My research interests revolve around developing machine learning algorithms that enable scientific discovery from heterogeneous data. Machine learning methods are usually constrained by the quality of feature representations and similarity measures that describe objects. My main focus has been to learn good feature representations and similarity measures by developing theoretically well-founded algorithms that uncover underlying mechanisms of complex systems under consideration using my statistics and optimization background. I have focused on applications that lie at the intersection of theoretical and applied research, in particular those arising in computational biology.

In the future, machine learning will become even more widespread to analyze high-dimensional data coming from complex systems such as cancer. In this vein of research, my current research agenda integrates novel machine learning solutions into cancer biology. However, it is not yet very clear how to benefit from the results of these studies for practice changes at the bedside. There are three major challenges in cancer biology: (i) being able to analyze the rapidly increasing amount of high-throughput data with these computational methods, possibly incorporating prior knowledge as well, (ii) being able to develop computational methods that support personalized cancer therapies based on biological characterization of each patient, and (iii) being able to explore the molecular mechanisms of cancer to identify and validate novel biomarkers.