I am working on advancing the mathematical foundations of Machine Learning to apply to modern challenges of data analysis. I am particularly interested in analyzing what kind of benefits and information we can extract from unlabelled labelled data (eg in semi-supervised or active learning) or data from that came from diverse sources (eg in transfer learning). I am also interested in developing new Machine Learning frameworks for problem settings in algorithmic game theory and computational economics.