My research area is Artificial Intelligence with a focus on large-scale constraint-based reasoning, optimization, and machine learning. Recently, I have become deeply immersed in the establishment of the new field of Computational Sustainability and in AI for Science.

Computational Sustainability is a new interdisciplinary research field, with the overarching goal of studying and providing solutions to computational problems for balancing environmental, economic, and societal needs for a sustainable future. Such problems are unique in scale, impact, complexity, and richness, often involving combinatorial decisions, in highly dynamic and uncertain environments, offering challenges but also opportunities for the advancement of the state-of-the-art of computer and information science. Work in Computational Sustainability integrates in a unique way various areas within computer science and applied mathematics, such as constraint reasoning, optimization, machine learning, and dynamical systems. Concrete examples of computational sustainability challenges range from planning and optimization for wildlife preservation and biodiversity conservation, to poverty mapping, to combining (deep) data-intensive learning with inference, reasoning, and optimization to accelerate the discovery of new renewable materials such as solar fuels.