Machine learning and Bayesian inference in high-dimensional time series
Recent years have seen major advances in the application of model-based analysis techniques to multivariate time series. One domain of application is neuroimaging, where data are extremely sparse, high-dimensional, and noisy. Another domain is global web traffic, which has remarkably similar structural properties. The design of predictive models for such datasets poses a number of compelling challenges for statistical inference and machine learning.
I completed my PhD studies at the Department of Computer Science (Machine Learning Group, Professor Joachim M Buhmann) in 2012. At present, I am a Postdoctoral Research Fellow / Quantitative Analyst at Google. In addition, I am affiliated as an Honorary Research Fellow with the Department of Information Technology and Electrical Engineering at the Swiss Federal Institute of Technology / ETH Zurich (Translational Neuromodeling Unit, Professor Klaas Enno Stephan).