Machine learning algorithms for automated decision making under domain shift

Machine Learning (ML) has made significant progress in recent years, powered by the development of new algorithms, availability of data, open-source software, and ever-increasing computational resources.

However, most applications in which machine learning has been successful so far are limited to the cases where data is abundant and cheap to gather, such as images, advertising, website user interactions, and financial data. In contrast, in many real-world scientific applications, data is scarce and expensive to collect and label, which mandates the use of historic data. Unfortunately, such data is often not representative of novel data points on which we want to perform predictions, a problem more formally called domain shift. This is even exacerbated when models trained on historic data are used in conjunction with autonomous decision-making agents.

In this project, you will work towards addressing these limitations of machine learning. The goal is to develop improved algorithms which are able to better extract relevant information from data instead of spurious correlations, are robust to domain shift and generalise better to novel situations, and can therefore be employed within decision making agents. To assess how the methods perform, you can address real-world examples from important chemistry problems, such as drug and materials discovery.