Isabel Valera is a full Professor on Machine Learning at the Department of Computer Science of Saarland University in Saarbrücken (Germany), and Adjunct Faculty at MPI for Software Systems in Saarbrücken (Germany).
She is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), where she is part of the Robust Machine Learning Program and of the Saarbrücken Artificial Intelligence & Machine learning (Sam) Unit.
Prior to this, she was an independent group leader at the MPI for Intelligent Systems in Tübingen (Germany) until the end of the year. She has held a German Humboldt Post-Doctoral Fellowship, and a “Minerva fast track” fellowship from the Max Planck Society. She obtained her PhD in 2014 and MSc degree in 2012 from the University Carlos III in Madrid (Spain), and worked as postdoctoral researcher at the MPI for Software Systems (Germany) and at the University of Cambridge (UK).

Research interests

Valera's research focuses on developing machine learning methods that are flexible, robust, interpretable and fair. Flexible means they are capable of modeling complex real-world data, which are often heterogeneous in nature and present temporal dependencies. Secondly, she aims to improve the robustness of machine learning algorithms to outliers, missing data and mixed statistical data types. Finally, she works on making algorithms fairer and interpretable – if they are part of important decision-making processes, the outcomes should be fair and explainable.
Her research can be applied in a broad range of fields, from medicine and psychiatry to social and communication systems. Recently, she also began putting a special focus on consequential decision making in several domains, including hiring processes, pre-trial bail, or loan approval.