Teaching philosophy
My teaching connects conceptual foundations with experimentation and real research practice. I aim to help students understand not only how an AI method works, but also how to question it, evaluate it and extend it.
Areas taught
- Artificial Intelligence and Machine Learning
- Deep and Generative Learning
- Evolutionary Computation and Optimization
- Cloud Application Development and Computing
- Advanced Master's-level research topics
Research supervision
Current supervision includes research on generative AI, co-adaptive learning, federated and privacy-preserving systems, large language models, climate intelligence, sustainable cities and efficient learning.
Prospective students
I welcome motivated Master's and doctoral researchers interested in developing ambitious, reproducible work at the intersection of evolutionary computation and modern machine learning.