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Probabilistic Learning Group Group

Our research applies in a broad range of fields, from medicine and psychiatry to social and communication systems. Recently, we also began putting a special focus on consequential decision making in several domains, including hiring processes, pre-trial bail, or loan approval.

The Research Group on Probabilistic Learning focuses on improving the flexibility, robustness and ethics of machine learning methods for real-world applications. Flexible means they are capable of modeling complex real-world data, which are often heterogeneous in nature and present temporal dependencies. Secondly, we aim to improve their robustness to outliers, missing data and mixed statistical data types. Finally, we work on aligning algorithms with the ethics of society by making them fairer and interpretable – if algorithms are part of important decision-making processes, the outcomes should be fair and explainable.

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