Anis Yazidi

Anis Yazidi is an Associate Professor at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, where he leads research in the Research Group for Digital Signal Processing and Image Analysis. His academic profile demonstrates significant contributions to artificial intelligence, machine learning, and signal processing with over 50 publications between 2021-2025 in high-impact venues including IEEE Transactions, Frontiers journals, and AAAI proceedings.

Professor Yazidi's research spans multiple interconnected domains with particular emphasis on Tsetlin Machines, deep learning for medical applications, and signal processing theory. His work bridges theoretical foundations with practical implementations, developing novel frameworks like DREAMS for EEG analysis with model card reporting and interpretable methods for ECG classification. His contributions to learning automata theory, particularly in convergence analysis of Tsetlin-based algorithms, represent significant theoretical advances. The research portfolio also extends to cybersecurity applications of AI for IoT protection, agricultural technology for plant disease detection, and renewable energy modeling for wind-speed statistics.

Yazidi's publication record reveals a strong interdisciplinary approach, with collaborations across computer science, neuroscience, medical diagnostics, and engineering disciplines. His recent work shows increasing focus on trustworthy AI systems, ethical considerations in medical applications, and specialized neural architectures tailored for specific data modalities including EEG, ECG, and eye-tracking data. The research demonstrates both theoretical rigor in algorithm development and practical implementation for real-world problems.

Professor Yazidi maintains an active collaboration network with researchers across the Department of Informatics at UiO, particularly with Pedro Lind, Hugo Lewi Hammer, and Paal Engelstad, while also engaging in international collaborations. His work contributes significantly to both the theoretical foundations of learning systems and their practical implementation in diverse application domains from healthcare to renewable energy.