Vasilis Gkolemis

Background

About Vasilis Gkolemis

I am a PhD candidate in Explainable AI (XAI) with uncertainty quantification under the supervision of Prof. Christos Diou, Prof. Eirini Ntoutsi and Prof. Theodore Dalamagas. My PhD research focuses on designing explainability techniques for probabilistic ML models, like Bayesian Neural Networks. I believe that, as stated here, uncertainty and explainability are the two fundamental components for Trustworthy ML. My first degree was in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2017. In the dissertation, I focused on Stereo Vision using Deep Neural Networks. Afterwards, I finished my M.Sc. in Data Science (with Distinction) at the University of Edinburgh, where I worked in probabilistic inference with Dr Michael Gutmann. I am broadly interested in ML for solving a wide range of problems. I will keep updating my website with new content related to my research. Main research interests include (a) Explainable AI,(b) Probabilistic Inference, and (c) Deep Learning. I actively contribute to some open-source projects, like ELFI, where I have implemented the ROMC method