My current research  focuses on civic-centered and community-minded artificial intelligence (AI) that aims to reduce inequality and promote equal opportunity in society. This interest arose out of the concern that AI is increasing inequality in society. If science  does not intervene, there is a great risk that the gap between the poor and the rich and the general inequality will increase. The predominant question I am an my research group - Socially-Intelligent Artificial Systems (SIAS) group - trie to answer: How can we use AI, and in particular learning systems, to advance society? And how can we do that in such a way that people from all corners of society benefit from it?

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SIAS is unique in the sense that it operates at the crossroad of two big transformations in society: diversification and digitization. Society is demographically changing rapidly and is becoming increasingly technologically complex. SIAS is one of the few research groups working on this intersection. The group merges the social and the technical aspect in a way that benefits the social. In order to develop socially intelligent AI, SIAS does research at the whole spectrum of theoretical, fundamental (e.g. what is fairness?) and practical research (how are communities impacted?). SIAS covers all these aspects. PhD students and post-docs come from different parts of the world and have a great motivation to use their AI knowledge for social good. The group operates out of the box and into future.

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My previous research focussed on the intersection between human vision and computer vision: cognitive vision. Cognitive vision strives to understand how we make sense of our visual environment by combining low-level cues (depth, color, texture, ...) with high-level prior knowledge (expectations, preconceptions, ...). My research objective was to advance our understanding of the human machine by relating computational, behavioral and brain responses to natural pictures and movies. In particular how subtle but computable patterns or biases in our natural environment underlie a quick and dirty first impression of our visual world.