I work in the fields of machine learning and computational biomedicine. My research focuses on new methods for learning and reasoning over rich interconnected data and on the translation of these methods into solutions for key problems in sciences and medicine.
This approach not only opens up new avenues for understanding nature, analyzing health, and developing new medicines to help people but can impact the way predictive modeling is performed today at the fundamental level. I use my methods to:
- answer burning scientific questions, such as how Darwinian evolution changes molecular networks, and how data-driven algorithms can accelerate discovery. For that, I leverage networks at the scale of billions of interactions among millions of entities and develop new methods blending learning with statistics and networks.
- solve high-impact problems, such as what drugs and their combinations are safe for patients, what molecules will treat what diseases, and how newborns are transferred between hospitals and how these transfers influence outcomes. These solutions serve as a first step to bridging the divide between science data and patient data.