Prof. Dr. Isabel Valera  Universität des Saarlandes PhD and MSc (Carlos III), Post-Doctoral Fellowship (Humboldt)

Speaker Bio:

PhD (2014) and MSc degree (2012) from the University Carlos III in Madrid (Spain), and postdoctoral researcher at the MPI for Software Systems (Germany) and at the University of Cambridge (UK).  

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).

Fellow of the European Laboratory for Learning and Intelligent Systems ( ELLIS), and part of the Robust Machine Learning Program and of the Saarbrücken Artificial Intelligence & Machine learning (Sam) Unit.

Former independent group leader at the MPI for Intelligent Systems in Tübingen (Germany), German Humboldt Post-Doctoral Fellowship, and “Minerva fast track” fellowship from the Max Planck Society. Research interests focus 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, improving the robustness of machine learning algorithms to outliers, missing data and mixed statistical data types. Finally, making algorithms fairer and interpretable – if they are part of important decision-making processes, the outcomes should be fair and explainable.