed M.U. (eds) Signal Processing Techniques for Computational Health Informatics. Intelligent Systems Reference Library, vol 192. pp. 23-79, Springer, Cham. https://doi.org/10.1007/978-3-030-54932-9_2.
- 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
S. Afshar, R. Boostani, and S. Sanei, “A combinatorial deep learning structure for precise depth of anesthesia estimation from EEG Signals,” IEEE Journal of Biomedical and Health Informatics. Doi, 10.1109/JBHI.2021.3068481, Apr. 26, 2021.
B. Abdi-Sargezeh, A. Valentin, G. Alarcon, and S. Sanei, “Incorporating uncertainty in data labeling into automatic detection of interictal epileptiform discharges from concurrent scalp EEG via multi-way analysis, to appear in the International Journal of Neural Systems (IJNS), 2021, 10.1142/S0129065721500192.
D. Jarchi, J. Kaler, and S. Sanei, “Lameness detection in cows using hierarchical deep learning and synchrosqueezed wavelet transform,” IEEE Sensors Journal, vol. 21, Issue: 7, April1, 1 2021.
Giv, H., Khalili, A., Rastegarnia, A., and Sanei, S., "A robust adaptive estimation algorithm for Hamiltonian sensor networks,” IEEE Control Systems Letters 5 (4), 1243-1248.
Khalili, A., Vahidpour, V., Rastegarnia, A., and Sanei, S., “Partial diffusion Kalman filter with adaptive combiners" Accepted by the IEEE Transactions on Aerospace and Electronic Systems, Jan. 2020, DOI: 10.1109/TAES.2020.3046085.
V. Vahidpour, A. Khalili, A. Rastegarnia, W. Bazzi, and S. Sanei, “Variants of partial update augmented CLMS algorithm and their performance analysis,” Accepted (on 07/05/2020) for publication in the IEEE Transactions on Signal Processing, vol. 68, no. 1, pp. 3146-3157, 2020, doi: 10.1109/TSP.2020.2993938.
M. Latifi, A. Khalili, A. Rastegarnia, and S. Sanei, “A robust scalable demand-side management based on diffusion-ADMM strategy for smart grid,” to appear in IEEE Internet of Things (IoT) Journal, vol. 7, no. 4, pp. 3363-3377, DOI. 10.1109/JIOT.2020.2968539.
D. Jarchi, J. Andreu-Perez, M. Kiani, O. Vyšata, J. Kunchynka, A. Procházka, and S. Sanei, “Recognition of patient groups with sleep related disorder using bio-signal processing and deep learning,” Sensors, 20(9) 2594, 2020.
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, vol. 67, issue 1, pp. 7484-7498, 2020, 10.1109/TIE.2019.2945280.
M. Latifi, A. Khalili, A. Rastegarnia, W. Bazzi, and S. Sanei, “Demand-Side management for smart grid via diffusion adaptation,” IET Smart Grid 3 (1), 69-82, 2020.
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), 798-804See all of Saeid Sanei's publications...
- Deep Neural Networks
- Seizure and Mental Health monitoring using EEG
- Brain Computer Interfacing
- EEG-assisted Speech Rehabilitation
- Digital Health
- 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