Machine learning is at the core of AI innovations in many domains, such as health, engineering, business, safety and security. It enables computers to realise problem-solving capabilities that frequently match or even surpass humans.
In the DMB group, we develop machine learning for Computer Vision, Biometrics, Data Engineering and other fields. Computer Vision aims at automated interpretation, analysis, and understanding of visual information from images and videos. Biometrics enables automated recognition of persons using traits like face, fingerprint, iris, and vein patterns. Data Engineering is about obtaining high quality data for development and use of AI through automated collection, integration, transformation, and cleaning of data.
Regulations and real-world innovations pose strong requirements on the technology in terms of transparency, explainability, robustness, trustworthiness, fairness, data quality, resilience to attacks, privacy, sustainability, and efficiency. Our research specifically focuses on these important aspects for developing responsible AI.
Examples are explainable AI-support for medical diagnosis and treatment decisions, face recognition resilient to face morphing attacks, understanding of soil ecology through network data science, counter energy-hungry training of LLMs and deep learning with sparse models, process mining for healthcare, understanding social interactions in first-person videos, identifying creators of artwork through biometric traces like fingerprints, etc.
