The Conference for Machine Learning Innovation

David Glavas

David Glavas
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David Glavas

Technical University of Munich (TUM)

David studies computer science at the Technical University of Munich (TUM) where he does research at both the IT Security and the Computer Vision chair. His main research focus is adversarial machine learning, that is, the study of security vulnerabilities in machine learning systems.

Previously, he worked at Fraunhofer AISEC’s cognitive security department where he focused on securing machine learning models at test time (Adversarial Examples). After having repeatedly experienced the gap between algorithms in academia and industry, David has developed a “code first” mentality and is well versed in various ML libraries such as TensorFlow and PyTorch.
Currently, David works part-time at Celonis where he develops process mining algorithms.

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