Adil Khan

Adil Khan is a Professor of Machine Learning and Artificial Intelligence at the University of Hull, affiliated with the School of Computer Science within the Faculty of Science and Engineering. His research focuses on advancing machine learning techniques, including deep learning, domain adaptation, adversarial robustness, and fairness in AI, with applications in remote sensing, medical image analysis, and natural language processing. He has led significant projects funded by organizations like Huawei and the Analytical Center of Artificial Intelligence of Innopolis University, addressing challenges such as improving model generalization and mitigating catastrophic forgetting in neural networks. Recent work includes developing novel attention mechanisms and Mamba models for hyperspectral image classification, as well as exploring cross-modal retrieval using large language models (LLMs).

His theoretical contributions span foundational questions in ML, such as training with limited data, adversarial robustness, and ethical AI. Notable projects include the £10 million 'Fair, Robust and Life-long Machine Learning' initiative, advancing representation learning for fair and robust models. Additionally, he serves as an external examiner for the London School of Innovation's MSc in Applied AI and Machine Learning. His publications emphasize innovative solutions in computer vision, domain adaptation, and healthcare applications, with a focus on practical industrial implementations and rigorous evaluation methodologies.

Key grants include the £500,000 Wolfson Foundation grant for advanced imaging equipment and the Innovate UK-funded 'AKT TRUST-LLM' project on trustworthy LLMs. His research bridges theoretical advancements with real-world applications, reflecting a commitment to both academic rigor and societal impact.