Understanding the unexpected: exposing information hidden in noise.

机器学习是一类依赖于现有数据来训练模型，然后预测感兴趣的数量的方法。一些机器学习方法也可以估计这些预测中的不确定性。我的研究项目是开拓性的利用这种不确定性作为第二个机器学习模型的输入，以预测不同的最终物理属性。这种通用技术应该在广泛的物理和生物系统中得到应用。

Machine learning is a class of methods that rely on existing data to train a model and then predict a quantity of interest. Some machine learning methods can also estimate the uncertainty in those predictions. My research program is pioneering the exploitation of this uncertainty as an input for the second machine learning model to predict a different and final physical property. This generic technology should find an application across a broad range of physical and biological systems.