My work falls primarily under the umbrella of Artificial Intelligence. I design and analyse learning algorithms with a focus on Algorithmic Game-Theoretic Learning — an interdisciplinary field that synthesises algorithmic and game-theoretic perspectives to develop learning algorithms for adversarial and stochastic environments.

I am particularly interested in algorithms that adapt and learn sequentially. My research examines the dynamic interplay between multiple decision-makers in strategic interactions, developing solutions that exhibit provably desirable predictive, space, and time complexities while maintaining strategic robustness.

This interdisciplinary approach enables the creation of adaptive systems capable of principled decision-making. It also contributes to the fundamental understanding of how intelligent systems can operate effectively in environments with multiple competing agents.

PROTEUS (Completed)

A European research project designed to address fundamental scientific challenges related to the scalability and responsiveness of analytics capabilities. It focused on developing advanced data analytics solutions for complex, large-scale systems, and contributed to the advancement of scalable machine learning algorithms for real-time data processing.
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HFDA (Completed)

A High Frequency Disaggregation Analysis project that analysed real-world data from the Energy Technologies Institute's energy management system across five homes to gather detailed energy data from water, gas, and electricity use. This research focused on energy consumption pattern analysis and predictive modelling for smart home applications.
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ExtremeXP (Active)

Aims to provide accurate, precise, fit-for-purpose, and trustworthy data-driven insights by evaluating different complex analytics variants, while considering end-users' preferences and feedback in an automated way. It includes binary classification research for malicious network traffic detection using online learning algorithms, demonstrating the practical application of algorithmic game-theoretic learning in cybersecurity contexts.
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• Jamil, W. and Bouchachia, A. (2022). Iterative ridge regression using the aggregating algorithm. Pattern Recognition Letters 158, 34-41.
• Jamil, W. and Bouchachia, A. (2020). Competitive normalised least-squares regression. IEEE Transactions on Neural Networks and Learning Systems 32 (7), 3262-3267.
• Jamil, W. and Bouchachia, A. (2020). Competitive regularised regression. Neurocomputing 390, 374-383.
• Jamil, W. and Bouchachia, A. (2020). Online Bayesian shrinkage regression. Neural Computing and Applications 32 (23), 17759-17767.
• Jamil, W. and Bouchachia, A. (2019). Online Bayesian shrinkage regression. Proceedings of the 27th European Symposium on Artificial Neural Networks.
• Jamil, W. and Bouchachia, A. (2018). Model selection in online learning for time series forecasting. Proceedings of the 18th Annual UK Workshop on Computational Intelligence, 83-95.
• Jamil, W., Duong, N.C., Wang, W., Mansouri, C., Mohamad, S. and Bouchachia, A. (2018). Scalable online learning for flink: SOLMA library. Proceedings of the 12th European Conference on Software Architecture.
• Jamil, W., Kalnishkan, Y. and Bouchachia, A. (2016). Aggregation algorithm vs. average for time series prediction. Proceedings of the ECML PKDD 2016 Workshop on Large-scale Learning from Data Streams in Evolving Environments.