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Abstract Advancing scientific discoveries can play a fundamental role in tackling some of 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 engineering and the maturity of machine learning methods offer an excellent opportunity to assist scientific progress. In this seminar, I would like to offer an overview of our recent work on generative modelling and active learning. In particular, the focus will be on the potential of generative flow networks (GFlowNets) as a generative model for scientific discoveries. I will offer an introduction to GFlowNets and explain 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.
Biography Alex Hernandez-Garcia is an assistant professor at the Université de Montréal, a core academic member at Mila, IVADO professor and member of the Institut Courtois. His machine learning research is motivated by scientific applications to tackle the climate crisis and other societal challenges. In particular, a current focus of his work is active and generative machine learning to facilitate scientific discoveries, such as new materials and antibiotics. He also advocates for a critical examination of the impacts of artificial intelligence, is a strong proponent of open science and is active in initiatives about making science more inclusive, equitable, open, reproducible, transparent and environmentally conscious.
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Status	scheduled (16 Dec 2025 13:00)