DEI Talks | ”AutoML and Meta-learning for Neural Network Robustness Verification” by Prof. Jan N. van Rijn, Leiden Institute of Advanced Computer Science

Jan N. van Rijn holds a tenured position as assistant professor at Leiden University (, where he works in the computer science department (LIACS) and Automated Design of Algorithms cluster (ADA).

His research interests include artificial intelligence, automated machine learning (AutoML) and meta-learning.

He obtained his PhD in Computer Science in 2016 at Leiden Institute of Advanced Computer Science (LIACS), Leiden University (the Netherlands).

During his PhD, he developed, an open science platform for machine learning, enabling sharing of machine learning results.

He made several funded research visits to the University of Waikato (New Zealand) and the University of Porto (Portugal).

After obtaining his PhD, he worked as a postdoctoral researcher in the Machine Learning lab at the University of Freiburg (Germany), headed by Prof. Dr. Frank Hutter, after which he moved to work as a postdoctoral researcher at Columbia University in the City of New York (USA). His research aim is to democratize access to machine learning and artificial intelligence across societal institutions, by developing knowledge and tools that support domain experts.

He is one of the authors of the book `Metalearning: Applications to Automated Machine Learning and Data Mining’ (published by Springer).”

AutoML and Meta-learning for Neural Network Robustness Verification” will be presented january the 25th, at 14:45, room B006 – free entrance, all are welcome.

Abstract: Artificial intelligence is being increasingly integrated in modern society, with applications ranging from self-driving cars to medicine development. However, artificial intelligence models (in particular neural networks) have been notoriously known for being susceptible for various forms of attacks, including adversarial attacks. In a bid to make these models more trustworthy, the field of neural network robustness verification aims to determine to which degree a given network is susceptible to such an attack. This is a very time consuming task, that can greatly benefit from the various advances that the Automated Machine Learning and meta-learning community have made.

In this talk, it will be explained the basis of automated machine learning and meta-learning, and the speaker will talk about their research on applying this to robustness verification. He will also explain how the community can further engage in this endevour towards trustworthy artificial intelligence.

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