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Prof Hamid Bouchachia


Professor of Data Science and Intelligent Systems

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Hamid Bouchachia is Professor at Bournemouth University, Faculty of Science and Technology, Department of Computing and Informatics, UK. His major research interests include Machine Learning and big data, with a particular focus on online learning, semi-supervised learning, active learning, learning with constraints, scalable learning, prediction systems, and uncertainty modelling with applications in pattern recognition, ubiquitous health monitoring, smart environments and technologies, assistive technologies and crisis management. 

He has been coordinating leading and participating in big European and national projects, which allowed him to secure fund and supervise 14 PhD students to completion and 5 postdoc researchers. Such projects cross the disciplinary borders, calling for interdisciplinary research. He has published more than 140 papers in top journals and conferences. Hamid is a member of the Evolving Intelligent Systems Technical Committee of the Systems, Man and Cybernetics Society of IEEE, member of the “IEEE Task-Force for Adaptive and Evolving Fuzzy Systems" and Senior Member of the IEEE Society. He was the founder and general chair of the International Conference on Adaptive and Intelligent Systems (ICAIS). He serves as program committee member for many conferences and project assessor for many national and international funding organisations.

Landmark publications:

Mohamad, S., Alamri, H. & Bouchachia, A. Scaling up stochastic gradient descent for non-convex optimisation. Mach Learn (2022).

Jamil, W.,  Bouchachia. H.: Iterative ridge regression using the aggregating algorithm. Pattern Recognit. Lett. 158: 34-41 (2022)

Pedrosa, J. et al.: LNDb challenge on automatic lung cancer patient management. Medical Image Anal. 70: 102027 (2021)

Galdran, A., Bouchachia, H.: Residual Networks for Pulmonary Nodule Segmentation and Texture Characterization. ICIAR (2) 2020: 396-405

Jamil, W., Bouchachia, H.: Competitive Normalized Least-Squares Regression. IEEE Trans. Neural Networks Learn. Syst. 32(7): 3262-3267 (2021) 

Arifoglu, D., Nait-Charif, H., Bouchachia H.: Detecting indicators of cognitive impairment via Graph Convolutional Networks. Eng. Appl. Artif. Intell. 89: 103401 (2020)

Mohamad, S., Sayed Mouchaweh, M., Bouchachia, H.: Online active learning for human activity recognition from sensory data streams. Neurocomputing 390: 341-358 (2020)

Mohamad, S., Bouchachia, H.: Deep online hierarchical dynamic unsupervised learning for pattern mining from utility usage data. Neurocomputing 390: 359-373 (2020)

Pohl, D., Bouchachia, H., Hellwagner, H.: Active Online Learning for Social Media Analysis to Support Crisis Management. IEEE Trans. Knowl. Data Eng. 32(8): 1445-1458 (2020)

Arifoglu, D., Bouchachia, H.: Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks. Artif. Intell. Medicine 94: 88-95 (2019)

Amiribesheli,  M., Bouchachia, H.: A tailored smart home for dementia care. J. Ambient Intell. Humaniz. Comput. 9(6): 1755-1782 (2018)

Mohamad, S., Sayed Mouchaweh, M.,  Bouchachia, H.: Active learning for classifying data streams with unknown number of classes. Neural Networks 98: 1-15 (2018)

Mohamad, S., Bouchachia, H., Sayed Mouchaweh. M.: A Bi-Criteria Active Learning Algorithm for Dynamic Data Streams. IEEE Trans. Neural Networks Learn. Syst. 29(1): 74-86 (2018)

Major grants:

ExtremeXP: EXPerimentation driven and user eXPerience oriented analytics for eXtremely Precise outcomes and decisions (Horizon Europe, 2022)

PROTEUS: Scalable online machine learning for predictive analytics and real-time interactive visualization (H2020, 2016).

High Frequency Appliance Disaggregation Analysis (Energy Technologies Institute, 2017)

Strategic Knowledge Enhancement/International Fellowship (KKS, Sweden, 2021)

Impact example:

Development of online machine learning library called SOLMA.

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