To use machine learning methods for novel biomedical discoveries we need methods that go beyond capabilities of conventional machine learning methods. Our research focuses on developing machine learning methods for real-world settings that drive new frontier of biomedical research. We gain new insights from challenging problems in biomedicine and solve them by developing widely applicable machine learning approaches that introduce new settings and innovative solutions in machine learning research. We design machine learning methods that have the ability to generalize to novel scenarios that have not been seen during model training and apply them to drive new discoveries from complex, heterogeneous and high-dimensional biomedical datasets. Our approach to research is highly interdisciplinary and our research bridges traditional disciplinary boundaries through collaborations with labs from other disciplines.
We are a team led by Prof. Maria Brbic at the Swiss Federal Institute of Technology, Lausanne (EPFL). We are part of the School of Computer and Communication Sciences, School of Life Sciences, Center for Intelligent Systems (CIS), EPFL ELLIS Unit and Institute of Bioengineering.