DESCRIBE YOUR RESEARCH AREA AND FOCUS
- I am part of a research project that aims to provide a benchmarking for the Explicability of Artificial Neural Networks on Graph-Structured datasets. The project also aims to cover benchmarking on hyperparameter optimization of the graph neural networks.
In the realm of artificial intelligence, most research projects target the application of Machine Learning and Deep Learning algorithms in different domains. In other words, many projects in this domain take advantage of those algorithms in different scientific approaches; however, the detailed manner and explicability of handling single sample classification by any ML algorithm are questionable.
Hence, the explainable artificial intelligence (i.e., XAI) plays a critical role here and fills the gap between the results of a learner and interpretable intuitions by a human-kind perception system. Over the last few years, some remarkable research projects have covered XAI and made favorable contributions in generating interpretable details on how an ML algorithm (e.g., a neural network) can come up with results that all influential parties are targetable from a real-life point of view.
Our research project is mainly related to experimental benchmarking in explainable methods on graph-structured datasets that comprise many real-life problems compared to grid data.
Why did you choose to continue your career at Aalborg University?
- Due to the great potential in deep learning fields, great academical atmosphere, collaborative research environment and projects in interesting research fields.
FUN AND OTHER FACTS?
- I like computer and cell-phone video games, pottery and ceramics, wood carving artworks and social activities,
CONTACT INFO
- Mail: ebmo@cs.aau.dk