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Mikhail Glazunov is a Ph.D. candidate in the Cybersecurity group at Delft University of Technology, under the supervision of Apostolis Zarras. Prior to his current position, Mikhail was a Ph.D. student at Maastricht University in the Cybersecurity SecLab within the Department of Data Science and Knowledge Engineering. He holds Master's degrees in applied and experimental mathematical linguistics from Saint Petersburg State University and in data science decision making from Maastricht University. Mikhail possesses several years of experience working in the industry as a software engineer and tech lead. His research interests focus on the robustness of deep neural networks and unsupervised anomaly detection, covering a broad range of topics related to probabilistic modeling, deep learning, adversarial attacks, generative modeling, epistemic uncertainty, explainability in AI, unsupervised learning, and Bayesian deep learning, as well as natural language processing.
Requirements apply generally across engineering and science master programs. Specific tracks like Architecture require a digital portfolio.