+* 09:00-10:00: Keynote by [Prof. Alex Hernandez-Garcia](https://alexhernandezgarcia.github.io/) - **Title:** *"Generative and active machine learning for scientific discoveries"*. **Abstract**: *"Science plays a fundamental role in tackling the most pressing challenges for humanity, such as the climate crisis, the threat of pandemics and antibiotic resistance. Meanwhile, the increasing capacity to generate large amounts of data, the progress in computer and software engineering and the maturity of machine learning methods offer an excellent opportunity to assist scientific progress. In this talk, I would like to offer an overview of how generative modelling and active learning can be used to assist scientific discovery research. In particular, the focus will be on the potential of GFlowNets as a flexible generative model for science. I will offer a gentle introduction to GFlowNets and present how we have adapted this method to incorporate domain knowledge from crystallography, physics and chemistry in the form of hard constraints, to efficiently discover new materials with desirable properties. I will also present our recent algorithm for multi-fidelity active learning with GFlowNets, designed to efficiently explore combinatorially large, high-dimensional and mixed spaces (discrete and continuous), inspired by challenges in materials and drug discovery."*
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