Amir Hussain
amir hussain

Prof Amir Hussain



Amir Hussain received his B.Eng (highest 1st Class Honours with distinction) and Ph.D degrees, from the University of Strathclyde, Glasgow, U.K., in 1992 and 1997, respectively.

Following postdoctoral and academic positions at the Universities of West of Scotland (EPSRC postdoctoral fellow: 1996-98), Dundee (Research Lecturer: 1998-2000) and Stirling (Lecturer: 2000-4; Senior Lecturer: 2004-8; Reader: 2008-12; Professor: 2012-18) respectively, he joined Edinburgh Napier University (ENU) in 2018 as a Professor in the School of Computing. He is founding Director of the Centre for AI and Robotics (CAIR) and Head of the Data Science and Cyber Analytics (DSCA) Research Group (managing over 20 academics and research staff). He is also founding Head of the Cognitive Big Data Analytics (CogBiD) Research Lab, and co-Lead of the Centre for Cardio-Vascular Health (with the School of Health and Social Care).

He currently holds a number of Visiting Professorships, including at the University of Electronic Science and Technology of China (UESTC), Xi’an Jiaotong-Liverpool University, Shanghai University and Anhui University. He has previously held Visiting Professorships at the Massachusetts Institute of Technology (MIT - Synthetic Intelligence Lab), USA and the University of Oxford (Oxford Computational Neuroscience), UK.

He is an elected Executive Committee member of the UK Computing Research Committee (UKCRC) - the National Expert Panel of the Institution of Engineering and Technology (IET) and the BCS, The Chartered Institute for IT - promoting quality and impact of UK computing research, including formulating policy and presenting views to Government and Parliamentary Committees through submissions and responses to consultations. He is also a member of the Association for BME Engineers (ABFE-UK) and strives to collaboratively explore new ways to promote equality, diversity and inclusivity, and help connect and inspire young professionals, women and other members of under-represented groups in computing and engineering.

He is Chair of the IEEE UK and Ireland Industry Applications Society Chapter, which has been recognised as one of the most productive Chapters in the IEEE UK and Ireland Section. He has served as advisor for national and global industry and for international government organisations. For example, during 2019-20, he was international advisor for Kuwait Government’s Institute for Scientific Research (KISR) to develop the country’s National AI Strategy and the National AI Centre of Excellence. He advised on strategy, capacity building and development of high-impact AI research and innovation programmes to deliver priority objectives of Kuwait Vision 2035. He is also an invited Advisory Board member of the National Centre of Big Data and Cloud Computing (NCBC), established by Pakistan Government, offering strategic advice on harnessing the transformative potential of AI and data science through the development of a national AI strategy for the country.


Prof Hussain's research and innovation interests are cross-disciplinary and industry-led, aimed at developing and commercializing cognitive data science and responsible AI technologies to engineer smart healthcare and industrial systems of tomorrow.

He has (co)authored three international patents and over 500 publications, including nearly 250 international journal papers, 20 Books/monographs and 100+ Book chapters (with a current h-index of 68, i10-index of 274, and ~20,000 citations ). His key patent on functionally expanded neural networks (FENN methodology) was acquired by a US company (Patent no. US6453206B1)

He has led major cross-disciplinary research projects (including current grants totalling over GBP 5 Million), as Principal Investigator, funded by national and European research councils, local and international charities and industry, and supervised over 35 PhD students to-date. His high-profile PhD graduates include (amongst numerous others): Prof Erik Cambria (NTU, Singapore, IEEE Fellow and the world’s most highly-cited AI researcher in sentiment analysis) and Dr Soujanya Poria (2018 Presidential Young Investigator Award Winner, Singapore).


• He is founding Editor-in-Chief of Cognitive Computation (Springer Nature: SCI Impact Factor (IF): 5.18) and the Springer Book Series on Socio-Affective Computing, and Cognitive Computation Trends.

• He has been appointed invited Associate Editor/Editorial Board member for a number of prestigious journals, including: the IEEE Transactions on Artificial Intelligence (AI), the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Information Fusion (Elsevier), AI Review (Springer), IEEE Computational Intelligence Magazine,, and the IEEE Transactions on Emerging Topics in Computational Intelligence.

• Since 2017, he has consistently ranked in independent surveys, published in leading international journals (including in Elsevier’s Information Processing & Management Journal), as one of the world’s top two most productive. AI researchers in NLP (sentiment analysis) since 2000. Many of his works on biologically-inspired natural language processing for sentiment and opinion mining are ISI highly-cited papers (e.g. which have also been commercialised by his cofounded spinout company (SenticNet).

• He was co-Leader of the Winning Team (jointly with Tsinghua University) at the 2nd World Intelligent Driving Challenge (WIDC), held in Tianjin, China, 15-17 May 2018 , demonstrating the outstanding commercial impact of their winning vehicle’s cognitive system architecture which is also being globally cited ( His team secured second-position in the 2019 WIDC Challenge. WIDC is an annual, internationally leading competition comprising contests in autonomous driving, intelligent assistance, information security and virtual testing. The Challenge attracts 90-100 teams from industry, universities and research institutes across the world, and is broadcast by over 100 international media outlets.

• His pioneering research on Sentic Computing for Big Data sentiment analytics and Natural Language Processing, was awarded the top “4* (Outstanding)” (industrial) Impact evaluation by UK Government’s Research Enhancement Framework (REF2014) exercise, and also the Best Performing Approach Award for 'Semantic Parsing' Task at the joint-industry & academic-led 'Concept-Level Sentiment Analysis Challenge’, organized as part of the 11th Extended Semantic Web Conference.

• He organised, as General Chair, the 2020 IEEE WCCI (the world's largest and top technical event in Computational Intelligence, comprising IJCNN, IEEE CEC and FUZZ-IEEE, attended by over 2,000 delegates from academia and industry).

• He is an Executive Committee member of the UK Computing Research Committee (UKCRC) - the National Expert Panel of the Institution of Engineering and Technology (IET) and the BCS, The Chartered Institute for IT, for computing research in the UK. He has also been elected Vice-Chair of the Emergent Technologies Technical Committee of the IEEE Computational Intelligence Society (CIS) and (founding) Vice-Chair for the IEEE CIS Task Force on Intelligence Systems for e-Health. He is currently IEEE Chapter Chair of the UK and Ireland IEEE Industry Applications Society, and member of the IEEE CIS Conference Committee.


He is Founding/General/Organizing Chair for over 50 leading international conferences, including:

General Chair: IEEE World Congress on Computational Intelligence (WCCI’2020) – world’s largest, top biennial Conference on Computational Intelligence (comprising IJCNN, FUZZ-IEEE, IEEE-CEC, attended by over 2,000 delegates), Glasgow, Scotland, 19-25 July 2020 .

General Chair, 2023 IEEE Smart World Congress, Portsmouth, UK, 25-28 August 2023 (

Founding General Chair: Annual IEEE Symposium on Computational Intelligence in Healthcare & e-Health (IEEE CICARE) - organized as part of the flagship IEEE SSCI (since 2013) – IEEE SSCI CICARE 2023 in Mexico, 5-8 Dec 2023.

Founding General Chair: International Conference on Brain Inspired Cognitive Systems (BICS – since 2004: 1st-11th), BICS 2018 co-hosted with IEEE Brain Data Bank (BDB) Challenge - BICS’2022 was organised at UESTC, Chengdu, China, 22-23 April 2022.

General co-Chair: 3rd IEEE International Conference on Smart Data, Exeter, UK, 21-23 June 2017

Program Chair: 4th IEEE International Conference on Data Science and Systems (IEEE DSS’2018), Exeter, UK, 28-20 June 2018


Current funded Projects (examples - currently managing research grants totalling >£5million):

1. Lead Chief/Principal Investigator (PI) and Programme Director for the prestigious large (~GBP 4million) EPSRC Programme Grant (2021-2025): “Towards cognitively-inspired 5G-IoT enabled, multi-modal Hearing Aids (COG-MHEAR)” (EP/T021063/1). The proposal was ranked second under the UK Government’s Engineering and Physical Sciences Research Council (EPSRC) Programme Grants Call: Transformative Healthcare Technologies 2050. The cross-disciplinary COG-MHEAR consortium includes partner co-investigators (CIs) from the Universities of Edinburgh, Heriot-Watt, Glasgow, Nottingham and Manchester, and a strong User Group comprising industrial and clinical collaborators and end-user engagement organisations (including the UK Royal National Institute for Deaf People, Deaf Scotland, National Robotarium, global hearing-aid manufacturers: Phonak and Nokia Bell Labs, providing ~GBP1 million match-funding).

COG-MHEAR has developed the world’s first multi-modal hearing-assistive technology demonstrators including real-time software and hardware prototypes. These are radically exploiting and integrating the transformative potential of privacy-assuring, explainable and energy-efficient AI with 5G/6G communications, IoT/Edge and cybersecurity, coupled with wireless and wearable sensing and flexible (skin-based) electronics and robotics. These are enabling commercialization of personalized, multi-modal hearing assistive technologies with reduced end-user cognitive load/listening effort for smart social and work spaces of tomorrow. The adventurous research outputs are also being exploited to develop multimodally-enhanced, speech-enabled communication aids to support e.g. autistic people with speech disorders, and more natural human-robot interaction in both real-world and immersive AR/VR/MR environments. For updates and job opportunities, please see the COG-MHEAR website: (

3. Lead PI for new Scottish Government's Chief Scientist Office (CSO) funded research project (~GBP 0.14 Million: Grant Ref. COV-NAP-20-07), joint with the Usher Institute, University of Edinburgh, as part of the CSO Rapid Research in Covid -19 Programme Call (2020). The interdisciplinary project has developed and validated the world’s first AI-enabled ‘live’ dashboard for real-time analysis of Covid-19 related public sentiment & attitudes on social media platforms to inform national policy considerations. Exemplar case studies focussed on COVID-19 vaccination and tracing apps, and ethnic minorities experiences during the pandemic. The Dashboard is being used by the Scottish Government (SG) to better understand public mood & wellbeing during COVID-19. Novel ‘expert-in-loop’ AI tools for topic/theme & sentiment classification enabled rapid analysis of SG COVID-19 Public Dialogue datasets, at scale. ( Project report available at:

4. Lead PI for the Innovate UK grant (with Ace Aquatec Ltd) (~GBP 0.3million, 2021-24: UK Government’s Research and Innovation funded Knowledge Transfer Partnership (KTP) Project Ref: 512070), on novel multi-modal and IoT-enabled AI tools and platforms for real-time detection of fish diseases/pathogens and automated feeding systems to improve fish health. This research and innovation project has funded a Postdoctoral Data Scientist (Dr A Anwary) under my principal supervision and led to development of ground-breaking AI-powered fish biomass measuring technology. The innovative prototype achieved more than 99percent accuracy in commercial farm trials. The practically validated and commercialised technology (A-BIOMASS) has transformed the way biomass estimation is currently conducted, based on calculations and manual sampling (which are often inaccurate and lead to higher production costs as well as increased fish handling). This has paved the way to provide faster data to farmers in Scotland and globally, with fewer systems required on site, significantly saving both time and cost. /

5. Lead CI and co-Director of new H2020 EIT Digital and industry funded (~GBP 0.8 Million) Centre for Doctoral Training (CDT) on IoT Trust: Privacy, Blockchain and Cryptography (2023-27). The CDT is funding the training of 18 Doctoral students with a focus on development of privacy-preserving AI and Blockchain-enabled trustworthy citizen-centric systems. Key application areas include digital health and well-being, energy supply systems, e-Commerce, Fintech and public services.

Previous funded Projects (examples):

1. Lead PI for EPSRC grant (~GBP 0.5million), 2015-2019 (EP/M026981/1: Towards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devices: AV-COGHEAR). This pioneering project has been extensively covered in media, including the BBC ( and developed novel deep learning based speech enhancement algorithms for next-generation multi-modal hearing aids, listening devices and assistive technology. Project partners included: Psychology (CI: Prof. R. Watt, Stirling University), Speech & Hearing Research Group (CI: Prof J. Barker, Sheffield University), major hearing-aid manufacturers: Phonak AG/ Sonova (Dr P. Derleth) & UK MRC Institute of Hearing Research (Dr W. Whitmer).

2. Lead PI for UK EPSRC grant (~GBP 0.5million), 2011-2014 (EP/I009310/1: Dual Process Control Models in the Brain and Machines with Application to Autonomous Vehicle Control). This interdisciplinary project funded a post-doctoral RA to develop cognitive, multi-modal AI algorithms and decision support systems for complex autonomous systems, in the context of regular road driving and planetary rovers - in collaboration with CI: Prof K. Gurney (Psychology, Sheffield), SciSys Ltd & Industrial Systems Control Ltd.

3. Project CI for Chief Scientist Office (CSO) & industry co-funded (~GBP 0.5million) grant, 2015-18 (led by Dr C. Lees, Lead PI, Edinburgh) on “Prognostic Modeling of multi-level Big Data genetic & environmental factors in Crohn’s and Colitis”. The consortium comprised 12 UK Universities and Hospitals.

4. Lead PI for two UK EPSRC & industry co-funded grants (~GBP 200k, 2009-14: EP/H501584/1, EP/G501750/1).), in partnership with Harvard Medical School (Prof. C. Macrae, Prof. W. Slack), MIT Media Lab (Prof. N. Howard), USA. and the Chinese Academy of Sciences (Prof C-L. Liu). These projects funded the development of machine learning and natural language processing (NLP)-powered tools for patient opinion mining and semantic web applications, along with real-time decision support systems to enhance cardiovascular care and pre-operative risk assessment. The EPSRC projects led to the pioneering development of sentic computing technologies and applications and founding of a successful spinout company (SenticNet: This is providing commercialised sentic technology based innovative services and solutions to global industry.

5. Lead PI for EPSRC funded Research Network (2003-4: GR/S63779/01): Novel Computation - Towards multiple-model based learning & optimized control paradigms for complex systems. This Network project funded a postdoctoral RA, and involved multiple academic and industrial partners. It was awarded an (Overall) Assessment of “Tending to Outstanding” by the EPSRC post-Grant Review Panel.


Prof. Hussain regularly publishes in prestigious top-ranked journals - examples include the IEEE Transactions on Neural Networks and Learning Systems; IEEE Transactions on Cybernetics; Information Fusion; Neural Networks and others.

(Up-to-date publications list is available on Google Scholar:

1. Hameed, H., Usman, M., Tahir, A. Hussain A. et al. Pushing the limits of remote RF sensing by reading lips under the face mask. Nature Communications 13, 5168 (2022). This demonstrates how the radical integration of wireless RF sensing with machine learning technologies can overcome key limitations of current camera based lip reading approaches for multimodal hearing aid technologies, e.g. occlusion, ambient lighting and privacy concerns. In addition, the pioneering technology is shown to deliver revolutionary lip reading capabilities in the presence of face masks.

2. Y. Zhou, K. Huang, C. Cheng, X. Wang, A. Hussain and X. Liu, "FastAdaBelief: Improving Convergence Rate for Belief-Based Adaptive Optimizers by Exploiting Strong Convexity," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2022.3143554

3. D. Comminiello, A. Nezamdoust, S. Scardapane, M. Scarpiniti, A. Hussain and A. Uncini, "A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, doi: 10.1109/TSMC.2022.3202656.

4. L. A. Passos, J. P Papa, J D Ser, A Hussain, A Adeel, Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement,
Information Fusion, Volume 90, 2023, Pages 1-11,

5. Hussain A, Sheikh A, Opportunities for artificial intelligence-enabled social media analysis of public attitudes towards Covid-19 vaccines, NEJM Catalyst Innovations in Care Delivery,

6. Gogate M, Dashtipour K, Hussain A: CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement, Information Fusion, 63: 273-285 (2020)

7. Adeel A, Gogate M, Hussain A: Contextual Deep Learning-based Audio-Visual Switching For Speech Enhancement in Real-World Environments, (Elsevier) Information Fusion, Vol.59:163-170 (2020)

8. Al-Ghadir A.I, Azmi A.M, Hussain A, A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments, (Elsevier) Information Fusion, Volume 67, Pages 29-40. (2021)

9. Ieracitano C, Mammone N, Hussain A, Morabito F.C, A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia, (Elsevier) Neural Networks, Vol.123:176-190 (2020)

10. Xiong F, Sun B, Yang X, Qiao H, Huang K, Hussain A, Liu Z, "Guided Policy Search for Sequential Multitask Learning," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1): 216-226 (2019)

11. Alsarhan A.A, Kilani Y, Al-Dubai A.Y, Zomaya A.Y., Hussain A: Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs. IEEE Transactions on Vehicular Technology 69(2): 1568-1581 (2020).

12. Mahmud, M; Kaiser, M. S; Hussain, A: Vassanelli, S, Applications of Deep Learning and Reinforcement Learning to Biological Data, IEEE Transactions in Neural Networks and Learning Systems, 29(6):2063-2079 (2018)

13. Yang X, Huang K, Zhang R, Hussain A: Learning Latent Features with Infinite Non-negative Binary Matrix Tri-factorization, IEEE Transactions on Emerging Topics in Computational Intelligence, 2(5): 450-463, (2018)

14. Hussain A, Cambria C: Semi-Supervised Learning for Big Social Data Analysis, (Elsevier) Neurocomputing, 275: 1662-1673 (2018)

15. Malik Z.K., Hussain A, Wu J: Multi-Layered Echo State Machine: A novel Architecture and Algorithm, IEEE Transactions on Cybernetics, 47(4):946 - 959 (2017)


414 results

A robust deep learning approach for tomato plant leaf disease localization and classification

Journal Article
Nawaz, M., Nazir, T., Javed, A., Masood, M., Rashid, J., Kim, J., & Hussain, A. (2022)
A robust deep learning approach for tomato plant leaf disease localization and classification. Scientific Reports, 12(1), Article 18568.
Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Altho...

A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks

Journal Article
Yan, S., Zhang, Y., Gao, F., Sun, J., Hussain, A., & Zhou, H. (2022)
A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 9566-9583.
Semisupervised learning in synthetic aperture radars (SARs) is one of the research hotspots in the field of radar image automatic target recognition. It can efficiently deal w...

Towards real-time privacy-preserving audio-visual speech enhancement

Presentation / Conference
Gogate, M., Dashtipour, K., & Hussain, A. (2022, September)
Towards real-time privacy-preserving audio-visual speech enhancement. Paper presented at 2nd Symposium on Security and Privacy in Speech Communication, Incheon, Korea
Human auditory cortex in everyday noisy situations is known to exploit aural and visual cues that are contextually combined by the brain’s multi-level integration strategies t...

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

Journal Article
Comminiello, D., Nezamdoust, A., Scardapane, S., Scarpiniti, M., Hussain, A., & Uncini, A. (in press)
A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling. IEEE Transactions on Systems Man and Cybernetics: Systems,
Nonlinear models are known to provide excellent performance in real-world applications that often operate in nonideal conditions. However, such applications often require onli...

DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm

Journal Article
Aboud, A., Rokbani, N., Fdhila, R., Qahtani, A. M., Almutiry, O., Dhahri, H., …Alimi, A. M. (2022)
DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm. Applied Soft Computing, 129, Article 109622.
Particle Swarm Optimization (PSO) system based on the distributed architecture over multiple sub-swarms is very efficient for static multi-objective optimization but has not b...

A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion

Journal Article
Gao, F., Xu, J., Lang, R., Wang, J., Hussain, A., & Zhou, H. (2022)
A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion. Remote Sensing, 14(18), Article 4583.
Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usuall...

Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement

Journal Article
Passos, L. A., Papa, J. P., Del Ser, J., Hussain, A., & Adeel, A. (2023)
Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement. Information Fusion, 90, 1-11.
This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canoni...

Pushing the limits of remote RF sensing by reading lips under the face mask

Journal Article
Hameed, H., Usman, M., Tahir, A., Hussain, A., Abbas, H., Cui, T. J., …Abbasi, Q. H. (2022)
Pushing the limits of remote RF sensing by reading lips under the face mask. Nature Communications, 13(1), Article 5168.
The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies...

A Mixed Approach for Aggressive Political Discourse Analysis on Twitter

Journal Article
Torregrosa, J., D’Antonio-Maceiras, S., Villar-Rodríguez, G., Hussain, A., Cambria, E., & Camacho, D. (in press)
A Mixed Approach for Aggressive Political Discourse Analysis on Twitter. Cognitive Computation,
Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countri...

Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges

Journal Article
Anwar, U., Arslan, T., Hussain, A., & Lomax, P. (2022)
Next Generation Cognition-Aware Hearing Aid Devices With Microwave Sensors: Opportunities and Challenges. IEEE Access, 10, 82214-82235.
The strong association between hearing loss and cognitive decline has developed into a major health challenge that calls for early detection, diagnosis and prevention. Hearing...

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