Mikhail Glazunov

Mikhail Glazunov is a Ph.D. candidate in the Cybersecurity group at the Delft University of Technology, supervised by Apostolis Zarras. Prior to that, Mikhail did the first year of his Ph.D. at the Maastricht University in the Cybersecurity SecLab of the Department of Data Science and Knowledge Engineering (DKE). Before starting his Ph.D. Mikhail obtained two Master's degrees: one in the applied, experimental and mathematical linguistics from the Saint Petersburg State University and another one in data science for decision making from the Maastricht University. Besides Mikhail possesses several years of experience working in the industry as a software engineer and a tech lead.

His research interests are focused on the robustness of deep neural networks and unsupervised anomaly detection. It includes a broad range of topics that primarily relate to probabilistic modeling and deep learning such as:

• Adversarial Attacks
• Generative Modelling
• Epistemic Uncertainty
• Explainability in AI
• Unsupervised Learning
• Bayesian Deep Learning
• Natural Language Processing