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 (in Scotland, UK) in 2018 as Professor and founding 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.

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

He is a member of the member of the UK Computing Research Committee (UKCRC) - the 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 been invited Advisor/Consultant for various international Governments and organisations, including at: Kuwait Institute for Scientific Research (KISR), Kuwait Government; and the National Centre of Big Data & Cloud Computing (NCBC), Higher Education Commission, Pakistan Government. He acts as a Consultant for various global companies and is co-founder/Advisor for a number of successful spin-out/start-up companies, including SenticNet, Smart Big Data Solutions Ltd. and AiGenics.


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 the smart and secure healthcare and industrial systems of tomorrow.

He has (co)authored three international patents and nearly 500 publications, including around 200 international journal papers, 20 Books/monographs and 100+ Book chapters with (current Google h-index of 57, i10-index of 195, and 43+ Research Gate score).

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 50 PhD students and postdoctoral researchers to-date. His high-profile PhD graduates include (amongst numerous others): Prof Erik Cambria (NTU, Singapore - the world’s most highly-cited AI researcher in sentiment analysis); Dr Soujanya Poria (2018 Presidential Young Investigator Award Winner, Singapore) and Prof Muaz Niazi (CIIT, Pakistan, & Chief-Editor: Springer journal of Complex Adaptive Systems Modeling)


• He is founding Editor-in-Chief of two leading international journals: Cognitive Computation (Springer Nature: SCI Impact Factor (IF): 4.29), and BMC Big Data Analytics (BioMed Central), and of 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 (IF: 12.2), IEEE Transactions on Systems, Man, and Cybernetics: Systems (IF: 7.35), Information Fusion (Elsevier, IF: 13.7), AI Review (Springer, IF: 9.3), IEEE Computational Intelligence Magazine (IF: 9), and the IEEE Transactions on Emerging Topics in Computational Intelligence (IF: 5).

• During 2017-20, he was 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 sentiment analysis (since 2000). His works on biologically-inspired natural language processing (for sentiment & opinion mining) are ISI highly-cited papers.

• 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. 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 team won second

• 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.

• Amongst other distinguished, international conference chairing roles (see examples below), he is General Chair for IEEE WCCI 2020 (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 a member of the UK Computing Research Committee (UKCRC) - the 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 Vice-Chair of the Emergent Technologies Technical Committee of the IEEE Computational Intelligence Society (CIS), IEEE Chapter Chair of the UK and Ireland IEEE Industry Applications Society, and (founding) Vice-Chair for the IEEE CIS Task Force on Intelligence Systems for e-Health.


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 (

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 2021 in Orlando, Florida 4-7 Dec 2021

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’2020 in Hefei , China, 18-20 Dec 2020

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 new (~GBP 4 Million) UK Government's Engineering and Physical Sciences Research Council (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 in the country, under the EPSRC Transformative Healthcare Technologies 2050 Call. The cross-disciplinary programme includes senior co-Investigators (CIs) from: Edinburgh, Heriot-Watt, Glasgow and Manchester; and a strong User Group comprising ten industrial and clinical collaborators, and end-user engagement organisations (such as Deaf Scotland, Action on Hearing Loss, global hearing-aid manufacturers: Phonak & Nokia Bell Labs, providing ~GBP1 million match-funding). COG-MHEAR aims to be the world’s first multi-modal hearing-aid demonstrator (including real-time software and hardware prototypes), by radically exploiting and integrating the transformative potential of privacy-assuring and explainable AI, 5G, Internet of Things (IoT) and cybersecurity, coupled with flexible (skin-based) electronics. This will enable commercialization of future personalized, multi-modal hearing aids, with reduced end-user cognitive load/listening effort, seamlessly operating in the smart social and work spaces of 2050 and beyond. The adventurous research outputs will also be exploited to develop multimodally-enhanced, speech-enabled communication aids to support e.g. autistic people with speech disorders. For job opportunities, please see the COG-MHEAR website:

2. Lead CI/co-PI for new (~GBP 0.5 Million) EPSRC grant (2021-24) on “Natural Language Generation (NLG) for Low-resource Domains” (EP/T024917/1). The proposal was ranked first (out of 19), under the EPSRC Responsible NLP for Intelligent Interfaces Call. The project funds two postdoctoral RAs (one under my principal supervision), to develop personalized, privacy-assuring and empathetic AI (NLG) approaches that account for the emotional state of the end-user. These will enable emotion-sensitive personal assistants to enhance mental health and well-being.

3. Lead PI for new Scottish Government's Chief Scientist Office (CSO) funded research project (~GBP 0.14 Million), joint with the Usher Institute, University of Edinburgh, as part of the CSO Rapid Research in Covid -19 Programme Call (2020). The project is aimed at developing a novel Artificial Intelligence (AI) powered dashboard for COVID-19 related public sentiment and opinion mining in social media platforms. (

4. 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 (2020-24). 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), in partnership with Harvard Medical School (Prof. C. Macrae, Prof. W. Slack) and MIT Media Lab (Prof. N. Howard), USA. These projects funded development of novel AI predictive models and real-time decision support systems to enhance cardiovascular care and pre-operative risk assessment; and integrating machine learning and natural language processing (NLP) for patient opinion mining and semantic web applications

5. Lead PI for EPSRC funded Research Network: 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. 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,

2. Ren J, Yan Y, Zhao H, Ma P, Zabalza J, Hussain Z, Luo S, Dai Q, Zhao S, Sheikh A, Hussain A, A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19. IEEE Journal of Biomedical Health Informatics (2020) Sep 30; PP. doi: 10.1109/JBHI.2020.3027987 (JIF: 5.2)

3. Gogate M, Dashtipour K, Hussain A: CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement, Information Fusion, 63: 273-285 (2020) (JIF): 13.7)

4. 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) (JIF: 13.7)

5. 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) (JIF: 13.7)

6. 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) (JIF:7.2)

7. 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) (JIF: 9.3)

8. 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) (JIF: 5.3)

9. Scardapane S, Van Vaerenbergh S, Hussain A. and Uncini A, "Complex-Valued Neural Networks With Nonparametric Activation Functions," in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 2, pp. 140-150, April 2020, doi: 10.1109/TETCI.2018.2872600. (JIF: 5)

10. Zhang L, Liu Z, Zhang S, Yang X, Qiao H, Huang K, Hussain A: Cross-modality interactive attention network for multispectral pedestrian detection, (Elsevier) Information Fusion, 50: 20-29 (2019) (JIF: 13.7)

11. Arafat S, Aljohani N, Abbasi R, Hussain A, Lytras M, Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study, (Elsevier) Computers in Human Behavior, 92: 478-486 (2019) (JIF: 5.9)

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) (JIF: 12.2)

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) (JIF: 5)

14. Hussain A, Cambria C: Semi-Supervised Learning for Big Social Data Analysis, (Elsevier) Neurocomputing, 275: 1662-1673 (2018) (JIF: 4.4)

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) (JIF: 11.1)


330 results

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images

Journal Article
Masood, M., Nazir, T., Nawaz, M., Mehmood, A., Rashid, J., Kwon, H., …Hussain, A. (2021)
A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images. Diagnostics, 11(5),
A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tum...

Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Journal Article
Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., …Sheikh, A. (in press)
Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study. Journal of Medical Internet Research, 23(4),
Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccin...

COVID-19 UK Social Media Dataset for Public Health Research

Plant, R., & Hussain, A. (2021)
COVID-19 UK Social Media Dataset for Public Health Research. [Dataset].
We present a benchmark database of public social media postings from the United Kingdom related to the Covid-19 pandemic for academic research purposes, along with some initia...

A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling

Journal Article
Farouq, M. W., Boulila, W., Hussain, Z., Rashid, A., Shah, M., Hussain, S., …Hussain, A. (2021)
A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling. Sensors, 21(6),
Machine learning (ML)-based algorithms are playing an important role in cancer diagnosis and are increasingly being used to aid clinical decision-making. However, these common...

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Journal Article
Ahmed, R., Gogate, M., Tahir, A., Dashtipour, K., Al-tamimi, B., Hawalah, A., …Hussain, A. (2021)
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts. Entropy, 23(3),
Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several field...

A novel explainable machine learning approach for EEG-based brain-computer interface systems

Journal Article
Ieracitano, C., Mammone, N., Hussain, A., & Morabito, F. C. (in press)
A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Computing and Applications,
Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor corte...

Discriminative Dictionary Design for Action Classification in Still Images and Videos

Journal Article
Roy, A., Banerjee, B., Hussain, A., & Poria, S. (in press)
Discriminative Dictionary Design for Action Classification in Still Images and Videos. Cognitive Computation,
In this paper, we address the problem of action recognition from still images and videos. Traditional local features such as SIFT and STIP invariably pose two potential proble...

A Multipath Fusion Strategy Based Single Shot Detector

Journal Article
Qu, S., Huang, K., Hussain, A., & Goulermas, Y. (2021)
A Multipath Fusion Strategy Based Single Shot Detector. Sensors, 21(4),
Object detection has wide applications in intelligent systems and sensor applications. Compared with two stage detectors, recent one stage counterparts are capable of running ...

Information fusion for affective computing and sentiment analysis

Journal Article
Hussain, A., Cambria, E., Poria, S., Hawalah, A., & Herrera, F. (2021)
Information fusion for affective computing and sentiment analysis. Information Fusion, 71, 97-98.
Abstract not available.

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

Journal Article
Rahal, N., Tounsi, M., Hussain, A., & Alimi, A. M. (2021)
Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition. IEEE Access, 9, 18569-18584.
One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddress...

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