- Invited to join the Pi School and the Collaborative Innovation Network (CIN) of the Phi Lab of the European Space Agency as a Visiting Professor (2025-2026)
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- Tutorial "Rethinking Interpretability and Adaptivity of Deep Learning", presented at the 24st International Conference on Machine Learning Applications (ICMLA–2025), Boca Raton, FL, USA, 3-5 December 2025
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- co-Chair IEEE International Workshop on Pervasive and Resource-constrained Artificial Intelligence, PerConAI within the IEEE International Conference on Pervasive Computing and Communications, PerCom 2026, Pisa, Italy, 16-20 March 2026
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​​Recently published papers:
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A. Aghasanli, P. Angelov, D. Kangin, J. Kerns, R. F. Shepherd, Transfer learning from inorganic materials to ivory detection, Nature Scientific Reports, 15 (1): 15536, April 2025. - for the first time has been demonstrated that Transfer Learning can be applied successfully across data from inorganic and organic matter; the method presented in the paper is also highly accurate (over 98%) without the need for destructive DNA tests in identifying origin of tusks - legal historic mammoths or endangered in terms of poaching elephants (African or Indian)
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A. Aghasanli, P. Angelov, Recursive SNE: Fast Prototype-Based t-SNE for Large-Scale and Online Data, Transactions on Machine Learning Research (TMLR), 2025 - the fastest reported t-SNE method (5 or more times faster than the faster state-of-the-art method (Barnes-Hut) t-SNE. Python code is published in GitHub and Matlab code - in the Mathworks' repository
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A. Aghasanli, P. Angelov, Y. Li, Prototype-based Continuous Learning with label-free replay buffer and cluster preservation loss, CVPR-2025, Nashville, TN, USA, June 2025
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A.L. Pellicer, A. Mariucci, P. Angelov, M. Bukhari, J.G. Kerns, ProtoMedX: Towards Explainable Multi-Modal Prototype Learning for Bone Health Classification, ICCV-2025, Honolulu, Hawaii, USA, Oct. 2025
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- Angelov's latest papers to be officially recognised as highly cited (top 1% according to Web of Science) include:
- A. Lopez Pellcier, Y. , Li, P. Angelov, PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection, CVPR-W 2024, pp.3809-3817, FWCI 22.62
- P. Angelov et al., Explainable artificial intelligence: an analytical review, WIREs Data Mining and Knowledge Discovery (IF 10.38), DOI:10.1002/widm.1424, 2021, FWCI 30.72​​​
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​​​​​- keynote talk at the 2024 IEEE World Congress on Computational Intelligence (WCCI-2024), International Joint Conference on Neural Networks, IJCNN-2024, Yokohama, Japan, 1-5 July 2024 press release
​​​​- Program co-Chair (Cybernetics Track), 2024 IEEE Systems, Man and Cybernetics Conference (IEEE-SMC 2024), 7-10 October 2024, Borneo, Malaysia
​​- co-organiser 2nd Workshop and Challenge on Deep Fake Analysis and Detection within the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-2024, Seattle, USA, 14-21 June 2024
- interview for a documentary on AI opportunities and risks by the Bulgarian National TV, BNT, available also on YouTube - click here
​​- provided written evidence to the House of Lords Communications and Digital Select Committee inquiry on Large Language Models​