I am a mathematician working on data science related topics such as anomaly detection, meta-learning and streaming data. I conduct research on theoretical and real-world problems. For example, visualizing anomalies in high dimensions is a very tricky business. How can we reduce dimensions in a way that anomalies are highlighted or brought to the forefront? Another problem that interests me is extracting events in streaming data. This has many applications in sensor networks. I come to statistical learning from a pure mathematics background. My PhD was in mean curvature flow, which lies in the intersection of partial differential equations and differential geometry. I like to bring my geometric intuition to solve inter-disciplinary real world problems.