Probabilistic machine learning methods for uncertainty quantification and decision-making

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• Javier Gonzalez
• Tuesday 30 November 2021, 13:00-14:00
• SS03.

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My research focuses on the development of probabilistic machine learning methods for uncertainty quantification and data-efficient sequential decision making. I work on the challenges arising when uncertainty of different types (the loss of precision induced by numerical calculations, data errors, model miss-calibration, etc.) need to be be propagated, controlled and reduced in complex pipelines. I am also interested on how causal inference can be used to leverage decision making methods and to improve the understanding of complex systems and processes. As fields of application of my research I am interested in computational biology, health and environmental sciences.

This talk is part of the ML@CL Seminar Series series.

This talk is included in these lists:

• Hanchen DaDaDash
• ML@CL Seminar Series
• SS03

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