Machine learning and pattern recognition in high-dimensional datasets
The last decade has witnessed a phenomenal increase in the application of multivariate and model-based analysis techniques to the examination of high-dimensional datasets. One particularly challenging example can be found in neuroimaging datasets. Because these datasets are extremely sparse, high-dimensional, and noisy, their analysis poses a number of compelling challenges for machine learning and pattern recognition.
I am a PhD student at ETH Zurich affiliated with both the Department of Information Technology and Electrical Engineering (Translational Neuromodelling Unit, Professor Klaas Enno Stephan) and the Department of Computer Science (Machine Learning Group, Professor Joachim M Buhmann). My research interests include the development of novel multivariate analysis techniques and their application to high-dimensional datasets, such as those obtained by functional magnetic resonance imaging (fMRI). These techniques may one day play a critical role in dissecting spectrum disorders into physiologically well-defined subtypes.