In this talk I will discuss some of the latest progress we have made in the space of certifying AI systems, ranging from certification of deep neural networks to entire deep learning pipelines. In the process I will also discuss new neural architectures that are more amenable to certification as well as mathematical impossibility and complexity results that help guide new kinds of certified training methods.

Martin Vechev is an Associate Professor at the Department of Computer Science, ETH Zurich. His work spans the intersection of machine learning and symbolic methods with applications to topics such as safety of artificial intelligence, quantum programming and security. He has co-founded 3 start-ups in the space of AI and security, the latest of which LatticeFlow aims to build and deploy trustworthy AI models.

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