School of Engineering

Explainable AI

"The future belongs to the AI we can understand, trust, and work alongside — AI that makes sense of complex, multimodal data, explains its decisions, and engages seamlessly with the humans who use it. That is how we unlock its true potential to transform industries and benefit humanity."

Dr. Jasmina Bogojeska

Expertise and Services

Expertise

  • Machine Learning
  • Deep Learning
  • Explainable/trustworthy AI
  • Multimodal AI
  • Healthcare

The research focus of the XAI group is on machine learning and deep learning methodology, particularly explainable, trustworthy and multimodal AI, to address complex decision-making and knowledge-discovery tasks in different domains. Our ultimate goal is to enable the successful application of these methods in products in practice functioning hand-in-hand with domain experts and users. More specifically, we are addressing the challenging research problem of developing AI-powered systems able to properly utilise multimodal data, provide human intelligible information about their outputs and engage seamlessly with users. Reinforcement learning and causal inference are two additional areas of interest very relevant for sequential decision making and knowledge discovery with human in the loop. Finally, while we are up for taking on challenging problems in various domains, we are particularly interested in advancing, improving, and digitising the healthcare domain. We envision this by building explainable AI-powered products for disease diagnosis and treatment, as well as developing transparent AI-powered approaches to advance the understanding of health and disease, all done collaboratively with users (experts and patients) in the loop, safely and responsibly.

Services

Additional Information

We are eager to build collaborations with practice partners, hospitals, and research institutions to translate cutting-edge AI research into meaningful real-world impact through collaborative research and innovation. Get in touch to explore opportunities to work together. 

We also welcome motivated students who share our vision and are interested in pursuing Master’s studies under our supervision. All open positions in the group, including PhD opportunities, are advertised on our institutional website as they arise.

Team

Projects

Publications