Michał Woźniak

Michał Woźniak is a Professor at the Department of Systems and Computer Networks, Wrocław University of Science and Technology. He serves as Head of the Department and leads the Machine Learning Research Team. His research spans machine learning, pattern recognition, data stream mining, and imbalanced data classification. He actively supervises MSc theses and leads multiple research projects including those on continual learning, classifier ensembles, and fake news detection.

Research Interests:

• Machine learning, particularly inductive and continual learning
• Pattern recognition and classifier ensembles
• Data stream mining under concept drift
• Imbalanced data classification
• Fake news and disinformation detection
• Cybersecurity and medical decision support

His recent publications (2024–2025) focus on continual learning under concept drift, deep learning for image fusion and forgery detection, ensemble methods for imbalanced data, and AI applications in network optimization. These works reflect strong trends in adaptive machine learning, robust classification, and real-world AI deployment.

Scientific Awards:

• BEST PAPER AWARD FOR CLVISION CVPR WORKSHOP 2024

He supervises numerous students and collaborates extensively on interdisciplinary projects. He has been involved in projects such as LM LDS (2021–2024), MOO (2020–2025), IDStream (2018–2022), and others. He is also a project manager and active in academic service, including membership in the Committee on Informatics of the Polish Academy of Sciences. His research group maintains a strong presence in AI and machine learning applications.

Laboratory and Teams: He leads the Machine Learning Team and is involved in multiple research groups including the Advanced Data Analysis Methods Team and Metaheuristics Team. His lab focuses on developing robust, adaptive AI models for real-world challenges.